A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

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A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data Firas Lethaus, Martin R.K. Baumann, Frank Köster, Karsten Lemmer

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S0925-2312(13)00570-5 http://dx.doi.org/10.1016/j.neucom.2013.04.035 NEUCOM13419

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Received date: 30 August 2012 Revised date: 27 March 2013 Accepted date: 5 April 2013 Cite this article as: Firas Lethaus, Martin R.K. Baumann, Frank Köster, Karsten Lemmer, A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data, Neurocomputing, http://dx.doi.org/ 10.1016/j.neucom.2013.04.035 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

1 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Firas Lethaus*, Martin R.K. Baumann, Frank Köster, Karsten Lemmer

German Aerospace Center (DLR), Institute of Transportation Systems, Lilienthalplatz 7, 38108 Braunschweig, Germany

*Corresponding author: Firas Lethaus, German Aerospace Center (DLR), Institute of Transportation Systems, Lilienthalplatz 7, 38108 Braunschweig, Germany Tel.: +49 531 295-3409 Fax: +49 531 295-3402 Email: [email protected]

2 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Abstract

Gaze behaviour is known to indicate information gathering. It is therefore suggested that it could be used to derive information about the driver’s next planned objective in order to identify intended manoeuvres without relying solely on car data. Ultimately this would be practically realised by an Advanced Driver Assistance System (ADAS) using gaze data to correctly infer the intentions of the driver from what is implied by the incoming gaze data available to it. Neural Networks’ ability to approximate arbitrary functions from observed data therefore makes them a candidate for modelling driver intent. Previous work has shown that significantly distinct gaze patterns precede each of the driving manoeuvres analysed indicating that eye movement data might be used as input to ADAS supplementing sensors, such as CAN-Bus (Controller Area Network), laser, radar or LIDAR (Light Detection and Ranging) in order to recognise intended driving manoeuvres. In this study, drivers’ gaze behaviour was measured prior to and during the execution of different driving manoeuvres performed in a dynamic driving simulator. Artificial Neural Networks (ANN), Bayesian Networks (BN), and Naive Bayes Classifiers (NBC) were then trained using gaze data to act as classifiers that predict the occurrence of certain driving manoeuvres. This has previously been successfully demonstrated with real traffic data [1]. Issues considered here included the amount of data that is used for predictive purposes prior to the manoeuvre, the accuracy of the predictive models at different times prior to the manoeuvre taking place and the relative difficulty of predicting a lane change left manoeuvre against predicting a lane change right manoeuvre.

Keywords: Artificial neural networks, Bayesian networks, Naive Bayes classifiers, Driver intent, Prediction, Driving manoeuvres, Eye tracking, Pattern recognition, Machine Learning

3 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

1 Introduction Human error is known to be the main factor in more than 90 percent of traffic accidents [2]. The automotive industry is actively seeking to tackle this problem via Advanced Driver Assistance Systems (ADAS). An ADAS should be able to alter its response so that it remains appropriate to the driving situation thus maintaining a reliable output. The use of manoeuvre recognition can help to avoid mismatches between the driver’s intention and the system’s reaction. A pertinent example is the situation where a driver intends to change lanes in order to overtake the lead car, but an incipient collision warning is emitted potentially confusing and irritating the driver, and constituting a false alarm. The extent to which the driver perceives this confusion or action intrusion is strongly linked to the way the false warning is presented. Haptic warnings in the form of a short duration brake have been demonstrated to be more effective than acoustic warnings [3,4]. However, false haptic warnings are also capable of distracting the driver. Such false alarms can lead to additional driving errors, thus, negating the benefit of such systems. Therefore, a collision warning system is required to be biased towards avoiding false alarms. The collision warning example also highlights the importance of identifying driving manoeuvres in advance. This temporal advantage allows the driver to be kept in the loop while using assistance systems by ensuring that the driver is informed at a point such that they have enough time to select an appropriate response. With reference to the collision warning example, the warning may be given earlier, if it is recognised by the system that the driver does not plan to overtake the lead car and that a critical situation is developing. The recognition of driving manoeuvres can be based on various data sources, such as CAN-Bus (Controller Area Network), where the change in the vehicle’s motion in response to the driver’s input is used. As a result, a manoeuvre can be detected once the driver has started carrying out the manoeuvre. Alternatively, if manoeuvre recognition is based on the driver’s gaze behaviour, manoeuvre prediction is then based on data which corresponds to the cognitive phase of information gathering [5]. Thus, the gaze data refers to the driver’s intent, which is formed before the manoeuvre takes place, rather than to his actual execution of the manoeuvre [6] resulting in a temporal benefit for gaze-based recognition. It was therefore proposed that observed driver gaze data be used to form a model of driver intent. An appropriate approach is to train an Artificial Neural Network (ANN), a Bayesian Network (BN), and a Naive Bayes Classifier (NBC) using supervised learning where the training data consists of observations of driver gaze behaviour. Accurate identification of a manoeuvre enables assistance appropriate to the driving situation to be given. In the example described above, the assistance system interprets the car’s fast approach, i.e. its decreasing time-to-collision (TTC), towards the car it intends to overtake as being a critical situation, i.e. a high probability of a collision occurring. The assistance system fails to assist the driver appropriately as the ’collision warning’ function has been designed for the manoeuvre ’car following’. As the system lacks the capacity to recognise manoeuvres besides ’car following’, it cannot classify

4 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

the driver’s actual intention as one of performing a lane change so that the collision warning alarm can safely remain off. The approach of using gaze data as a source for predicting driving manoeuvres could also be applied in the context of a Lane Departure Warning System (LDW). A LDW warns the driver every time the vehicle starts to leave the lane, which becomes an annoyance when lane changes are intentional. Gaze data would also be appropriate in implementing a Lane Change Assist System (LCA) which monitors the adjacent lanes, that is, the area around and behind the vehicle, in order to avoid lane change collisions that may occur by giving an early warning if the driver is unaware of an approaching vehicle in the adjacent lane. A CAN-Bus-data-based and vehicle-sensor-based system identifies an intended lane change as soon as the indicator is being set and a change occurs in the steering wheel angle. As a result, a warning is given to the driver while being in the beginning phase of executing a lane change manoeuvre. However, during the cognitive phase of action execution, the resistance of humans to change their intended behaviour is higher than during the information gathering phase [7-9]. Therefore, the use of gaze data could lead to earlier warnings that arrive at a time where the driver is more flexible in terms of selecting a response. The experimental work presented here is intended to act as a foundation. It considers whether a satisfactory model of driver intent can be produced using a simple ANN trained using vanilla Backpropagation, a simple BN, and a simple NBC. This means that as the task of inferring driver intent is increased in complexity later, it can build upon the work presented here.

2 Recognition of Driving Manoeuvres and Driver Intent

A number of studies have been carried out in real traffic as well as in driving simulators focussing on recognising and identifying driving manoeuvres by incorporating driver data. McCall et al. [10] noted that Driver Intent Inference (DII) is distinct from Trajectory Forecasting (TF) approaches. A DII approach infers if / when a driver is intentionally about to execute a lane change whereas a TF approach predicts whether the vehicle trajectory is likely to cross the lane boundary in the near future (irrespective of driver awareness level). It was further stated in [10] that most other approaches perform TF using the results as a proxy for DII. This can be summarised by stating that the absence of data derived from measurement of the driver means that nothing can be inferred about the driver's intent. Hidden Markov Models (HMMs) were applied to vehicle data from real traffic in order to recognise and identify driving manoeuvres using a batch algorithm [11]. Here, entire instances of a manoeuvre were detected as opposed to a constant stream of real-time data, and contextual information, such as gaze behaviour, lane, and surrounding traffic was used for their models. Gaze was fed into the model as a discretely valued input with six possible values (front road, rear view

5 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

mirror, right mirror, left mirror, right, and left). Drivers’ gaze behaviour was identified to be a relevant feature for driving manoeuvre prediction and recognition, predominantly in connection with lane changes overtaking, and executing turns. A combination of vehicle and gaze data delivered the best results. Further results showed that the discrimination of manoeuvres, such as overtaking and lane change left, is relatively poor, if only based on vehicle data, and that recognising turns and lane changes requires contextual information. On average, driving manoeuvres were recognised one second prior to a significant change. A study focussed on continuously recognising lane change manoeuvres in real-time using a moving-base driving simulator was also undertaken [12]. Steering behaviour models were produced that were based on HMMs aiming at recognising and characterising emergency and normal lane changes as well as lane keeping manoeuvres. Information on the surrounding situation was ignored. However, exploiting contextual information is indispensable for making solid and robust detections available. Sparse Bayesian Learning was used to predict lane change manoeuvres in real traffic when using car data alone and when incorporating driver state information in the form of head position, i.e., head movement data [10]. It was found that the inclusion of this driver state information resulted in the predictions of lane change manoeuvre at 3.0 seconds before manoeuvre that were as accurate as the predictions made at 2.5 seconds before manoeuvre using car data only. Real world data is typically imbalanced requiring the development of systems designed to detect relatively rare but important cases [13] and traffic data is no exception. However, the authors do not provide information detailing the number of training and testing examples used or the ratio of lane keeping to lane change examples within the dataset. Without consideration of the degree of imbalance that may have existed within the dataset used it is not possible to fully assess the impact of the addition of head position data. If measures had been taken to ameliorate any imbalance, then the degree of improvement observed may have been of a reduced magnitude when the head position data was added to the input vector. This could have been achieved by, for example, undersampling the majority class or oversampling the minority class or the application of cost sensitive measures during the learning process [14,15]. Using a real-time system in a fixed-base driving simulator, the detection of lane change manoeuvres was studied [16,17] by implementing a cognitive driver model in the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture [18]. The driver model was validated by comparing its behaviour with that of drivers. A combination of car and gaze data was used to infer information about drivers’ intentions. The detection of intended lane changes in real-time achieved an accuracy of 85 percent and detection rates of 80 percent within half a second and 90 percent within 1 second after initial behaviour leading into the execution of manoeuvres. It can be concluded from these results that the detection rate can be improved by adding gaze behaviour to recognition models for driving manoeuvres. It has been demonstrated that significantly distinct gaze patterns precede each of the driving manoeuvres analysed [19,20] indicating that eye movement data may be used as input to ADAS

6 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

supplementing sensors, such as CAN-Bus, laser, radar or LIDAR (Light Detection and Ranging) in order to recognise intended driving manoeuvres. The data were gathered in a field study using a vehicle equipped with appropriate sensors, which logged data from the driver, the vehicle, and the environment. Drivers were asked to drive approximately 110 kilometres (approx. 68 miles) on a threelane and a two-lane motorway as well as on one-lane rural roads. By performing Markov analyses (zero- and first-order) it was found that the tendency to use the left wing mirror more often than the rear view mirror for manoeuvres to the left is the reverse for manoeuvres to the right, also observed by [21]. Overall, it was concluded that the number of mirror inspections increases with the number of lanes of a road, which concurs with conclusions drawn in other studies [22-24]. Taking the outcomes of the study described above into account a model based on gaze data from real traffic was created in order to predict specific driving manoeuvres [1]. A Feedforward Neural Network (FFNN) was trained using the Backpropagation algorithm [25] to be able to predict lane changes. The study using the real-world data demonstrated that it was possible to discriminate lane change left and lane change right from lane keeping prior to the manoeuvre actually taking place by building models using gaze data. However, due to the fact that the data was gathered in a real world environment the opportunities to perform these manoeuvres during the trial were relatively few and could not be guaranteed to be the same for every driver resulting in models based on a small pool of data. It was decided to carry out trials in a simulated environment where the level of traffic and opportunities to change lanes could be controlled providing the opportunity to gather a larger volume of data upon which to base predictive models of lane change.

3 Simulator Study

The data were gathered in a dynamic driving simulator in order to provide repeatability of scenarios, that is, each driver was exposed to same driving conditions, as well as to be able to provide many safe opportunities to change lane. This could also be achieved by controlling the volume of traffic, which is known to be a problematic factor in field studies.

3.1 Driving Task

The study included a total of 10 participants (5 female, 5 male) aged 23 to 36 years (M=29.8, SD=4.6). All had normal vision, had held their driving licence for at least 5 years and drove more than 10,000 kilometres p.a. (~6250 miles p.a.). Informed consent was obtained from each driver who participated prior to testing. The driving task took place in simulated traffic and comprised a drive on a three-lane and two-lane motorway each having a length of 70 kilometres (~43.5 miles). Drivers were instructed to drive on the right-most lane throughout the experiment and to only use the centre lane (for three-

7 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

lane motorway) or the leftmost lane (two-lane motorway) for the purpose of overtaking lead cars. Each drive took approximately 40 minutes and started with overtaking a group of lead cars, which was repeated ten times, was followed by 10 kilometres (~6.2 miles) of car following, and ended with overtaking a single lead car, also repeated ten times. Prior to the beginning of the experiment, drivers were given oral and written instructions, followed by a gaze calibration procedure of the eye tracking system.

3.2 Equipment

The driving task was performed in a dynamic driving simulator, whose motion is provided by a hexapod system, which allows motion with six degrees of freedom (see Figure 1). The cabin hangs below the upper couplings. This allows a larger range of motion in a smaller space than would be possible with simulators whose couplings connect to the bottom of the cabin. A complete real vehicle is mounted in the simulator’s cabin. The vehicle is surrounded by the projection system of the dynamic driving simulator which provides a wide visual field covering the front and the sides of the vehicle (270° horizontal × 40° vertical) and a high resolution presentation (approx. 9200 × 1280 pixels). The rear view mirror and TFT-displays in the side mirrors allow the driver to keep an eye on the rear traffic. A large plasma screen in the back of the vehicle displays the scenery to the rear. Communication between the cockpit and the simulation system is realised via CAN-Bus, which makes it possible to transmit all driver actions and to control the instruments inside the cockpit. The system also allows all inputs and actions made by the driver to be recorded and analysed.

Fig. 1. Dynamic driving simulator at the German Aerospace Center. Eye movements were recorded using a head-mounted eye tracking system, SMI iView XTM HED (=head-mounted eye tracking device), and five non-overlapping viewing zones, i.e. areas of interest, were defined inside the vehicle (windscreen, left window/wing mirror, rear view mirror, speedometer, right-hand side) in order to analyse the driver’s gaze behaviour.

3.3 Data Processing

The gaze data was processed such that the ability to form a predictive model could be investigated in terms of:

– the amount of data available prior to the manoeuvre taking place and – the amount of time before manoeuvre occurred at which the prediction was made by the model.

8 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

A 10 second window of gaze data preceding the beginning of lane change left and right manoeuvres was extracted (see Figure 2) and took the moment the vehicle began to leave its lane to perform a lane change as a reference to mark the beginning of a lane change manoeuvre. In almost the same manner, lane keeping manoeuvres, such as road and car following, were extracted by subdividing sections of these manoeuvres into 10 second windows of gaze data. Either lane keeping manoeuvre was defined by the vehicle’s TTC to the vehicle ahead. Car following manoeuvres had a TTC of less than 3 seconds, whereas road following manoeuvres’ TTC was defined as being more than 3 seconds.

Fig. 2. Time windows of 10 seconds of gaze data selected prior to the beginning of lane change left and right manoeuvres.

Previous work [19,20] showed that a 10 second window of data preceding the manoeuvre was rich enough in information that distinct gaze patterns could be recognised. For further modelling, two groups of data samples were selected, representing 5 second and 10 second windows of data preceding manoeuvres of interest (see Figure 3). In order to establish whether there is redundancy within this 10 seconds of data, 5 second samples were also used here so that the predictive accuracy of models built using 10 seconds of data could be compared with models built using 5 seconds of data. Figure 3 shows the distribution of gaze behaviour across 5 viewing zones (1=windscreen, 2=left window/wing mirror, 3=rear view mirror, 4=speedometer, 5=right window/wing mirror) from a 10 second window of data prior to a lane change manoeuvre to the left.

In this study, each window of data was truncated by 0, 0.5, 1.0, 1.5 up to 9.5, 10 seconds prior to the beginning of a manoeuvre. For instance, the 5 second window was either used as a whole sample (a) or reduced by half a second (b) (=4.5sec data sample), by 1 second (c) (=4sec), by 1.5 seconds (d) (=3.5sec), by 2 seconds (e) (=3sec), until reaching a distance of 4.5 seconds for a data sample of 0.5 seconds (see Figure 3). Equally, 10 second windows were partitioned into data samples of 0.5 to 10 seconds having a distance to the beginning of a lane change manoeuvre of 9.5 to 0 seconds. Figure 3 shows the data sampling for 5 and 10 second windows.

9 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

The data were encoded into a format suitable for use in supervised learning, i.e. a vector of input and target data was produced using real number in the range [0.0, 1.0] [26]. According to [26, p. 11], the choice of encoding the representation of input and output attributes of a learning problem is crucial with regard to the quality of results. The input section of the vector described the proportion of time spent looking in the 5 viewing zones during the selected period of time prior to execution of the manoeuvre. The input vector that resulted from data sample “(a)” of the 5 second window in Figure 3 looked as follows:

0.69,0.31,0,0,0

.

The target section of the vector indicated the ’class’ represented by the input vector and used a binary encoding, i.e., if the data represented lane keeping the target output was 0 and if it represented one of the manoeuvres of interest then the target was given as 1. The input vector that resulted for input and target data from data sample “(a)” of the 5 second window in Figure 3 showing a lane change looked as follows:

0.69,0.31,0,0,0,1

.

Assuming that looking into the windscreen (viewing zone 1) can be considered a “default”, viewing zone 1 was ignored in an alternative encoding. By this means, the relative proportion of the remaining 4 viewing zones changed tremendously and hence the proportional weight. Thus, following input vector resulted for input and target data from data sample “(a)” of the 5 second window in Figure 3 showing a lane change:

1,0,0,0,1

.

That way, the relative proportion of viewing zone 2 (left window/wing mirror) was increased from 0.3 in the 5-viewing-zone-encoding to 1 in the 4-viewing-zone-encoding, presuming an advantageous impact on the learning algorithm’s performance. The result was three groups of 5-viewing-zoneencoding and three groups of 4-viewing-zone-encoding each consisting of 284 (input, target) vectors of instances of lane change left, lane change right and lane keeping applied to 5 and 10 second windows of gaze data.

10 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Fig. 3. Gaze data used from a section of typical gaze patterns across 5 viewing zones prior to a lane change left manoeuvre.

4 Selected Simple Supervised Learning Algorithms

4.1 Artificial Neural Networks (ANN)

Feedforward neural networks with five input nodes, two hidden nodes, and a single output node were trained using the Backpropagation algorithm [25] to be binary classifiers ideally outputting 1 when an instance of the manoeuvre of interest was detected in the input data and 0 when the inputs indicated that the car was keeping to the lane. The actual output of the ANNs was a real number in the range [0.0, 1.0], which can be taken a measure of probability that the manoeuvre of interest has been detected. A threshold T , which was given values in the range [0.0, 1.0] in increments of 0.1, was then used in order to decide which of the two classes (lane change / lane keeping, or lane change left / right) the neural network output represented, i.e. the output of the neural network is P (C|x) where C is the class lane change and x is the input vector, hence the threshold T can be used to decide which class the neural network output represents as follows:

11 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

IF P(C|x) >T lane change ELSE  lane keeping

or

IF P(C|x) >T lane change left ELSE  lane change right

The ANNs were trained with 500 epochs (fixed epoch learning) at a learning rate of 0.1. In order to receive a very simple ANN, 2-fold cross-validation was used as a testing method.

4.2 Bayesian Networks (BN)

In order to obtain a very simple BN, 10-fold cross-validation was used as a testing method. The technique used to encode the data was the same as with the ANNs.

4.3 Naive Bayes Classifiers (NBC)

In order to obtain a very simple NBC, 10-fold cross-validation was used as a testing method. The technique used to encode the data was the same as with the ANNs.

5 Analysis and Results

It is of importance to establish the ability of predictive models to correctly identify instances of the phenomenon of interest while avoiding false alarms. Analysis of the results has been carried out using methods from signal detection theory so that variation in the neural network models’ sensitivity and specificity, as its discriminating threshold is changed, can be measured, and the position of the threshold at which the best balance between the two measures exists can be found. Sensitivity measures the proportion of positive examples which are correctly identified (also known as the true positive rate (TPR) or ’hit’ rate). Specificity measures the proportion of negative examples which are correctly identified (also known as the ’true negative’ rate). The false positive rate (FPR) indicates the number of examples incorrectly identified as positive examples and it can be shown that Specificity = (1-FPR). The threshold, T , was varied from 0.0 to 1.0 in order to create plots of the TPR (sensitivity) and the FPR (1-specificity), known as ROC curves (Receiver Operating Characteristic), which give a graphical representation of the trade-off between false alarms and higher detection rate of the phenomenon of interest with changing T [27-31].

12 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

The sensitivity index d’ (d prime) by [32] is a measure of the difference between the TPR and FPR, with larger values of d’ indicating a better performing model [33]. It is calculated as

d’ = Z(true positive rate) - Z(false positive rate), where function Z(p), p ‫[ א‬0,1], is the inverse of the cumulative Gaussian distribution [34]. The d’ values obtained using 5 seconds of data were found to be larger than when using 10 seconds of data when predicting both lane change right and lane change left (see Tables 1-6). The d’ values were found to be smaller for lane change right than those obtained when predicting lane change left for both the 10 second and 5 second windows of data. This indicates that a better model can be obtained using just 5 seconds of data with the additional data in the 10 second windows constituting noise that the ANN has to learn to ignore. Since FPRs with values of 0 and TPRs with values of 1 cannot be calculated with d’, another measure for sensitivity and nonparametric counterpart of d’, A’ [35,36] was given. A’ highly correlates with d’ and is independent of values TPRs and FPRs can take [37, p. 451]. A’ can take values between 0 and 1, whereas values greater than 0.5 mean that the TPR is bigger than the FPR. When comparing a model’s performance, the ability to discriminate between TPR and FPR is of equal importance as the tendency as to how this is achieved. This tendency is called bias (also response tendency) and is based upon human response behaviour. The analysis was carried out using two reliable measures of bias, C [33] and B"D [38]. Positive values of C and B"D hint towards a conservative tendency to reject uncertain driving manoeuvres, whereas negative values refer to a liberal tendency to accept driving manoeuvres. Different to C, B"D is based on a nonparametric model and can take values between -1 and 1, while 0 means that no bias is indicated. ROC curves are independent of bias. Tables 1-6, Tables 7-12, and Tables 13-18 show the detailed overall performance of predictive ANN, BN, and NBC models, respectively, when discriminating between lane keeping and lane change manoeuvres as well as between lane change left and right manoeuvres at different times before the beginning of a manoeuvre for sets of 5 and 10 second windows of gaze data using a 4- and 5-viewingzone-encoding. The ROC curves of ANNs (Figures 4-6), BNs (Figures 7-9), and NBCs (Figures 1012) show this performance for times ranging from 0 to 2 seconds before the beginning of manoeuvre to facilitate the visual comparison of the results.

13 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 1. Results of experiments using ANNs to discriminate between lane keeping and lane change left manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D , and threshold value T. Lane Change Left 5 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D T

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

5s 0.944 [0.955] 0.063 [0.084] 3.121 [3.069] 0.968 [0.965] -0.029 [-0.155] -0.062 [-0.315] 0.5 [0.5]

4.5s 0.937 [0.934] 0.087 [0.080] 2.887 [2.905] 0.959 [0.960] -0.086 [-0.050] -0.175 [-0.102] 0.4 [0.4]

4s 0.839 [0.878] 0.066 [0.073] 2.493 [2.613] 0.937 [0.946] 0.255 [0.143] 0.458 [0.275] 0.7 [0.6]

3.5s 0.881 [0.899] 0.140 [0.150] 2.261 [2.308] 0.925 [0.928] -0.049 [-0.119] -0.093 [-0.221] 0.3 [0.3]

3s 0.832 [0.832] 0.129 [0.122] 2.092 [2.125] 0.912 [0.915] 0.083 [0.100] 0.151 [0.182] 0.3 [0.4]

2.5s 0.689 [0.731] 0.140 [0.108] 1.573 [1.850] 0.858 [0.887] 0.294 [0.309] 0.470 [0.503] 0.4 [0.3]

2s 0.531 [0.567] 0.129 [0.094] 1.208 [1.481] 0.804 [0.838] 0.525 [0.573] 0.711 [0.760] 0.5 [0.6]

1.5s 0.388 [0.469] 0.094 [0.084] 1.029 [1.300] 0.770 [0.810] 0.799 [0.729] 0.875 [0.850] 0.6 [0.4]

1s 0.287 [0.339] 0.080 [0.070] 0.839 [1.061] 0.735 [0.770] 0.982 [0.945] 0.932 [0.925] 0.6 [0.5]

0.5s 0.059 [0.203] 0.028 [0.063] 0.351 [0.698] 0.640 [0.709] 1.735 [1.181] 0.996 [0.966] 0.8 [0.6]

Table 2. Results of experiments using ANNs to discriminate between lane keeping and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D , and threshold value T. Lane Change Right 5 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D T

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

5s 0.877 [0.894] 0.141 [0.155] 2.235 [2.265] 0.923 [0.925] -0.041 [-0.117] -0.076 [-0.216] 0.3 [0.3]

4.5s 0.771 [0.796] 0.120 [0.127] 1.918 [1.968] 0.896 [0.901] 0.216 [0.157] 0.371 [0.277] 0.4 [0.5]

4s 0.683 [0.690] 0.116 [0.120] 1.670 [1.672] 0.867 [0.868] 0.358 [0.340] 0.558 [0.535] 0.4 [0.4]

3.5s 0.475 [0.500] 0.074 [0.088] 1.385 [1.352] 0.819 [0.818] 0.754 [0.676] 0.865 [0.823] 0.5 [0.5]

3s 0.324 [0.359] 0.063 [0.067] 1.070 [1.138] 0.770 [0.781] 0.991 [0.930] 0.937 [0.922] 0.6 [0.6]

2.5s 0.046 [0.778] 0.004 [0.595] 1.007 [0.525] 0.741 [0.671] 2.191 [-0.503] 0.999 [-0.675] 0.8 [0.4]

2s 0.113 [0.778] 0.060 [0.623] 0.343 [0.451] 0.631 [0.652] 1.384 [-0.540] 0.983 [-0.706] 0.6 [0.4]

1.5s 0.028 [0.813] 0.018 [0.666] 0.197 [0.462] 0.596 [0.655] 2.007 [-0.658] 0.998 [-0.793] 0.7 [0.4]

1s 0.014 [0.461] 0.011 [0.401] 0.110 [0.152] 0.563 [0.557] 2.250 [0.173] 0.999 [0.270] 0.7 [0.5]

0.5s 0.996 [0.940] 0.989 [0.894] 0.389 [0.305] 0.668 [0.620] -2.500 [-1.403] -0.999 [-0.985] 0.2 [0.4]

14 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 3. Results of experiments using ANNs to discriminate between lane change left and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D , and threshold value T. Lane Change Left / Right 5 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D T

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

5s 0.972 [0.975] 0.046 [0.049] 3.595 [3.617] 0.980 [0.980] -0.110 [-0.157] -0.246 [-0.344] 0.6 [0.6]

4.5s 0.965 [0.965] 0.077 [0.063] 3.231 [3.336] 0.970 [0.974] -0.193 [-0.141] -0.394 [-0.299] 0.6 [0.7]

4s 0.954 [0.965] 0.123 [0.113] 2.846 [3.021] 0.954 [0.960] -0.264 [-0.298] -0.491 [-0.553] 0.7 [0.8]

3.5s 0.958 [0.965] 0.229 [0.215] 2.467 [2.599] 0.926 [0.933] -0.491 [-0.509] -0.741 [-0.764] 0.6 [0.6]

3s 0.919 [0.965] 0.324 [0.349] 1.855 [2.198] 0.881 [0.896] -0.470 [-0.710] -0.689 [-0.872] 0.6 [0.5]

2.5s 0.817 [0.880] 0.366 [0.398] 1.245 [1.435] 0.815 [0.837] -0.280 [-0.458] -0.440 [-0.658] 0.6 [0.5]

2s 0.750 [0.820] 0.394 [0.426] 0.942 [1.103] 0.765 [0.791] -0.203 [-0.365] -0.322 [-0.544] 0.6 [0.5]

1.5s 0.870 [0.859] 0.585 [0.553] 0.911 [0.943] 0.753 [0.760] -0.669 [-0.604] -0.807 [-0.765] 0.5 [0.4]

1s 0.916 [0.898] 0.683 [0.658] 0.898 [0.861] 0.746 [0.741] -0.925 [-0.838] -0.917 [-0.888] 0.5 [0.5]

0.5s 0.905 [0.873] 0.792 [0.761] 0.495 [0.433] 0.666 [0.649] -1.062 [-0.924] -0.946 [-0.912] 0.5 [0.5]

15 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 4. Results of experiments using ANNs to discriminate between lane keeping and lane change left manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D , and threshold value T. Lane Change Left 10 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D T Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D T

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

10s 0.937 [0.927] 0.140 [0.136] 2.611 [2.547] 0.944 [0.941] -0.224 [-0.176] -0.415 [-0.331] 0.4 [0.6]

9.5s 0.899 [0.906] 0.136 [0.150] 2.370 [2.349] 0.932 [0.930] -0.088 [-0.139] -0.166 [-0.258] 0.5 [0.6]

9s 0.930 [0.878] 0.227 [0.185] 2.224 [2.058] 0.916 [0.909] -0.364 [-0.133] -0.592 [-0.239] 0.3 [0.5]

8.5s 0.853 [0.895] 0.213 [0.252] 1.845 [1.923] 0.890 [0.894] -0.127 [-0.292] -0.223 [-0.483] 0.5 [0.3]

8s 0.801 [0.871] 0.241 [0.248] 1.546 [1.809] 0.859 [0.885] -0.070 [-0.224] -0.121 [-0.379] 0.5 [0.3]

7.5s 0.769 [0.808] 0.241 [0.255] 1.438 [1.527] 0.845 [0.856] -0.017 [-0.105] -0.029 [-0.180] 0.4 [0.3]

7s 0.703 [0.710] 0.241 [0.231] 1.234 [1.289] 0.816 [0.824] 0.084 [0.091] 0.141 [0.153] 0.4 [0.4]

6.5s 0.654 [0.661] 0.227 [0.234] 1.143 [1.139] 0.801 [0.800] 0.176 [0.155] 0.285 [0.253] 0.4 [0.4]

6s 0.507 [0.584] 0.199 [0.210] 0.861 [1.019] 0.747 [0.778] 0.413 [0.297] 0.592 [0.457] 0.5 [0.5]

5.5s 0.451 [0.511] 0.192 [0.192] 0.746 [0.895] 0.723 [0.754] 0.496 [0.421] 0.672 [0.602] 0.5 [0.5]

-5s

-5.5s

-6s

-6.5s

-7s

-7.5s

-8s

-8.5s

-9s

-9.5s

5s 0.392 [0.448] 0.185 [0.182] 0.620 [0.776] 0.695 [0.729] 0.585 [0.520] 0.744 [0.694] 0.5 [0.5]

4.5s 0.360 [0.395] 0.175 [0.161] 0.577 [0.724] 0.684 [0.718] 0.646 [0.628] 0.786 [0.777] 0.5 [0.5]

4s 0.329 [0.367] 0.164 [0.154] 0.533 [0.680] 0.674 [0.708] 0.710 [0.679] 0.824 [0.809] 0.5 [0.5]

3.5s 0.276 [0.462] 0.147 [0.353] 0.455 [0.280] 0.654 [0.600] 0.822 [0.236] 0.876 [0.362] 0.5 [0.5]

3s 0.241 [0.213] 0.140 [0.094] 0.378 [0.519] 0.634 [0.672] 0.891 [1.054] 0.901 [0.945] 0.5 [0.6]

2.5s 0.196 [0.157] 0.115 [0.080] 0.341 [0.396] 0.625 [0.643] 1.027 [1.203] 0.938 [0.967] 0.5 [0.6]

2s 0.119 [0.143] 0.035 [0.070] 0.631 [0.410] 0.698 [0.647] 1.496 [1.270] 0.990 [0.975] 0.6 [0.7]

1.5s 0.451 [0.147] 0.360 [0.073] 0.234 [0.400] 0.585 [0.644] 0.240 [1.250] 0.367 [0.973] 0.5 [0.7]

1s 0.423 [0.108] 0.388 [0.059] 0.090 [0.324] 0.534 [0.625] 0.239 [1.397] 0.365 [0.984] 0.5 [0.7]

0.5s 0.066 [0.073] 0.014 [0.021] 0.694 [0.583] 0.710 [0.691] 1.850 [1.742] 0.997 [0.996] 0.6 [0.6]

16 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 5. Results of experiments using ANNs to discriminate between lane keeping and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D , and threshold value T. Lane Change Right 10 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D T Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D T

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

10s 0.827 [0.884] 0.190 [0.218] 1.821 [1.972] 0.889 [0.901] -0.033 [-0.208] -0.059 [-0.359] 0.4 [0.3]

9.5s 0.785 [0.799] 0.187 [0.204] 1.680 [1.665] 0.874 [0.873] 0.050 [-0.006] 0.087 [-0.010] 0.4 [0.4]

9s 0.613 [0.683] 0.151 [0.187] 1.316 [1.366] 0.824 [0.834] 0.372 [0.207] 0.559 [0.338] 0.5 [0.5]

8.5s 0.493 [0.542] 0.144 [0.169] 1.043 [1.064] 0.778 [0.784] 0.539 [0.425] 0.718 [0.611] 0.5 [0.5]

8s 0.588 [0.419] 0.366 [0.158] 0.564 [0.796] 0.681 [0.732] 0.059 [0.602] 0.096 [0.760] 0.5 [0.5]

7.5s 0.592 [0.405] 0.447 [0.225] 0.364 [0.513] 0.626 [0.668] -0.049 [0.497] -0.078 [0.669] 0.5 [0.5]

7s 0.616 [0.363] 0.486 [0.218] 0.331 [0.426] 0.616 [0.645] -0.130 [0.564] -0.205 [0.725] 0.5 [0.5]

6.5s 0.637 [0.335] 0.535 [0.218] 0.262 [0.350] 0.594 [0.624] -0.219 [0.602] -0.338 [0.753] 0.5 [0.5]

6s 0.623 [0.324] 0.560 [0.229] 0.163 [0.285] 0.561 [0.604] -0.232 [0.599] -0.355 [0.750] 0.5 [0.5]

5.5s 0.637 [0.320] 0.539 [0.239] 0.253 [0.241] 0.592 [0.589] -0.224 [0.587] -0.344 [0.741] 0.5 [0.5]

-5s

-5.5s

-6s

-6.5s

-7s

-7.5s

-8s

-8.5s

-9s

-9.5s

5s 0.592 [0.310] 0.532 [0.250] 0.151 [0.178] 0.557 [0.568] -0.155 [0.585] -0.243 [0.739] 0.5 [0.5]

4.5s 0.560 [0.479] 0.556 [0.398] 0.107 [0.205] 0.541 [0.575] -0.195 [0.156] -0.303 [0.244] 0.5 [0.5]

4s 0.658 [0.518] 0.585 [0.468] 0.194 [0.123] 0.572 [0.546] -0.310 [0.017] -0.461 [0.028] 0.5 [0.5]

3.5s 0.651 [0.532] 0.592 [0.461] 0.157 [0.177] 0.559 [0.565] -0.310 [0.008] -0.460 [0.014] 0.5 [0.5]

3s 0.655 [0.578] 0.602 [0.521] 0.139 [0.142] 0.553 [0.553] -0.328 [-0.124] -0.483 [-0.195] 0.5 [0.5]

2.5s 0.715 [0.620] 0.630 [0.567] 0.234 [0.136] 0.586 [0.551] -0.450 [-0.236] -0.620 [-0.361] 0.5 [0.5]

2s 0.715 [0.637] 0.620 [0.599] 0.262 [0.101] 0.595 [0.539] -0.436 [-0.300] -0.606 [-0.447] 0.5 [0.5]

1.5s 0.778 [0.754] 0.648 [0.666] 0.386 [0.258] 0.634 [0.594] -0.572 [-0.556] -0.731 [-0.717] 0.5 [0.5]

1s 0.796 [0.799] 0.683 [0.718] 0.350 [0.261] 0.624 [0.597] -0.651 [-0.708] -0.787 [-0.820] 0.5 [0.5]

0.5s 0.975 [0.852] 0.961 [0.799] 0.200 [0.206] 0.594 [0.581] -1.865 [-0.942] -0.997 [-0.916] 0.4 [0.5]

17 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 6. Results of experiments using ANNs to discriminate between lane change left and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D , and threshold value T. Lane Change Left / Right 10 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D T Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D T

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

10s 0.958 [0.937] 0.074 [0.049] 3.172 [3.178] 0.969 [0.970] -0.139 [0.062] -0.288 [0.132] 0.4 [0.6]

9.5s 0.933 [0.916] 0.099 [0.063] 2.788 [2.902] 0.955 [0.960] -0.104 [0.075] -0.208 [0.154] 0.4 [0.7]

9s 0.937 [0.905] 0.162 [0.099] 2.513 [2.599] 0.937 [0.946] -0.27 [-0.010] -0.481 [-0.020] 0.3 [0.8]

8.5s 0.916 [0.905] 0.218 [0.211] 2.153 [2.112] 0.913 [0.911] -0.298 [-0.254] -0.503 [-0.436] 0.5 [0.5]

8s 0.894 [0.905] 0.317 [0.310] 1.726 [1.806] 0.872 [0.879] -0.386 [-0.406] -0.594 [-0.620] 0.5 [0.4]

7.5s 0.810 [0.905] 0.342 [0.423] 1.285 [1.505] 0.822 [0.842] -0.234 [-0.557] -0.376 [-0.748] 0.5 [0.4]

7s 0.789 [0.764] 0.391 [0.345] 1.079 [1.118] 0.789 [0.797] -0.262 [-0.160] -0.410 [-0.261] 0.5 [0.5]

6.5s 0.775 [0.761] 0.405 [0.391] 0.994 [0.985] 0.774 [0.773] -0.256 [-0.215] -0.401 [-0.341] 0.5 [0.5]

6s 0.778 [0.771] 0.482 [0.440] 0.810 [0.893] 0.737 [0.755] -0.360 [-0.295] -0.531 [-0.451] 0.5 [0.5]

5.5s 0.792 [0.866] 0.493 [0.563] 0.832 [0.948] 0.742 [0.760] -0.398 [-0.634] -0.575 [-0.786] 0.5 [0.4]

-5s

-5.5s

-6s

-6.5s

-7s

-7.5s

-8s

-8.5s

-9s

-9.5s

5s 0.806 [0.813] 0.518 [0.539] 0.820 [0.793] 0.739 [0.733] -0.454 [-0.493] -0.634 [-0.671] 0.5 [0.5]

4.5s 0.820 [0.838] 0.553 [0.609] 0.784 [0.709] 0.731 [0.714] -0.524 [-0.631] -0.699 [-0.779] 0.5 [0.5]

4s 0.799 [0.842] 0.599 [0.627] 0.589 [0.677] 0.687 [0.707] -0.544 [-0.662] -0.711 [-0.798] 0.5 [0.5]

3.5s 0.930 [0.870] 0.785 [0.739] 0.682 [0.483] 0.706 [0.662] -1.131 [-0.883] -0.959 [-0.899] 0.4 [0.4]

3s 0.750 [0.880] 0.637 [0.761] 0.323 [0.468] 0.615 [0.659] -0.512 [-0.942] -0.681 [-0.917] 0.5 [0.4]

2.5s 0.947 [0.905] 0.849 [0.859] 0.587 [0.233] 0.688 [0.593] -1.324 [-1.193] -0.980 [-0.966] 0.4 [0.4]

2s 0.782 [0.901] 0.666 [0.813] 0.350 [0.399] 0.624 [0.642] -0.602 [-1.090] -0.753 [-0.951] 0.5 [0.4]

1.5s 0.796 [0.908] 0.690 [0.806] 0.330 [0.466] 0.618 [0.659] -0.661 [-1.097] -0.793 [-0.952] 0.5 [0.4]

1s 0.813 [0.919] 0.725 [0.852] 0.291 [0.352] 0.607 [0.631] -0.744 [-1.222] -0.840 [-0.969] 0.5 [0.4]

0.5s 0.940 [0.870] 0.849 [0.820] 0.525 [0.208] 0.675 [0.582] -1.293 [-1.021] -0.977 [-0.936] 0.4 [0.5]

18 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

The generated ROC curves (Figures 4b, 4d, 5b, 5d, 6b, 6d) and their d’ values for 10 second windows (see also Tables 4-6) deteriorate at a continuous rate with increasing prediction time before manoeuvre (0s). When predicting lane change left manoeuvres at 0s, d’ values are just above 2.5 (Table 4). The advantage of the 4-viewing-zone-encoding only arises at -1.5s and very considerably at -2s. From 6.5s it is not discernible anymore, which type of encoding accounts more for an advantage, whereas d’ values are around 0.5. Up to -1s, either type of encoding is above a value of 2. In general, the difference between 4- and 5-viewing-zone-encoding is rather small. This minor difference continues in the results of lane change right manoeuvres with d’ values being below 2 at 0s (Table 5). From -2.5s onwards, the values drop steadily below 0.5 showing a minor advantage for 4-viewing-zone-encoding. Up to -0.5s, either type of encoding is above a value of 1.5. Hence, lane change manoeuvres to the right are less predictable than those to the left. Also, when discriminating between lane changes to the left and to the right, only a small difference between 4- and 5-viewing-zone-encoding can be observed (Table 6). At the beginning of a manoeuvre, d’ values of 3.1 are the highest followed by a drop down to just above 2 at -1.5s. In comparison with the other two learning problems (lane keeping / lane change left, and lane keeping / lane change right), the sharpest rate of improvement is seen between 0s and -1.5s. The generated ROC curves (Figures 4a, 4b, 5a, 5b, 6a, 6b) and their d’ values for 5 second windows (Tables 1-3) show better results, when predicting lane change left and right manoeuvres as well as discriminating the two from each other, than those yielded for the 10 second windows (Tables 4-6). The advantage of the 4-viewing-zone-encoding only surfaces very lightly, but is evident. The differences between lane change manoeuvres become apparent, in that d’ values for lane changes right are smaller than those for lane changes left. This accounts for 5 second windows as well as for 10 second windows. When predicting lane change left manoeuvres at 0s, d’ values are just above 3 (Table 1). The advantage of the 4-viewing-zone-encoding only arises at -0.5s and becomes more visible the further the point of time departs from the beginning of manoeuvre (0s). Up to -2s, either type of encoding is above a value of 2. When predicting lane change right manoeuvres at 0s, d’ values are just above 2.2 (Table 2). The advantage of the 4-viewing-zone-encoding is only marginal. Up to -1s, either type of encoding is above a d’ value of 1.5. When discriminating lane change left and right manoeuvres from each other at 0s, d’ values are above 3.5 (Table 3). The advantage of the 4-viewingzone-encoding only arises at -0.5s and becomes more obvious the further the point of time departs from the beginning of manoeuvre (0s). Up to -1.5s, either type of encoding and up to -2s, the 4viewing-zone-encoding is above a value of 2. These results show that a better predictive model can be produced with a 5 second window and suggests that the additional data contained in 10 second windows presumably hold noise, an ANN needs to learn to ignore. Furthermore, the results demonstrate that the advantage of 5 second windows only becomes visible under certain conditions depending on the problem to be learnt. From a certain distance to the beginning of manoeuvre, d’ values approximate to those yielded by 10 second windows or are even lower. With lane changes to the left such a ‘turning point’ occurs at -4s with a d’ value of around 1 and affects either size of window showing similar values. Hence, 5 second windows produce higher d’ values up to a distance of -4s when learning lane changes to the left than can be achieved by 10 second windows. With lane changes to the right, the turning point can be found at -2.5s in values of just above 0.5 for the 4-viewing-zone-encoding. In contrast, a huge difference can be observed for the 5-viewing-zone-encoding, where 10 second windows produce values of below 0.4 and 5 second

19 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

windows values of above 1. The advantage of 5 second windows and the 4-viewing-zone-encoding also becomes evident when discriminating lane change manoeuvres to the left from lane changes to the right, where a turning point occurs at -2.5s, showing higher d’ values for 10 second windows for bigger distances to the beginning of a manoeuvre. At -2s, the lane change manoeuvres can be discriminated with a d’ value of almost 2.2 using the 4-viewing-zone-encoding from a 5 second window of gaze data. The task of predicting lane change right appears to be more difficult than predicting lane change left as indicated by the d’ values obtained. Performance of the ANN models decreases as the time before the manoeuvre is increased as indicated by the changing d’ values and ROC curves (Figures 4-6). However, the ROC curves indicate that the predictions made by the ANNs are of genuine predictive value.

20 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

4a

4b

4c

4d

Fig. 4a-d. ROC curves showing the predictive performance of ANNs for discriminating between lane change left and lane keeping manoeuvres ranging from 0 to 2 seconds before the beginning of manoeuvre based on gaze data using 5 and 4 viewing zones for 5 and 10 second windows.

21 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

5a

5b

5c

5d

Fig. 5a-d. ROC curves showing the predictive performance of ANNs for discriminating between lane change right and lane keeping manoeuvres ranging from 0 to 2 seconds before the beginning of manoeuvre based on gaze data using 5 and 4 viewing zones for 5 and 10 second windows.

22 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

6a

6b

6c

6d

Fig. 6a-d. ROC curves showing the predictive performance of ANNs for discriminating between lane change left and lane change right manoeuvres ranging from 0 to 2 seconds before the beginning of manoeuvre based on gaze data using 5 and 4 viewing zones for 5 and 10 second windows.

23 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 7. Results of experiments using BNs to discriminate between lane keeping and lane change left manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Left 5 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

5s 0.932 [0.925] 0.068 [0.075] 2.981 [2.879] 0.963 [0.959] 0 [0] 0 [0]

4.5s 0.906 [0.906] 0.094 [0.094] 2.633 [2.633] 0.948 [0.948] 0 [0] 0 [0]

4s 0.904 [0.890] 0.096 [0.110] 2.609 [2.453] 0.946 [0.938] 0 [0] 0 [0]

3.5s 0.871 [0.860] 0.129 [0.140] 2.262 [2.160] 0.925 [0.918] 0 [0] 0 [0]

3s 0.844 [0.851] 0.156 [0.149] 2.022 [2.081] 0.907 [0.912] 0 [0] 0 [0]

2.5s 0.790 [0.811] 0.210 [0.189] 1.612 [1.763] 0.867 [0.883] 0 [0] 0 [0]

2s 0.729 [0.743] 0.271 [0.257] 1.219 [1.305] 0.814 [0.827] 0 [0] 0 [0]

1.5s 0.622 [0.692] 0.378 [0.308] 0.621 [1.003] 0.696 [0.777] 0 [0] 0 [0]

1s 0.612 [0.636] 0.388 [0.364] 0.569 [0.695] 0.683 [0.713] 0 [0] 0 [0]

0.5s 0.488 [0.495] 0.512 [0.505] -0.060 [-0.025] 0.476 [0.490] 0 [0] 0 [0]

Table 8. Results of experiments using BNs to discriminate between lane keeping and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Right 5 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

5s 0.868 [0.870] 0.132 [0.130] 2.233 [2.252] 0.923 [0.925] 0 [0] 0 [0]

4.5s 0.808 [0.829] 0.192 [0.171] 1.741 [1.900] 0.881 [0.896] 0 [0] 0 [0]

4s 0.782 [0.778] 0.218 [0.222] 1.557 [1.530] 0.860 [0.857] 0 [0] 0 [0]

3.5s 0.706 [0.701] 0.294 [0.299] 1.083 [1.054] 0.791 [0.786] 0 [0] 0 [0]

3s 0.613 [0.606] 0.387 [0.394] 0.574 [0.537] 0.684 [0.674] 0 [0] 0 [0]

2.5s 0.581 [0.555] 0.419 [0.445] 0.409 [0.276] 0.639 [0.599] 0 [0] 0 [0]

2s 0.493 [0.489] 0.507 [0.511] -0.035 [-0.055] 0.486 [0.478] 0 [0] 0 [0]

1.5s 0.493 [0.488] 0.507 [0.512] -0.035 [-0.060] 0.486 [0.476] 0 [0] 0 [0]

1s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

0.5s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

24 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 9. Results of experiments using BNs to discriminate between lane change left and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Left / Right 5 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

5s 0.956 [0.956] 0.044 [0.044] 3.412 [3.412] 0.976 [0.976] 0 [0] 0 [0]

4.5s 0.952 [0.945] 0.048 [0.055] 3.329 [3.196] 0.974 [0.970] 0 [0] 0 [0]

4s 0.923 [0.924] 0.077 [0.076] 2.851 [2.865] 0.958 [0.958] 0 [0] 0 [0]

3.5s 0.875 [0.873] 0.125 [0.127] 2.300 [2.281] 0.928 [0.927] 0 [0] 0 [0]

3s 0.782 [0.810] 0.218 [0.190] 1.557 [1.755] 0.860 [0.882] 0 [0] 0 [0]

2.5s 0.722 [0.738] 0.278 [0.262] 1.177 [1.274] 0.807 [0.822] 0 [0] 0 [0]

2s 0.660 [0.664] 0.340 [0.336] 0.824 [0.846] 0.742 [0.746] 0 [0] 0 [0]

1.5s 0.630 [0.600] 0.370 [0.400] 0.663 [0.506] 0.706 [0.666] 0 [0] 0 [0]

1s 0.602 [0.577] 0.398 [0.423] 0.517 [0.388] 0.669 [0.633] 0 [0] 0 [0]

0.5s 0.555 [0.549] 0.445 [0.451] 0.276 [0.246] 0.599 [0.589] 0 [0] 0 [0]

25 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 10. Results of experiments using BNs to discriminate between lane keeping and lane change left manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Left 10 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

10s 0.881 [0.878] 0.119 [0.122] 2.360 [2.330] 0.932 [0.930] 0 [0] 0 [0]

9.5s 0.879 [0.878] 0.121 [0.122] 2.340 [2.330] 0.931 [0.930] 0 [0] 0 [0]

9s 0.846 [0.830] 0.154 [0.170] 2.038 [1.908] 0.908 [0.897] 0 [0] 0 [0]

8.5s 0.808 [0.820] 0.192 [0.180] 1.741 [1.830] 0.881 [0.890] 0 [0] 0 [0]

8s 0.771 [0.809] 0.229 [0.191] 1.484 [1.748] 0.851 [0.881] 0 [0] 0 [0]

7.5s 0.747 [0.780] 0.253 [0.220] 1.330 [1.544] 0.830 [0.858] 0 [0] 0 [0]

7s 0.731 [0.740] 0.269 [0.260] 1.231 [1.286] 0.816 [0.824] 0 [0] 0 [0]

6.5s 0.698 [0.705] 0.302 [0.295] 1.037 [1.077] 0.783 [0.790] 0 [0] 0 [0]

6s 0.675 [0.675] 0.325 [0.325] 0.907 [0.907] 0.759 [0.759] 0 [0] 0 [0]

5.5s 0.586 [0.635] 0.414 [0.365] 0.434 [0.690] 0.646 [0.712] 0 [0] 0 [0]

-5s

-5.5s

-6s

-6.5s

-7s

-7.5s

-8s

-8.5s

-9s

-9.5s

5s 0.586 [0.573] 0.414 [0.427] 0.434 [0.368] 0.646 [0.627] 0 [0] 0 [0]

4.5s 0.566 [0.568] 0.434 [0.432] 0.332 [0.342] 0.616 [0.619] 0 [0] 0 [0]

4s 0.566 [0.566] 0.434 [0.434] 0.332 [0.332] 0.616 [0.616] 0 [0] 0 [0]

3.5s 0.493 [0.503] 0.507 [0.497] -0.035 [0.015] 0.486 [0.505] 0 [0] 0 [0]

3s 0.493 [0.516] 0.507 [0.484] -0.035 [0.080] 0.486 [0.531] 0 [0] 0 [0]

2.5s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

2s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

1.5s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

1s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

0.5s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

26 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 11. Results of experiments using BNs to discriminate between lane keeping and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Right 10 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

10s 0.789 [0.820] 0.211 [0.180] 1.605 [1.830] 0.866 [0.890] 0 [0] 0 [0]

9.5s 0.796 [0.790] 0.204 [0.210] 1.654 [1.612] 0.871 [0.867] 0 [0] 0 [0]

9s 0.762 [0.748] 0.238 [0.252] 1.425 [1.336] 0.843 [0.831] 0 [0] 0 [0]

8.5s 0.690 [0.687] 0.310 [0.313] 0.991 [0.974] 0.775 [0.772] 0 [0] 0 [0]

8s 0.629 [0.595] 0.371 [0.405] 0.658 [0.480] 0.705 [0.659] 0 [0] 0 [0]

7.5s 0.560 [0.570] 0.440 [0.430] 0.302 [0.352] 0.607 [0.622] 0 [0] 0 [0]

7s 0.498 [0.539] 0.502 [0.461] -0.010 [0.196] 0.496 [0.572] 0 [0] 0 [0]

6.5s 0.502 [0.489] 0.498 [0.511] 0.010 [-0.055] 0.503 [0.478] 0 [0] 0 [0]

6s 0.500 [0.493] 0.500 [0.507] 0 [-0.035] 0.500 [0.486] 0 [0] 0 [0]

5.5s 0.502 [0.493] 0.498 [0.507] 0.010 [-0.035] 0.503 [0.486] 0 [0] 0 [0]

-5s

-5.5s

-6s

-6.5s

-7s

-7.5s

-8s

-8.5s

-9s

-9.5s

5s 0.495 [0.493] 0.505 [0.507] -0.025 [-0.035] 0.490 [0.486] 0 [0] 0 [0]

4.5s 0.495 [0.493] 0.505 [0.507] -0.025 [-0.035] 0.490 [0.486] 0 [0] 0 [0]

4s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

3.5s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

3s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

2.5s 0.495 [0.493] 0.505 [0.507] -0.025 [-0.035] 0.490 [0.486] 0 [0] 0 [0]

2s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

1.5s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

1s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

0.5s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

27 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 12. Results of experiments using BNs to discriminate between lane change left and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Left / Right 10 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

10s 0.951 [0.928] 0.049 [0.072] 3.309 [2.922] 0.974 [0.961] 0 [0] 0 [0]

9.5s 0.930 [0.926] 0.070 [0.074] 2.951 [2.893] 0.962 [0.960] 0 [0] 0 [0]

9s 0.894 [0.903] 0.106 [0.097] 2.496 [2.597] 0.940 [0.946] 0 [0] 0 [0]

8.5s 0.836 [0.859] 0.164 [0.141] 1.956 [2.151] 0.901 [0.917] 0 [0] 0 [0]

8s 0.778 [0.813] 0.222 [0.187] 1.530 [1.778] 0.857 [0.884] 0 [0] 0 [0]

7.5s 0.724 [0.718] 0.276 [0.282] 1.189 [1.153] 0.809 [0.803] 0 [0] 0 [0]

7s 0.674 [0.704] 0.326 [0.296] 0.902 [1.071] 0.758 [0.789] 0 [0] 0 [0]

6.5s 0.671 [0.678] 0.329 [0.322] 0.885 [0.924] 0.754 [0.762] 0 [0] 0 [0]

6s 0.667 [0.660] 0.333 [0.340] 0.863 [0.824] 0.750 [0.742] 0 [0] 0 [0]

5.5s 0.644 [0.636] 0.356 [0.364] 0.738 [0.695] 0.723 [0.713] 0 [0] 0 [0]

-5s

-5.5s

-6s

-6.5s

-7s

-7.5s

-8s

-8.5s

-9s

-9.5s

5s 0.621 [0.623] 0.379 [0.377] 0.616 [0.626] 0.694 [0.697] 0 [0] 0 [0]

4.5s 0.609 [0.548] 0.391 [0.452] 0.553 [0.241] 0.678 [0.587] 0 [0] 0 [0]

4s 0.551 [0.502] 0.449 [0.498] 0.256 [0.010] 0.592 [0.503] 0 [0] 0 [0]

3.5s 0.495 [0.493] 0.505 [0.507] -0.025 [-0.035] 0.490 [0.486] 0 [0] 0 [0]

3s 0.491 [0.493] 0.509 [0.507] -0.045 [-0.035] 0.482 [0.486] 0 [0] 0 [0]

2.5s 0.502 [0.493] 0.498 [0.507] 0.010 [-0.035] 0.503 [0.486] 0 [0] 0 [0]

2s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

1.5s 0.491 [0.493] 0.509 [0.507] -0.045 [-0.035] 0.482 [0.486] 0 [0] 0 [0]

1s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

0.5s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

28 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

The generated ROC curves (Figures 7b, 7d, 8b, 8d, 9b, 9d) and their d’ values for 10 second windows (see also Tables 10-12) deteriorate at a continuous rate with increasing prediction time before manoeuvre (0s). When predicting lane change left manoeuvres at 0s, d’ values are just above 2.3 (Table 10). The advantage of the 4-viewing-zone-encoding only arises at -1.5s and very considerably at -2s. From -5.5s, it is not discernible anymore, which type of encoding accounts more for an advantage, whereas from -6.5s d’ values are around 0.5. Up to -1s, either type of encoding is above a value of 2. In general, the difference between 4- and 5-viewing-zone-encoding is rather small. This minor difference continues in the results of lane change right manoeuvres with d’ values being below 2 at 0s (Table 11). From -3s onwards, the values stay at around 0. Up to -0.5s, either type of encoding is above a value of 1.5. Hence, lane change manoeuvres to the right are less predictable than those to the left. Also, when discriminating between lane changes to the left and to the right, only a small difference between 4- and 5-viewing-zone-encoding can be observed (Table 12). At the beginning of a manoeuvre, d’ values of 3.3 for 5-viewing-zone-encoding are the highest followed by a drop down to just above 2 at -1.5s. In comparison with the other two learning problems (lane keeping / lane change left, and lane keeping / lane change right), the sharpest rate of improvement is seen between 0s and 2.5s. The generated ROC curves (Figures 7a, 7c, 8a, 8c, 9a, 9c) and their d’ values for 5 second windows (Tables 7-9) show better results, when predicting lane change left and right manoeuvres as well as discriminating the two from each other, than those yielded for the 10 second windows (Tables 10-12). The advantage of the 4-viewing-zone-encoding only surfaces very lightly, but is evident. The differences between lane change manoeuvres become apparent, in that d’ values for lane changes right are smaller than those for lane changes left. This accounts for 5 second windows as well as for 10 second windows. When predicting lane change left manoeuvres at 0s, d’ values are just above 3 (Table 7). The advantage of the 4-viewing-zone-encoding only arises at -2s and becomes more visible the further the point of time departs from the beginning of manoeuvre (0s). Up to -2s, either type of encoding is above a value of 2. When predicting lane change right manoeuvres at 0s, d’ values are just above 2.2 (Table 8). The advantage of the 4-viewing-zone-encoding is only discernible at -0.5s. Up to -1s, either type of encoding is above a d’ value of 1.5. When discriminating lane change left and right manoeuvres from each other at 0s, d’ values are just below 3.5 (Table 9). The advantage of the 4viewing-zone-encoding only arises at -2s to -3s. Up to -1.5s, either type of encoding is above a value of 2. These results show that a better predictive model can be produced with a 5 second window and suggests that, as with ANNs, the additional data contained in 10 second windows presumably hold noise, a BN needs to learn to ignore. Furthermore, the results demonstrate that the advantage of 5 second windows only becomes visible under certain conditions depending on the problem to be learnt. From a certain distance to the beginning of manoeuvre, d’ values approximate to those yielded by 10 second windows or are even lower. With lane changes to the left such a ‘turning point’ occurs at -3s with a d’ value of around 1.2 and affects either type of encoding showing similar values. Hence, 5 second windows produce higher d’ values up to a distance of -3s when learning lane changes to the left than can be achieved by 10 second windows. With lane changes to the right, the turning point can be found at -2.5s in values of just below 0.5 for either type of encoding. At -2s, the lane change manoeuvres can be discriminated equally well and also show similar values.

29 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

The task of predicting lane change right appears to be more difficult than predicting lane change left as indicated by the d’ values obtained. Performance of the BN models decreases as the time before the manoeuvre is increased as indicated by the changing d’ values and ROC curves (Figures 7-9). As with the ANNs, the ROC curves indicate that the predictions made by the BNs are of genuine predictive value.

30 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

7a

7b

7c

7d

Fig. 7a-d. ROC curves showing the predictive performance of BNs for discriminating between lane change left and lane keeping manoeuvres ranging from 0 to 2 seconds before the beginning of manoeuvre based on gaze data using 5 and 4 viewing zones for 5 and 10 second windows.

31 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

8a

8b

8c

8d

Fig. 8a-d. ROC curves showing the predictive performance of BNs for discriminating between lane change right and lane keeping manoeuvres ranging from 0 to 2 seconds before the beginning of manoeuvre based on gaze data using 5 and 4 viewing zones for 5 and 10 second windows.

32 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

9a

9b

9c

9d

Fig. 9a-d. ROC curves showing the predictive performance of BNs for discriminating between lane change left and lane change right manoeuvres ranging from 0 to 2 seconds before the beginning of manoeuvre based on gaze data using 5 and 4 viewing zones for 5 and 10 second windows.

33 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 13. Results of experiments using NBCs to discriminate between lane keeping and lane change left manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Left 5 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

5s 0.906 [0.927] 0.094 [0.073] 2.633 [2.907] 0.948 [0.960] 0 [0] 0 [0]

4.5s 0.881 [0.885] 0.119 [0.115] 2.360 [2.400] 0.932 [0.935] 0 [0] 0 [0]

4s 0.855 [0.874] 0.145 [0.126] 2.116 [2.291] 0.915 [0.927] 0 [0] 0 [0]

3.5s 0.823 [0.867] 0.177 [0.133] 1.853 [2.224] 0.892 [0.923] 0 [0] 0 [0]

3s 0.783 [0.844] 0.217 [0.156] 1.564 [2.022] 0.861 [0.907] 0 [0] 0 [0]

2.5s 0.729 [0.802] 0.271 [0.198] 1.219 [1.697] 0.814 [0.876] 0 [0] 0 [0]

2s 0.678 [0.733] 0.322 [0.267] 0.924 [1.243] 0.762 [0.817] 0 [0] 0 [0]

1.5s 0.640 [0.684] 0.360 [0.316] 0.717 [0.957] 0.718 [0.769] 0 [0] 0 [0]

1s 0.600 [0.631] 0.400 [0.369] 0.506 [0.669] 0.666 [0.707] 0 [0] 0 [0]

0.5s 0.551 [0.570] 0.449 [0.430] 0.256 [0.352] 0.592 [0.622] 0 [0] 0 [0]

Table 14. Results of experiments using NBCs to discriminate between lane keeping and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Right 5 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

5s 0.831 [0.863] 0.169 [0.137] 1.916 [2.187] 0.898 [0.920] 0 [0] 0 [0]

4.5s 0.776 [0.812] 0.224 [0.188] 1.517 [1.770] 0.855 [0.884] 0 [0] 0 [0]

4s 0.710 [0.761] 0.290 [0.239] 1.106 [1.419] 0.795 [0.842] 0 [0] 0 [0]

3.5s 0.650 [0.683] 0.350 [0.317] 0.770 [0.952] 0.730 [0.767] 0 [0] 0 [0]

3s 0.616 [0.634] 0.384 [0.366] 0.590 [0.685] 0.688 [0.711] 0 [0] 0 [0]

2.5s 0.551 [0.565] 0.449 [0.435] 0.256 [0.327] 0.592 [0.615] 0 [0] 0 [0]

2s 0.542 [0.546] 0.458 [0.454] 0.211 [0.231] 0.577 [0.584] 0 [0] 0 [0]

1.5s 0.544 [0.572] 0.456 [0.428] 0.221 [0.363] 0.580 [0.625] 0 [0] 0 [0]

1s 0.514 [0.542] 0.486 [0.458] 0.070 [0.211] 0.527 [0.577] 0 [0] 0 [0]

0.5s 0.523 [0.537] 0.477 [0.463] 0.115 [0.186] 0.543 [0.568] 0 [0] 0 [0]

34 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 15. Results of experiments using NBCs to discriminate between lane change left and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Left / Right 5 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

5s 0.965 [0.956] 0.035 [0.044] 3.623 [3.412] 0.981 [0.976] 0 [0] 0 [0]

4.5s 0.947 [0.898] 0.053 [0.102] 3.232 [2.540] 0.972 [x] 0 [0] 0 [0]

4s 0.914 [0.840] 0.086 [0.160] 2.731 [1.988] 0.952 [0.904] 0 [0] 0 [0]

3.5s 0.829 [0.674] 0.171 [0.326] 1.900 [0.902] 0.896 [0.758] 0 [0] 0 [0]

3s 0.783 [0.607] 0.217 [0.393] 1.564 [0.543] 0.861 [0.676] 0 [0] 0 [0]

2.5s 0.715 [0.729] 0.285 [0.271] 1.136 [1.219] 0.800 [0.814] 0 [0] 0 [0]

2s 0.653 [0.687] 0.347 [0.313] 0.786 [0.974] 0.734 [0.772] 0 [0] 0 [0]

1.5s 0.625 [0.644] 0.375 [0.356] 0.637 [0.738] 0.700 [0.723] 0 [0] 0 [0]

1s 0.579 [0.618] 0.421 [0.382] 0.398 [0.600] 0.636 [0.690] 0 [0] 0 [0]

0.5s 0.560 [0.551] 0.440 [0.449] 0.302 [0.256] 0.607 [0.592] 0 [0] 0 [0]

35 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 16. Results of experiments using NBCs to discriminate between lane keeping and lane change left manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Left 10 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

10s 0.865 [0.872] 0.135 [0.128] 2.206 [2.271] 0.921 [0.926] 0 [0] 0 [0]

9.5s 0.851 [0.836] 0.149 [0.164] 2.081 [1.956] 0.912 [0.901] 0 [0] 0 [0]

9s 0.806 [0.795] 0.194 [0.205] 1.726 [1.647] 0.879 [0.871] 0 [0] 0 [0]

8.5s 0.787 [0.762] 0.213 [0.238] 1.592 [1.425] 0.864 [0.843] 0 [0] 0 [0]

8s 0.757 [0.741] 0.243 [0.259] 1.393 [1.292] 0.839 [0.825] 0 [0] 0 [0]

7.5s 0.715 [0.757] 0.285 [0.243] 1.136 [1.393] 0.800 [0.839] 0 [0] 0 [0]

7s 0.687 [0.713] 0.313 [0.287] 0.974 [1.124] 0.772 [0.798] 0 [0] 0 [0]

6.5s 0.652 [0.671] 0.348 [0.329] 0.781 [0.885] 0.733 [0.754] 0 [0] 0 [0]

6s 0.619 [0.659] 0.381 [0.341] 0.605 [0.819] 0.692 [0.741] 0 [0] 0 [0]

5.5s 0.601 [0.640] 0.399 [0.360] 0.512 [0.717] 0.668 [0.718] 0 [0] 0 [0]

-5s

-5.5s

-6s

-6.5s

-7s

-7.5s

-8s

-8.5s

-9s

-9.5s

5s 0.591 [0.633] 0.409 [0.367] 0.460 [0.679] 0.653 [0.710] 0 [0] 0 [0]

4.5s 0.568 [0.615] 0.432 [0.385] 0.342 [0.584] 0.619 [0.686] 0 [0] 0 [0]

4s 0.559 [0.605] 0.441 [0.395] 0.297 [0.532] 0.605 [0.673] 0 [0] 0 [0]

3.5s 0.552 [0.584] 0.448 [0.416] 0.261 [0.424] 0.594 [0.643] 0 [0] 0 [0]

3s 0.537 [0.582] 0.463 [0.418] 0.186 [0.414] 0.568 [0.640] 0 [0] 0 [0]

2.5s 0.533 [0.552] 0.467 [0.448] 0.165 [0.261] 0.561 [0.594] 0 [0] 0 [0]

2s 0.535 [0.533] 0.465 [0.467] 0.175 [0.165] 0.565 [0.561] 0 [0] 0 [0]

1.5s 0.533 [0.554] 0.467 [0.446] 0.165 [0.271] 0.561 [0.597] 0 [0] 0 [0]

1s 0.526 [0.542] 0.474 [0.458] 0.130 [0.211] 0.549 [0.577] 0 [0] 0 [0]

0.5s 0.505 [0.497] 0.495 [0.503] 0.025 [-0.015] 0.509 [0.494] 0 [0] 0 [0]

36 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 17. Results of experiments using NBCs to discriminate between lane keeping and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Right 10 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

10s 0.787 [0.829] 0.213 [0.171] 1.592 [1.900] 0.864 [0.896] 0 [0] 0 [0]

9.5s 0.739 [0.775] 0.261 [0.225] 1.280 [1.510] 0.823 [0.854] 0 [0] 0 [0]

9s 0.690 [0.704] 0.310 [0.296] 0.991 [1.071] 0.775 [0.789] 0 [0] 0 [0]

8.5s 0.643 [0.643] 0.357 [0.357] 0.733 [0.733] 0.722 [0.722] 0 [0] 0 [0]

8s 0.563 [0.604] 0.437 [0.396] 0.317 [0.527] 0.611 [0.672] 0 [0] 0 [0]

7.5s 0.569 [0.583] 0.431 [0.417] 0.347 [0.419] 0.621 [0.642] 0 [0] 0 [0]

7s 0.563 [0.562] 0.437 [0.438] 0.317 [0.312] 0.611 [0.610] 0 [0] 0 [0]

6.5s 0.560 [0.555] 0.440 [0.445] 0.302 [0.276] 0.607 [0.599] 0 [0] 0 [0]

6s 0.555 [0.542] 0.445 [0.458] 0.276 [0.211] 0.599 [0.577] 0 [0] 0 [0]

5.5s 0.546 [0.526] 0.454 [0.474] 0.231 [0.130] 0.584 [0.549] 0 [0] 0 [0]

-5s

-5.5s

-6s

-6.5s

-7s

-7.5s

-8s

-8.5s

-9s

-9.5s

5s 0.556 [0.521] 0.444 [0.479] 0.281 [0.105] 0.600 [0.540] 0 [0] 0 [0]

4.5s 0.540 [0.516] 0.460 [0.484] 0.201 [0.080] 0.574 [0.531] 0 [0] 0 [0]

4s 0.546 [0.514] 0.454 [0.486] 0.231 [0.070] 0.584 [0.527] 0 [0] 0 [0]

3.5s 0.526 [0.518] 0.474 [0.482] 0.130 [0.090] 0.549 [0.534] 0 [0] 0 [0]

3s 0.528 [0.519] 0.472 [0.481] 0.140 [0.095] 0.553 [0.536] 0 [0] 0 [0]

2.5s 0.544 [0.512] 0.456 [0.488] 0.221 [0.060] 0.580 [0.523] 0 [0] 0 [0]

2s 0.551 [0.540] 0.449 [0.460] 0.256 [0.201] 0.592 [0.574] 0 [0] 0 [0]

1.5s 0.533 [0.553] 0.467 [0.447] 0.165 [0.266] 0.561 [0.595] 0 [0] 0 [0]

1s 0.537 [0.551] 0.463 [0.449] 0.186 [0.256] 0.568 [0.592] 0 [0] 0 [0]

0.5s 0.528 [0.540] 0.472 [0.460] 0.140 [0.201] 0.553 [0.574] 0 [0] 0 [0]

37 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 18. Results of experiments using NBCs to discriminate between lane change left and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Left / Right 10 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

10s 0.935 [0.935] 0.065 [0.065] 3.028 [3.028] 0.965 [0.965] 0 [0] 0 [0]

9.5s 0.912 [0.894] 0.088 [0.106] 2.706 [2.496] 0.951 [0.940] 0 [0] 0 [0]

9s 0.873 [0.843] 0.127 [0.157] 2.281 [2.013] 0.927 [0.906] 0 [0] 0 [0]

8.5s 0.831 [0.718] 0.169 [0.282] 1.916 [1.153] 0.898 [0.803] 0 [0] 0 [0]

8s 0.769 [0.687] 0.231 [0.313] 1.471 [0.974] 0.849 [0.772] 0 [0] 0 [0]

7.5s 0.722 [0.662] 0.278 [0.338] 1.177 [0.835] 0.807 [0.744] 0 [0] 0 [0]

7s 0.683 [0.671] 0.317 [0.329] 0.952 [0.885] 0.767 [0.754] 0 [0] 0 [0]

6.5s 0.664 [0.655] 0.336 [0.345] 0.846 [0.797] 0.746 [0.736] 0 [0] 0 [0]

6s 0.646 [0.646] 0.354 [0.354] 0.749 [0.749] 0.726 [0.726] 0 [0] 0 [0]

5.5s 0.629 [0.643] 0.371 [0.357] 0.658 [0.733] 0.705 [0.722] 0 [0] 0 [0]

-5s

-5.5s

-6s

-6.5s

-7s

-7.5s

-8s

-8.5s

-9s

-9.5s

5s 0.618 [0.627] 0.382 [0.373] 0.600 [0.647] 0.690 [0.702] 0 [0] 0 [0]

4.5s 0.613 [0.592] 0.387 [0.408] 0.574 [0.465] 0.684 [0.655] 0 [0] 0 [0]

4s 0.595 [0.572] 0.405 [0.428] 0.480 [0.363] 0.659 [0.625] 0 [0] 0 [0]

3.5s 0.574 [0.560] 0.426 [0.440] 0.373 [0.302] 0.628 [0.607] 0 [0] 0 [0]

3s 0.563 [0.537] 0.437 [0.463] 0.317 [0.186] 0.611 [0.568] 0 [0] 0 [0]

2.5s 0.546 [0.533] 0.454 [0.467] 0.231 [0.165] 0.584 [0.561] 0 [0] 0 [0]

2s 0.555 [0.549] 0.445 [0.451] 0.276 [0.246] 0.599 [0.589] 0 [0] 0 [0]

1.5s 0.549 [0.551] 0.451 [0.449] 0.246 [0.256] 0.589 [0.592] 0 [0] 0 [0]

1s 0.546 [0.526] 0.454 [0.474 ] 0.231 [0.130] 0.584 [0.549] 0 [0] 0 [0]

0.5s 0.533 [0.551] 0.467 [0.449] 0.165 [0.256] 0.561 [0.592] 0 [0] 0 [0]

38 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

The generated ROC curves (Figures 10b, 10d, 11b, 11d, 12b, 12d) and their d’ values for 10 second windows (see also Tables 10-12) deteriorate at a continuous rate with increasing prediction time before manoeuvre (0s). When predicting lane change left manoeuvres at 0s, d’ values are just above 2.2 (Table 10). The advantage of the 4-viewing-zone-encoding only arises at -2.5s. From -9.5s, the d' values lie at 0. Up to -0.5s, either type of encoding is above a value of 2. In general, the difference between 4- and 5-viewing-zone-encoding is rather small. This minor difference continues in the results of lane change right manoeuvres with d’ values being below 2 at 0s (Table 11). After a steady drop, the values are smaller than 0.3 from -3s onwards. Up to -0.5s, the 4-viewing-zone-encoding is just above a value of 1.5. As could be seen in the results of ANNs and BNs, lane change manoeuvres to the right are less predictable than those to the left. Also, when discriminating between lane changes to the left and to the right, only a small difference between 4- and 5-viewing-zone-encoding can be observed, except the section of -1,5s to -2,5s (Table 12). At the beginning of a manoeuvre, d’ values of just 3 are the highest for either type of encoding. An advantage of the 4-viewing-zone-encoding cannot be observed here, in fact, d' values of the 5-viewing-zone-encoding are distinctly higher up to -2.5s. In comparison with the other two learning problems (lane keeping / lane change left, and lane keeping / lane change right), the sharpest rate of improvement is seen between 0s and -2.5s. The generated ROC curves (Figures 10a, 10c, 11a, 11c, 12a, 12c) and their d’ values for 5 second windows (Tables 13-15) show better results, when predicting lane change left and right manoeuvres as well as discriminating the two from each other, than those yielded for the 10 second windows (Tables 16-18). The advantage of the 4-viewing-zone-encoding surfaces very clearly and throughout for lane change left and right manoeuvres. The differences between lane change manoeuvres become apparent, in that, as in the results for ANNs and BNs, d’ values for lane changes right are smaller than those for lane changes left. This accounts for 5 second windows as well as for 10 second windows. When predicting lane change left manoeuvres at 0s, d’ values are below 3 (Table 13). The advantage of the 4-viewing-zone-encoding already arises at 0s and becomes more visible the further the point of time departs from the manoeuvre onset (0s). Up to -1s, either type of encoding is above a value of 2, whereas the 4-viewing-zone-encoding reaches this value up to -2s. When predicting lane change right manoeuvres at 0s, d’ values are around 2 (Table 14). The advantage of the 4-viewing-zone-encoding shows here throughout. Up to -0.5s, either type of encoding is above a d’ value of 1.5. When discriminating lane change left and right manoeuvres from each other at 0s, d’ values are around 3.5 (Table 15). The advantage of the 4-viewing-zone-encoding only arises at -2.5s. Up to -1s, the 5-viewing-zone-encoding is above and the 4-viewing-zone-encoding just under a value of 2. These results show that a better predictive model can be produced with a 5 second window and suggests that, as with ANNs and BNs, the additional data contained in 10 second windows presumably hold noise, an NBC needs to learn to ignore. Furthermore, the results demonstrate that the advantage of 5 second windows only becomes visible under certain conditions depending on the problem to be learnt. From a certain distance to the beginning of manoeuvre, d’ values approximate to those yielded by 10 second windows or are even lower. With lane changes to the left such a ‘turning point’ occurs at -3.5s with a d’ value of around 0.8 and shows slightly increased values for the 4viewing-zone-encoding. Hence, 5 second windows produce higher d’ values up to a distance of -3.5s when learning lane changes to the left than can be achieved by 10 second windows. With lane changes to the right, the turning point can be found at -2.5s in values of just below 0.5 for either type of

39 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

encoding. From -1.5s, the lane change manoeuvres can be discriminated equally well and also show similar values at this point, which are clearly in favour of the 5-viewing-zone-encoding. The task of predicting lane change right appears to be more difficult than predicting lane change left as indicated by the d’ values obtained. The performance of the NBC models decreases as the time before the manoeuvre is increased as indicated by the changing d’ values and ROC curves (Figures 10-12). As with the ANNs and BNs, the ROC curves indicate that the predictions made by the NBCs are of genuine predictive value.

40 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

10a

10b

10c

10d

Fig. 10a-d. ROC curves showing the predictive performance of NBCs for discriminating between lane change left and lane keeping manoeuvres ranging from 0 to 2 seconds before the beginning of manoeuvre based on gaze data using 5 and 4 viewing zones for 5 and 10 second windows.

41 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

11a

11b

11c

11d

Fig. 11a-d. ROC curves showing the predictive performance of NBCs for discriminating between lane change right and lane keeping manoeuvres ranging from 0 to 2 seconds before the beginning of manoeuvre based on gaze data using 5 and 4 viewing zones for 5 and 10 second windows.

42 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

12a

12b

12c

12d

Fig. 12a-d. ROC curves showing the predictive performance of NBCs for discriminating between lane change left and lane change right manoeuvres ranging from 0 to 2 seconds before the beginning of manoeuvre based on gaze data using 5 and 4 viewing zones for 5 and 10 second windows.

43 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

In general and for all three predictive models, the d' values show that the greater the distance from the onset of a manoeuvre (0s) the more the predictive performance decreases. This accounts for 10- as well as for 5 second windows of data. Here, an acceptable performance is not ought to lie below a d' value of 2. Based on the results for 5 second windows of data, it seems to be more difficult to predict lane change manoeuvres to the right (Figure 14) than to the left (Figure 13). As could already be demonstrated during experimental drives in real traffic [19,20], planning and preparing a lane change manoeuvre to the left runs across a longer period of time than during a lane change manoeuvre to the right in dynamically simulated traffic. A direct comparison of the results of ANNs, BNs, and NBCs demonstrates that the performance of all three predictive models decreases with lane change manoeuvres to the left and to the right as well as with discriminating between lane change left and right manoeuvres with increasing distance to manoeuvre onset (Figure 15). Overall, ANNs show the strongest result and respond the best to the 4-viewing-zone-encoding. This is reflected in particular in lane change left manoeuvres with increasing distance from manoeuvre onset, whereas NBCs visibly respond here as well. The discrimination between lane change left and right manoeuvres from each other yields the highest d' values of all three learning problems across all three predictive models (Figure 15). This is characteristic for either type of encoding.

Fig. 13. Overview of results for ANNs, BNs, and NBCs presented as bar charts of d' values in direct comparison pertaining to discrimination performance between lane keeping and lane change left manoeuvres, at increasing time from the manoeuvre onset, across all 5 and 4 viewing zones, either type of road, and all drivers, based on 5 second windows of gaze data.

44 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Fig. 14. Overview of results for ANNs, BNs, and NBCs presented as bar charts of d' values in direct comparison pertaining to discrimination performance between lane keeping and lane change right manoeuvres, at increasing time from the manoeuvre onset, across all 5 and 4 viewing zones, either type of road, and all drivers, based on 5 second windows of gaze data.

Fig. 15. Overview of results for ANNs, BNs, and NBCs presented as bar charts of d' values in direct comparison pertaining to discrimination performance between lane change left and lane change right manoeuvres, at increasing time from the manoeuvre onset, across all 5 and 4 viewing zones, either type of road, and all drivers, based on 5 second windows of gaze data.

45 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

6 Conclusions

This study has shown that gaze data are viable as a ‘stand-alone’ predictive measure of lane change manoeuvres. The size of the window of data has been shown to be important in producing good predictive models and the advantage of using 5 second windows of gaze data in comparison with 10 second windows could be clearly demonstrated. Care should be taken to use as small a window of data as possible in order to avoid the inclusion of irrelevant sections which have the potential to act as additional noise. The closer a section of data is located to the beginning of a lane change manoeuvre, the better a predictive model can be produced and the higher a model’s performance in discriminating lane change manoeuvres from lane keeping manoeuvres. Furthermore, it was shown that lane change manoeuvres to the left fundamentally differ from lane change manoeuvres to the right and therefore form two completely different learning problems, which require different approaches. Additionally, a change in weights of the viewing zones considered could be procured while also reducing data redundancy. This was achieved by disregarding the viewing zone “windscreen” in the 4-viewing-zoneencoding as this can be seen as a default gaze location when driving a car. It could be shown that 4viewing-zone-encoding occasionally, or rather generally, depending on the problem to be learnt and the predictive model to be produced clearly increases the discriminative performance. The results lead to the conclusion that it is more challenging to produce a predictive model for lane change right than lane change left and that better results may be obtained on the lane change right problem by generating a larger dataset and / or using a more complex model. It is theorised that if it is found to be true that measurable signs of planning for lane change right (as indicated by gaze behaviour) start later than for lane change left, this could account for the poorer performance of all three predictive models at predicting lane change right manoeuvres relative to lane change left. This would be due to the smaller amount of available data within any time window chosen relative to that available for lane change left based on 5- and 4-viewing-zone-encoding, as the major part of planning takes place via the windscreen, literally having the goal in view, includes mirror gazes only conditionally, and seems to be sufficient to produce a conducive 3D-model of the surrounding traffic situation relevant to the driving task. For a better understanding, the following should be shown quite plainly: If a driver finds himself in a convenient situation to carry out a lane change right manoeuvre, he has overtaken the traffic on the right side, that is, the traffic situation is apparent and straightforward when approaching from behind, as visual information is available to the driver without the need to shift gaze. Henceforth, this information is obtained via the windscreen. In contrast, when planning a lane change left manoeuvre, the driver needs to assess the situation on a lane, which carries fast moving traffic, by obtaining the information necessary to decide about carrying out a manoeuvre almost exclusively from behind. Accordingly, gazes into the left wing mirror or the left window take

46 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

place earlier and last longer than characteristic gazes with a lane change manoeuvre to the right, where the driver merely needs to look ahead in order to get a picture of the situation on the right lane. A proof for lane change manoeuvres to the left fundamentally differing from lane change manoeuvres to the right when producing predictive models can be found in the good results when discriminating either lane change manoeuvre from each other. Of all three problems to be learnt and across either type of encoding, this highest discriminative performance was found in 5 and 10 second windows. In this context, the question arises of what would be considered a sufficiently accepted performance in order to predict an intended driving manoeuvre. Would a threshold of d' > 2 be too low? Information about driver intent enriches assistance functions with valuable knowledge and they experience a significant additional value, since they can adapt to the drivers’ behaviour in realtime that way. Thus, drivers’ planned actions can be considered in an information strategy comprising ADAS. The development of such a function would require the implementation of a prediction model into an online compliant system, which would need to consider other challenges, such as processing a continuous stream of data. This study delivers findings with respect to characteristics of gaze data in the context of driving manoeuvres and identifies new paths of temporal-local and weighted data encoding. Future work will seek to combine gaze data with other available car data in order to ascertain what effects this has on the models’ predictive capabilities.

47 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

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[16] D.D. Salvucci, E.R. Boer, A. Liu, Toward an integrated model of driver behaviour in a cognitive architecture, Transportation Research Record 1779 (2001) 9–16. [17] D.D. Salvucci, Inferring driver intent: a case study in lane-change detection, in: Proceedings of the HFES 48th Annual Meeting, Santa Monica, CA, USA, 2004. [18] J.R. Anderson, C. Lebiere, The atomic components of thought, Lawrence Erlbaum Associates, London, 1998. [19] F. Lethaus, J. Rataj, Do eye movements reflect driving manoeuvres?, IET Intelligent Transport Systems 1(3) (2007) 199–204. [20] F. Lethaus, J. Rataj, Using eye movements as a reference to identify driving manoeuvres, in: ATZ | ATZautotechnology (Ed.), Proceedings of the FISITA World Automotive Congress 2008, vol. 1, Springer Automotive Media, Wiesbaden, 2008. [21] R.R. Mourant, R.J. Donohue, Acquisition of indirect vision information by novice, experiences, and mature drivers, Journal of Safety Research 9 (1977) 39–46. [22] G. Pastor, P. Tejero, M. Chóliz, J. Roca, Rear-view mirror use, driver alertness and road type: an empirical study using EEG measures, Transportation Research: Part F 9 (2006) 286–297. [23] G. Underwood, P. Chapman, N. Brocklehurst, J. Underwood, D.E. Crundall, Visual attention while driving: sequences of eye fixations made by experienced and novice drivers, Ergonomics 46 (6) (2003) 629–646. [24] M.A. Recarte, L.M. Nunes, Effects of verbal and spatial-imagery tasks on eye fixations while driving, Journal of Experimental Psychology: Applied 6 (1) (2000) 31–43. [25] D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning internal representations by error propagation, in: D.E. Rumelhart, J.L. McClelland (Eds.), Parallel Distributed Processing, vol. 1, MIT Press, Cambridge, 1986, pp. 318–362. [26] L. Prechelt, PROBEN1 - A Set of Neural Network Benchmark Problems and Benchmarking Rules, Technical Report 21/94, Universität Karlsruhe, 1994. [27] J.P. Egan, Signal detection theory and ROC analysis. Series in Cognition and Perception, Academic Press, New York, 1975. [28] T. Fawcett, An introduction to ROC analysis, Pattern Recognition Letters 27 (2006) 861–874. [29] K.A. Spackman, Signal detection theory: Valuable tools for evaluating inductive learning, in: Proceedings of the Sixth International Workshop on Machine Learning, Morgan Kaufman, San Mateo, CA, 1989, pp. 160–163. [30] J.A. Swets, Measuring the accuracy of diagnostic systems, Science 240 (1988) 1285–1293. [31] J.A. Swets, R.M. Dawes, J. Monahan, Better decisions through science, Scientific American 283 (2000) 82–87. [32] P.B. Elliott, Tables of d', in: J.A. Swets (Ed.), Signal detection and recognition by human observers, Wiley, New York, 1964, pp. 651–684. [33] A.L. Brophy, Alternatives to a table of criterion values in signal detection theory, Behavior Research Methods, Instruments, & Computers 18 (1986) 285–286.

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50 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Figure captions Fig. 1. Dynamic driving simulator at the German Aerospace Center. Fig. 2. Time windows of 10 seconds of gaze data selected prior to the beginning of lane change left and right manoeuvres. Fig. 3. Gaze data used from a section of typical gaze patterns across 5 viewing zones prior to a lane change left manoeuvre. Fig. 4a-d. ROC curves showing the predictive performance of ANNs for discriminating between lane change left and lane keeping manoeuvres ranging from 0 to 2 seconds before the beginning of manoeuvre based on gaze data using 5 and 4 viewing zones for 5 and 10 second windows. Fig. 5a-d. ROC curves showing the predictive performance of ANNs for discriminating between lane change right and lane keeping manoeuvres ranging from 0 to 2 seconds before the beginning of manoeuvre based on gaze data using 5 and 4 viewing zones for 5 and 10 second windows. Fig. 6a-d. ROC curves showing the predictive performance of ANNs for discriminating between lane change left and lane change right manoeuvres ranging from 0 to 2 seconds before the beginning of manoeuvre based on gaze data using 5 and 4 viewing zones for 5 and 10 second windows. Fig. 7a-d. ROC curves showing the predictive performance of BNs for discriminating between lane change left and lane keeping manoeuvres ranging from 0 to 2 seconds before the beginning of manoeuvre based on gaze data using 5 and 4 viewing zones for 5 and 10 second windows. Fig. 8a-d. ROC curves showing the predictive performance of BNs for discriminating between lane change right and lane keeping manoeuvres ranging from 0 to 2 seconds before the beginning of manoeuvre based on gaze data using 5 and 4 viewing zones for 5 and 10 second windows. Fig. 9a-d. ROC curves showing the predictive performance of BNs for discriminating between lane change left and lane change right manoeuvres ranging from 0 to 2 seconds before the beginning of manoeuvre based on gaze data using 5 and 4 viewing zones for 5 and 10 second windows. Fig. 10a-d. ROC curves showing the predictive performance of NBCs for discriminating between lane change left and lane keeping manoeuvres ranging from 0 to 2 seconds before the beginning of manoeuvre based on gaze data using 5 and 4 viewing zones for 5 and 10 second windows. Fig. 11a-d. ROC curves showing the predictive performance of NBCs for discriminating between lane change right and lane keeping manoeuvres ranging from 0 to 2 seconds before the beginning of manoeuvre based on gaze data using 5 and 4 viewing zones for 5 and 10 second windows.

51 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Fig. 12a-d. ROC curves showing the predictive performance of NBCs for discriminating between lane change left and lane change right manoeuvres ranging from 0 to 2 seconds before the beginning of manoeuvre based on gaze data using 5 and 4 viewing zones for 5 and 10 second windows. Fig. 13. Overview of results for ANNs, BNs, and NBCs presented as bar charts of d' values in direct comparison pertaining to discrimination performance between lane keeping and lane change left manoeuvres, at increasing time from the manoeuvre onset, across all 5 and 4 viewing zones, either type of road, and all drivers, based on 5 second windows of gaze data. Fig. 14. Overview of results for ANNs, BNs, and NBCs presented as bar charts of d' values in direct comparison pertaining to discrimination performance between lane keeping and lane change right manoeuvres, at increasing time from the manoeuvre onset, across all 5 and 4 viewing zones, either type of road, and all drivers, based on 5 second windows of gaze data. Fig. 15. Overview of results for ANNs, BNs, and NBCs presented as bar charts of d' values in direct comparison pertaining to discrimination performance between lane change left and lane change right manoeuvres, at increasing time from the manoeuvre onset, across all 5 and 4 viewing zones, either type of road, and all drivers, based on 5 second windows of gaze data.

Table captions Table 1. Results of experiments using ANNs to discriminate between lane keeping and lane change left manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D , and threshold value T. Table 2. Results of experiments using ANNs to discriminate between lane keeping and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D , and threshold value T. Table 3. Results of experiments using ANNs to discriminate between lane change left and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D , and threshold value T. Table 4. Results of experiments using ANNs to discriminate between lane keeping and lane change left manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D , and threshold value T.

52 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 5. Results of experiments using ANNs to discriminate between lane keeping and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D , and threshold value T. Table 6. Results of experiments using ANNs to discriminate between lane change left and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D , and threshold value T. Table 7. Results of experiments using BNs to discriminate between lane keeping and lane change left manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Table 8. Results of experiments using BNs to discriminate between lane keeping and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Table 9. Results of experiments using BNs to discriminate between lane change left and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Table 10. Results of experiments using BNs to discriminate between lane keeping and lane change left manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Table 11. Results of experiments using BNs to discriminate between lane keeping and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Table 12. Results of experiments using BNs to discriminate between lane change left and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using

53 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Table 13. Results of experiments using NBCs to discriminate between lane keeping and lane change left manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Table 14. Results of experiments using NBCs to discriminate between lane keeping and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Table 15. Results of experiments using NBCs to discriminate between lane change left and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Table 16. Results of experiments using NBCs to discriminate between lane keeping and lane change left manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Table 17. Results of experiments using NBCs to discriminate between lane keeping and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Table 18. Results of experiments using NBCs to discriminate between lane change left and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D .

54 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 1. Results of experiments using ANNs to discriminate between lane keeping and lane change left manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D , and threshold value T. Lane Change Left 5 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D T

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

5s 0.944 [0.955] 0.063 [0.084] 3.121 [3.069] 0.968 [0.965] -0.029 [-0.155] -0.062 [-0.315] 0.5 [0.5]

4.5s 0.937 [0.934] 0.087 [0.080] 2.887 [2.905] 0.959 [0.960] -0.086 [-0.050] -0.175 [-0.102] 0.4 [0.4]

4s 0.839 [0.878] 0.066 [0.073] 2.493 [2.613] 0.937 [0.946] 0.255 [0.143] 0.458 [0.275] 0.7 [0.6]

3.5s 0.881 [0.899] 0.140 [0.150] 2.261 [2.308] 0.925 [0.928] -0.049 [-0.119] -0.093 [-0.221] 0.3 [0.3]

3s 0.832 [0.832] 0.129 [0.122] 2.092 [2.125] 0.912 [0.915] 0.083 [0.100] 0.151 [0.182] 0.3 [0.4]

2.5s 0.689 [0.731] 0.140 [0.108] 1.573 [1.850] 0.858 [0.887] 0.294 [0.309] 0.470 [0.503] 0.4 [0.3]

2s 0.531 [0.567] 0.129 [0.094] 1.208 [1.481] 0.804 [0.838] 0.525 [0.573] 0.711 [0.760] 0.5 [0.6]

1.5s 0.388 [0.469] 0.094 [0.084] 1.029 [1.300] 0.770 [0.810] 0.799 [0.729] 0.875 [0.850] 0.6 [0.4]

1s 0.287 [0.339] 0.080 [0.070] 0.839 [1.061] 0.735 [0.770] 0.982 [0.945] 0.932 [0.925] 0.6 [0.5]

0.5s 0.059 [0.203] 0.028 [0.063] 0.351 [0.698] 0.640 [0.709] 1.735 [1.181] 0.996 [0.966] 0.8 [0.6]

Table 2. Results of experiments using ANNs to discriminate between lane keeping and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D , and threshold value T. Lane Change Right 5 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D T

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

5s 0.877 [0.894] 0.141 [0.155] 2.235 [2.265] 0.923 [0.925] -0.041 [-0.117] -0.076 [-0.216] 0.3 [0.3]

4.5s 0.771 [0.796] 0.120 [0.127] 1.918 [1.968] 0.896 [0.901] 0.216 [0.157] 0.371 [0.277] 0.4 [0.5]

4s 0.683 [0.690] 0.116 [0.120] 1.670 [1.672] 0.867 [0.868] 0.358 [0.340] 0.558 [0.535] 0.4 [0.4]

3.5s 0.475 [0.500] 0.074 [0.088] 1.385 [1.352] 0.819 [0.818] 0.754 [0.676] 0.865 [0.823] 0.5 [0.5]

3s 0.324 [0.359] 0.063 [0.067] 1.070 [1.138] 0.770 [0.781] 0.991 [0.930] 0.937 [0.922] 0.6 [0.6]

2.5s 0.046 [0.778] 0.004 [0.595] 1.007 [0.525] 0.741 [0.671] 2.191 [-0.503] 0.999 [-0.675] 0.8 [0.4]

2s 0.113 [0.778] 0.060 [0.623] 0.343 [0.451] 0.631 [0.652] 1.384 [-0.540] 0.983 [-0.706] 0.6 [0.4]

1.5s 0.028 [0.813] 0.018 [0.666] 0.197 [0.462] 0.596 [0.655] 2.007 [-0.658] 0.998 [-0.793] 0.7 [0.4]

1s 0.014 [0.461] 0.011 [0.401] 0.110 [0.152] 0.563 [0.557] 2.250 [0.173] 0.999 [0.270] 0.7 [0.5]

0.5s 0.996 [0.940] 0.989 [0.894] 0.389 [0.305] 0.668 [0.620] -2.500 [-1.403] -0.999 [-0.985] 0.2 [0.4]

55 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 3. Results of experiments using ANNs to discriminate between lane change left and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D , and threshold value T. Lane Change Left / Right 5 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D T

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

5s 0.972 [0.975] 0.046 [0.049] 3.595 [3.617] 0.980 [0.980] -0.110 [-0.157] -0.246 [-0.344] 0.6 [0.6]

4.5s 0.965 [0.965] 0.077 [0.063] 3.231 [3.336] 0.970 [0.974] -0.193 [-0.141] -0.394 [-0.299] 0.6 [0.7]

4s 0.954 [0.965] 0.123 [0.113] 2.846 [3.021] 0.954 [0.960] -0.264 [-0.298] -0.491 [-0.553] 0.7 [0.8]

3.5s 0.958 [0.965] 0.229 [0.215] 2.467 [2.599] 0.926 [0.933] -0.491 [-0.509] -0.741 [-0.764] 0.6 [0.6]

3s 0.919 [0.965] 0.324 [0.349] 1.855 [2.198] 0.881 [0.896] -0.470 [-0.710] -0.689 [-0.872] 0.6 [0.5]

2.5s 0.817 [0.880] 0.366 [0.398] 1.245 [1.435] 0.815 [0.837] -0.280 [-0.458] -0.440 [-0.658] 0.6 [0.5]

2s 0.750 [0.820] 0.394 [0.426] 0.942 [1.103] 0.765 [0.791] -0.203 [-0.365] -0.322 [-0.544] 0.6 [0.5]

1.5s 0.870 [0.859] 0.585 [0.553] 0.911 [0.943] 0.753 [0.760] -0.669 [-0.604] -0.807 [-0.765] 0.5 [0.4]

1s 0.916 [0.898] 0.683 [0.658] 0.898 [0.861] 0.746 [0.741] -0.925 [-0.838] -0.917 [-0.888] 0.5 [0.5]

0.5s 0.905 [0.873] 0.792 [0.761] 0.495 [0.433] 0.666 [0.649] -1.062 [-0.924] -0.946 [-0.912] 0.5 [0.5]

56 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 4. Results of experiments using ANNs to discriminate between lane keeping and lane change left manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D , and threshold value T. Lane Change Left 10 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D T Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D T

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

10s 0.937 [0.927] 0.140 [0.136] 2.611 [2.547] 0.944 [0.941] -0.224 [-0.176] -0.415 [-0.331] 0.4 [0.6]

9.5s 0.899 [0.906] 0.136 [0.150] 2.370 [2.349] 0.932 [0.930] -0.088 [-0.139] -0.166 [-0.258] 0.5 [0.6]

9s 0.930 [0.878] 0.227 [0.185] 2.224 [2.058] 0.916 [0.909] -0.364 [-0.133] -0.592 [-0.239] 0.3 [0.5]

8.5s 0.853 [0.895] 0.213 [0.252] 1.845 [1.923] 0.890 [0.894] -0.127 [-0.292] -0.223 [-0.483] 0.5 [0.3]

8s 0.801 [0.871] 0.241 [0.248] 1.546 [1.809] 0.859 [0.885] -0.070 [-0.224] -0.121 [-0.379] 0.5 [0.3]

7.5s 0.769 [0.808] 0.241 [0.255] 1.438 [1.527] 0.845 [0.856] -0.017 [-0.105] -0.029 [-0.180] 0.4 [0.3]

7s 0.703 [0.710] 0.241 [0.231] 1.234 [1.289] 0.816 [0.824] 0.084 [0.091] 0.141 [0.153] 0.4 [0.4]

6.5s 0.654 [0.661] 0.227 [0.234] 1.143 [1.139] 0.801 [0.800] 0.176 [0.155] 0.285 [0.253] 0.4 [0.4]

6s 0.507 [0.584] 0.199 [0.210] 0.861 [1.019] 0.747 [0.778] 0.413 [0.297] 0.592 [0.457] 0.5 [0.5]

5.5s 0.451 [0.511] 0.192 [0.192] 0.746 [0.895] 0.723 [0.754] 0.496 [0.421] 0.672 [0.602] 0.5 [0.5]

-5s

-5.5s

-6s

-6.5s

-7s

-7.5s

-8s

-8.5s

-9s

-9.5s

5s 0.392 [0.448] 0.185 [0.182] 0.620 [0.776] 0.695 [0.729] 0.585 [0.520] 0.744 [0.694] 0.5 [0.5]

4.5s 0.360 [0.395] 0.175 [0.161] 0.577 [0.724] 0.684 [0.718] 0.646 [0.628] 0.786 [0.777] 0.5 [0.5]

4s 0.329 [0.367] 0.164 [0.154] 0.533 [0.680] 0.674 [0.708] 0.710 [0.679] 0.824 [0.809] 0.5 [0.5]

3.5s 0.276 [0.462] 0.147 [0.353] 0.455 [0.280] 0.654 [0.600] 0.822 [0.236] 0.876 [0.362] 0.5 [0.5]

3s 0.241 [0.213] 0.140 [0.094] 0.378 [0.519] 0.634 [0.672] 0.891 [1.054] 0.901 [0.945] 0.5 [0.6]

2.5s 0.196 [0.157] 0.115 [0.080] 0.341 [0.396] 0.625 [0.643] 1.027 [1.203] 0.938 [0.967] 0.5 [0.6]

2s 0.119 [0.143] 0.035 [0.070] 0.631 [0.410] 0.698 [0.647] 1.496 [1.270] 0.990 [0.975] 0.6 [0.7]

1.5s 0.451 [0.147] 0.360 [0.073] 0.234 [0.400] 0.585 [0.644] 0.240 [1.250] 0.367 [0.973] 0.5 [0.7]

1s 0.423 [0.108] 0.388 [0.059] 0.090 [0.324] 0.534 [0.625] 0.239 [1.397] 0.365 [0.984] 0.5 [0.7]

0.5s 0.066 [0.073] 0.014 [0.021] 0.694 [0.583] 0.710 [0.691] 1.850 [1.742] 0.997 [0.996] 0.6 [0.6]

57 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 5. Results of experiments using ANNs to discriminate between lane keeping and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D , and threshold value T. Lane Change Right 10 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D T Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D T

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

10s 0.827 [0.884] 0.190 [0.218] 1.821 [1.972] 0.889 [0.901] -0.033 [-0.208] -0.059 [-0.359] 0.4 [0.3]

9.5s 0.785 [0.799] 0.187 [0.204] 1.680 [1.665] 0.874 [0.873] 0.050 [-0.006] 0.087 [-0.010] 0.4 [0.4]

9s 0.613 [0.683] 0.151 [0.187] 1.316 [1.366] 0.824 [0.834] 0.372 [0.207] 0.559 [0.338] 0.5 [0.5]

8.5s 0.493 [0.542] 0.144 [0.169] 1.043 [1.064] 0.778 [0.784] 0.539 [0.425] 0.718 [0.611] 0.5 [0.5]

8s 0.588 [0.419] 0.366 [0.158] 0.564 [0.796] 0.681 [0.732] 0.059 [0.602] 0.096 [0.760] 0.5 [0.5]

7.5s 0.592 [0.405] 0.447 [0.225] 0.364 [0.513] 0.626 [0.668] -0.049 [0.497] -0.078 [0.669] 0.5 [0.5]

7s 0.616 [0.363] 0.486 [0.218] 0.331 [0.426] 0.616 [0.645] -0.130 [0.564] -0.205 [0.725] 0.5 [0.5]

6.5s 0.637 [0.335] 0.535 [0.218] 0.262 [0.350] 0.594 [0.624] -0.219 [0.602] -0.338 [0.753] 0.5 [0.5]

6s 0.623 [0.324] 0.560 [0.229] 0.163 [0.285] 0.561 [0.604] -0.232 [0.599] -0.355 [0.750] 0.5 [0.5]

5.5s 0.637 [0.320] 0.539 [0.239] 0.253 [0.241] 0.592 [0.589] -0.224 [0.587] -0.344 [0.741] 0.5 [0.5]

-5s

-5.5s

-6s

-6.5s

-7s

-7.5s

-8s

-8.5s

-9s

-9.5s

5s 0.592 [0.310] 0.532 [0.250] 0.151 [0.178] 0.557 [0.568] -0.155 [0.585] -0.243 [0.739] 0.5 [0.5]

4.5s 0.560 [0.479] 0.556 [0.398] 0.107 [0.205] 0.541 [0.575] -0.195 [0.156] -0.303 [0.244] 0.5 [0.5]

4s 0.658 [0.518] 0.585 [0.468] 0.194 [0.123] 0.572 [0.546] -0.310 [0.017] -0.461 [0.028] 0.5 [0.5]

3.5s 0.651 [0.532] 0.592 [0.461] 0.157 [0.177] 0.559 [0.565] -0.310 [0.008] -0.460 [0.014] 0.5 [0.5]

3s 0.655 [0.578] 0.602 [0.521] 0.139 [0.142] 0.553 [0.553] -0.328 [-0.124] -0.483 [-0.195] 0.5 [0.5]

2.5s 0.715 [0.620] 0.630 [0.567] 0.234 [0.136] 0.586 [0.551] -0.450 [-0.236] -0.620 [-0.361] 0.5 [0.5]

2s 0.715 [0.637] 0.620 [0.599] 0.262 [0.101] 0.595 [0.539] -0.436 [-0.300] -0.606 [-0.447] 0.5 [0.5]

1.5s 0.778 [0.754] 0.648 [0.666] 0.386 [0.258] 0.634 [0.594] -0.572 [-0.556] -0.731 [-0.717] 0.5 [0.5]

1s 0.796 [0.799] 0.683 [0.718] 0.350 [0.261] 0.624 [0.597] -0.651 [-0.708] -0.787 [-0.820] 0.5 [0.5]

0.5s 0.975 [0.852] 0.961 [0.799] 0.200 [0.206] 0.594 [0.581] -1.865 [-0.942] -0.997 [-0.916] 0.4 [0.5]

58 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 6. Results of experiments using ANNs to discriminate between lane change left and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D , and threshold value T. Lane Change Left / Right 10 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D T Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D T

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

10s 0.958 [0.937] 0.074 [0.049] 3.172 [3.178] 0.969 [0.970] -0.139 [0.062] -0.288 [0.132] 0.4 [0.6]

9.5s 0.933 [0.916] 0.099 [0.063] 2.788 [2.902] 0.955 [0.960] -0.104 [0.075] -0.208 [0.154] 0.4 [0.7]

9s 0.937 [0.905] 0.162 [0.099] 2.513 [2.599] 0.937 [0.946] -0.27 [-0.010] -0.481 [-0.020] 0.3 [0.8]

8.5s 0.916 [0.905] 0.218 [0.211] 2.153 [2.112] 0.913 [0.911] -0.298 [-0.254] -0.503 [-0.436] 0.5 [0.5]

8s 0.894 [0.905] 0.317 [0.310] 1.726 [1.806] 0.872 [0.879] -0.386 [-0.406] -0.594 [-0.620] 0.5 [0.4]

7.5s 0.810 [0.905] 0.342 [0.423] 1.285 [1.505] 0.822 [0.842] -0.234 [-0.557] -0.376 [-0.748] 0.5 [0.4]

7s 0.789 [0.764] 0.391 [0.345] 1.079 [1.118] 0.789 [0.797] -0.262 [-0.160] -0.410 [-0.261] 0.5 [0.5]

6.5s 0.775 [0.761] 0.405 [0.391] 0.994 [0.985] 0.774 [0.773] -0.256 [-0.215] -0.401 [-0.341] 0.5 [0.5]

6s 0.778 [0.771] 0.482 [0.440] 0.810 [0.893] 0.737 [0.755] -0.360 [-0.295] -0.531 [-0.451] 0.5 [0.5]

5.5s 0.792 [0.866] 0.493 [0.563] 0.832 [0.948] 0.742 [0.760] -0.398 [-0.634] -0.575 [-0.786] 0.5 [0.4]

-5s

-5.5s

-6s

-6.5s

-7s

-7.5s

-8s

-8.5s

-9s

-9.5s

5s 0.806 [0.813] 0.518 [0.539] 0.820 [0.793] 0.739 [0.733] -0.454 [-0.493] -0.634 [-0.671] 0.5 [0.5]

4.5s 0.820 [0.838] 0.553 [0.609] 0.784 [0.709] 0.731 [0.714] -0.524 [-0.631] -0.699 [-0.779] 0.5 [0.5]

4s 0.799 [0.842] 0.599 [0.627] 0.589 [0.677] 0.687 [0.707] -0.544 [-0.662] -0.711 [-0.798] 0.5 [0.5]

3.5s 0.930 [0.870] 0.785 [0.739] 0.682 [0.483] 0.706 [0.662] -1.131 [-0.883] -0.959 [-0.899] 0.4 [0.4]

3s 0.750 [0.880] 0.637 [0.761] 0.323 [0.468] 0.615 [0.659] -0.512 [-0.942] -0.681 [-0.917] 0.5 [0.4]

2.5s 0.947 [0.905] 0.849 [0.859] 0.587 [0.233] 0.688 [0.593] -1.324 [-1.193] -0.980 [-0.966] 0.4 [0.4]

2s 0.782 [0.901] 0.666 [0.813] 0.350 [0.399] 0.624 [0.642] -0.602 [-1.090] -0.753 [-0.951] 0.5 [0.4]

1.5s 0.796 [0.908] 0.690 [0.806] 0.330 [0.466] 0.618 [0.659] -0.661 [-1.097] -0.793 [-0.952] 0.5 [0.4]

1s 0.813 [0.919] 0.725 [0.852] 0.291 [0.352] 0.607 [0.631] -0.744 [-1.222] -0.840 [-0.969] 0.5 [0.4]

0.5s 0.940 [0.870] 0.849 [0.820] 0.525 [0.208] 0.675 [0.582] -1.293 [-1.021] -0.977 [-0.936] 0.4 [0.5]

59 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 7. Results of experiments using BNs to discriminate between lane keeping and lane change left manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Left 5 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

5s 0.932 [0.925] 0.068 [0.075] 2.981 [2.879] 0.963 [0.959] 0 [0] 0 [0]

4.5s 0.906 [0.906] 0.094 [0.094] 2.633 [2.633] 0.948 [0.948] 0 [0] 0 [0]

4s 0.904 [0.890] 0.096 [0.110] 2.609 [2.453] 0.946 [0.938] 0 [0] 0 [0]

3.5s 0.871 [0.860] 0.129 [0.140] 2.262 [2.160] 0.925 [0.918] 0 [0] 0 [0]

3s 0.844 [0.851] 0.156 [0.149] 2.022 [2.081] 0.907 [0.912] 0 [0] 0 [0]

2.5s 0.790 [0.811] 0.210 [0.189] 1.612 [1.763] 0.867 [0.883] 0 [0] 0 [0]

2s 0.729 [0.743] 0.271 [0.257] 1.219 [1.305] 0.814 [0.827] 0 [0] 0 [0]

1.5s 0.622 [0.692] 0.378 [0.308] 0.621 [1.003] 0.696 [0.777] 0 [0] 0 [0]

1s 0.612 [0.636] 0.388 [0.364] 0.569 [0.695] 0.683 [0.713] 0 [0] 0 [0]

0.5s 0.488 [0.495] 0.512 [0.505] -0.060 [-0.025] 0.476 [0.490] 0 [0] 0 [0]

Table 8. Results of experiments using BNs to discriminate between lane keeping and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Right 5 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

5s 0.868 [0.870] 0.132 [0.130] 2.233 [2.252] 0.923 [0.925] 0 [0] 0 [0]

4.5s 0.808 [0.829] 0.192 [0.171] 1.741 [1.900] 0.881 [0.896] 0 [0] 0 [0]

4s 0.782 [0.778] 0.218 [0.222] 1.557 [1.530] 0.860 [0.857] 0 [0] 0 [0]

3.5s 0.706 [0.701] 0.294 [0.299] 1.083 [1.054] 0.791 [0.786] 0 [0] 0 [0]

3s 0.613 [0.606] 0.387 [0.394] 0.574 [0.537] 0.684 [0.674] 0 [0] 0 [0]

2.5s 0.581 [0.555] 0.419 [0.445] 0.409 [0.276] 0.639 [0.599] 0 [0] 0 [0]

2s 0.493 [0.489] 0.507 [0.511] -0.035 [-0.055] 0.486 [0.478] 0 [0] 0 [0]

1.5s 0.493 [0.488] 0.507 [0.512] -0.035 [-0.060] 0.486 [0.476] 0 [0] 0 [0]

1s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

0.5s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

60 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 9. Results of experiments using BNs to discriminate between lane change left and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Left / Right 5 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

5s 0.956 [0.956] 0.044 [0.044] 3.412 [3.412] 0.976 [0.976] 0 [0] 0 [0]

4.5s 0.952 [0.945] 0.048 [0.055] 3.329 [3.196] 0.974 [0.970] 0 [0] 0 [0]

4s 0.923 [0.924] 0.077 [0.076] 2.851 [2.865] 0.958 [0.958] 0 [0] 0 [0]

3.5s 0.875 [0.873] 0.125 [0.127] 2.300 [2.281] 0.928 [0.927] 0 [0] 0 [0]

3s 0.782 [0.810] 0.218 [0.190] 1.557 [1.755] 0.860 [0.882] 0 [0] 0 [0]

2.5s 0.722 [0.738] 0.278 [0.262] 1.177 [1.274] 0.807 [0.822] 0 [0] 0 [0]

2s 0.660 [0.664] 0.340 [0.336] 0.824 [0.846] 0.742 [0.746] 0 [0] 0 [0]

1.5s 0.630 [0.600] 0.370 [0.400] 0.663 [0.506] 0.706 [0.666] 0 [0] 0 [0]

1s 0.602 [0.577] 0.398 [0.423] 0.517 [0.388] 0.669 [0.633] 0 [0] 0 [0]

0.5s 0.555 [0.549] 0.445 [0.451] 0.276 [0.246] 0.599 [0.589] 0 [0] 0 [0]

61 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 10. Results of experiments using BNs to discriminate between lane keeping and lane change left manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Left 10 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

10s 0.881 [0.878] 0.119 [0.122] 2.360 [2.330] 0.932 [0.930] 0 [0] 0 [0]

9.5s 0.879 [0.878] 0.121 [0.122] 2.340 [2.330] 0.931 [0.930] 0 [0] 0 [0]

9s 0.846 [0.830] 0.154 [0.170] 2.038 [1.908] 0.908 [0.897] 0 [0] 0 [0]

8.5s 0.808 [0.820] 0.192 [0.180] 1.741 [1.830] 0.881 [0.890] 0 [0] 0 [0]

8s 0.771 [0.809] 0.229 [0.191] 1.484 [1.748] 0.851 [0.881] 0 [0] 0 [0]

7.5s 0.747 [0.780] 0.253 [0.220] 1.330 [1.544] 0.830 [0.858] 0 [0] 0 [0]

7s 0.731 [0.740] 0.269 [0.260] 1.231 [1.286] 0.816 [0.824] 0 [0] 0 [0]

6.5s 0.698 [0.705] 0.302 [0.295] 1.037 [1.077] 0.783 [0.790] 0 [0] 0 [0]

6s 0.675 [0.675] 0.325 [0.325] 0.907 [0.907] 0.759 [0.759] 0 [0] 0 [0]

5.5s 0.586 [0.635] 0.414 [0.365] 0.434 [0.690] 0.646 [0.712] 0 [0] 0 [0]

-5s

-5.5s

-6s

-6.5s

-7s

-7.5s

-8s

-8.5s

-9s

-9.5s

5s 0.586 [0.573] 0.414 [0.427] 0.434 [0.368] 0.646 [0.627] 0 [0] 0 [0]

4.5s 0.566 [0.568] 0.434 [0.432] 0.332 [0.342] 0.616 [0.619] 0 [0] 0 [0]

4s 0.566 [0.566] 0.434 [0.434] 0.332 [0.332] 0.616 [0.616] 0 [0] 0 [0]

3.5s 0.493 [0.503] 0.507 [0.497] -0.035 [0.015] 0.486 [0.505] 0 [0] 0 [0]

3s 0.493 [0.516] 0.507 [0.484] -0.035 [0.080] 0.486 [0.531] 0 [0] 0 [0]

2.5s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

2s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

1.5s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

1s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

0.5s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

62 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 11. Results of experiments using BNs to discriminate between lane keeping and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Right 10 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

10s 0.789 [0.820] 0.211 [0.180] 1.605 [1.830] 0.866 [0.890] 0 [0] 0 [0]

9.5s 0.796 [0.790] 0.204 [0.210] 1.654 [1.612] 0.871 [0.867] 0 [0] 0 [0]

9s 0.762 [0.748] 0.238 [0.252] 1.425 [1.336] 0.843 [0.831] 0 [0] 0 [0]

8.5s 0.690 [0.687] 0.310 [0.313] 0.991 [0.974] 0.775 [0.772] 0 [0] 0 [0]

8s 0.629 [0.595] 0.371 [0.405] 0.658 [0.480] 0.705 [0.659] 0 [0] 0 [0]

7.5s 0.560 [0.570] 0.440 [0.430] 0.302 [0.352] 0.607 [0.622] 0 [0] 0 [0]

7s 0.498 [0.539] 0.502 [0.461] -0.010 [0.196] 0.496 [0.572] 0 [0] 0 [0]

6.5s 0.502 [0.489] 0.498 [0.511] 0.010 [-0.055] 0.503 [0.478] 0 [0] 0 [0]

6s 0.500 [0.493] 0.500 [0.507] 0 [-0.035] 0.500 [0.486] 0 [0] 0 [0]

5.5s 0.502 [0.493] 0.498 [0.507] 0.010 [-0.035] 0.503 [0.486] 0 [0] 0 [0]

-5s

-5.5s

-6s

-6.5s

-7s

-7.5s

-8s

-8.5s

-9s

-9.5s

5s 0.495 [0.493] 0.505 [0.507] -0.025 [-0.035] 0.490 [0.486] 0 [0] 0 [0]

4.5s 0.495 [0.493] 0.505 [0.507] -0.025 [-0.035] 0.490 [0.486] 0 [0] 0 [0]

4s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

3.5s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

3s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

2.5s 0.495 [0.493] 0.505 [0.507] -0.025 [-0.035] 0.490 [0.486] 0 [0] 0 [0]

2s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

1.5s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

1s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

0.5s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

63 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 12. Results of experiments using BNs to discriminate between lane change left and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Left / Right 10 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

10s 0.951 [0.928] 0.049 [0.072] 3.309 [2.922] 0.974 [0.961] 0 [0] 0 [0]

9.5s 0.930 [0.926] 0.070 [0.074] 2.951 [2.893] 0.962 [0.960] 0 [0] 0 [0]

9s 0.894 [0.903] 0.106 [0.097] 2.496 [2.597] 0.940 [0.946] 0 [0] 0 [0]

8.5s 0.836 [0.859] 0.164 [0.141] 1.956 [2.151] 0.901 [0.917] 0 [0] 0 [0]

8s 0.778 [0.813] 0.222 [0.187] 1.530 [1.778] 0.857 [0.884] 0 [0] 0 [0]

7.5s 0.724 [0.718] 0.276 [0.282] 1.189 [1.153] 0.809 [0.803] 0 [0] 0 [0]

7s 0.674 [0.704] 0.326 [0.296] 0.902 [1.071] 0.758 [0.789] 0 [0] 0 [0]

6.5s 0.671 [0.678] 0.329 [0.322] 0.885 [0.924] 0.754 [0.762] 0 [0] 0 [0]

6s 0.667 [0.660] 0.333 [0.340] 0.863 [0.824] 0.750 [0.742] 0 [0] 0 [0]

5.5s 0.644 [0.636] 0.356 [0.364] 0.738 [0.695] 0.723 [0.713] 0 [0] 0 [0]

-5s

-5.5s

-6s

-6.5s

-7s

-7.5s

-8s

-8.5s

-9s

-9.5s

5s 0.621 [0.623] 0.379 [0.377] 0.616 [0.626] 0.694 [0.697] 0 [0] 0 [0]

4.5s 0.609 [0.548] 0.391 [0.452] 0.553 [0.241] 0.678 [0.587] 0 [0] 0 [0]

4s 0.551 [0.502] 0.449 [0.498] 0.256 [0.010] 0.592 [0.503] 0 [0] 0 [0]

3.5s 0.495 [0.493] 0.505 [0.507] -0.025 [-0.035] 0.490 [0.486] 0 [0] 0 [0]

3s 0.491 [0.493] 0.509 [0.507] -0.045 [-0.035] 0.482 [0.486] 0 [0] 0 [0]

2.5s 0.502 [0.493] 0.498 [0.507] 0.010 [-0.035] 0.503 [0.486] 0 [0] 0 [0]

2s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

1.5s 0.491 [0.493] 0.509 [0.507] -0.045 [-0.035] 0.482 [0.486] 0 [0] 0 [0]

1s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

0.5s 0.493 [0.493] 0.507 [0.507] -0.035 [-0.035] 0.486 [0.486] 0 [0] 0 [0]

64 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 13. Results of experiments using NBCs to discriminate between lane keeping and lane change left manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Left 5 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

5s 0.906 [0.927] 0.094 [0.073] 2.633 [2.907] 0.948 [0.960] 0 [0] 0 [0]

4.5s 0.881 [0.885] 0.119 [0.115] 2.360 [2.400] 0.932 [0.935] 0 [0] 0 [0]

4s 0.855 [0.874] 0.145 [0.126] 2.116 [2.291] 0.915 [0.927] 0 [0] 0 [0]

3.5s 0.823 [0.867] 0.177 [0.133] 1.853 [2.224] 0.892 [0.923] 0 [0] 0 [0]

3s 0.783 [0.844] 0.217 [0.156] 1.564 [2.022] 0.861 [0.907] 0 [0] 0 [0]

2.5s 0.729 [0.802] 0.271 [0.198] 1.219 [1.697] 0.814 [0.876] 0 [0] 0 [0]

2s 0.678 [0.733] 0.322 [0.267] 0.924 [1.243] 0.762 [0.817] 0 [0] 0 [0]

1.5s 0.640 [0.684] 0.360 [0.316] 0.717 [0.957] 0.718 [0.769] 0 [0] 0 [0]

1s 0.600 [0.631] 0.400 [0.369] 0.506 [0.669] 0.666 [0.707] 0 [0] 0 [0]

0.5s 0.551 [0.570] 0.449 [0.430] 0.256 [0.352] 0.592 [0.622] 0 [0] 0 [0]

Table 14. Results of experiments using NBCs to discriminate between lane keeping and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Right 5 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

5s 0.831 [0.863] 0.169 [0.137] 1.916 [2.187] 0.898 [0.920] 0 [0] 0 [0]

4.5s 0.776 [0.812] 0.224 [0.188] 1.517 [1.770] 0.855 [0.884] 0 [0] 0 [0]

4s 0.710 [0.761] 0.290 [0.239] 1.106 [1.419] 0.795 [0.842] 0 [0] 0 [0]

3.5s 0.650 [0.683] 0.350 [0.317] 0.770 [0.952] 0.730 [0.767] 0 [0] 0 [0]

3s 0.616 [0.634] 0.384 [0.366] 0.590 [0.685] 0.688 [0.711] 0 [0] 0 [0]

2.5s 0.551 [0.565] 0.449 [0.435] 0.256 [0.327] 0.592 [0.615] 0 [0] 0 [0]

2s 0.542 [0.546] 0.458 [0.454] 0.211 [0.231] 0.577 [0.584] 0 [0] 0 [0]

1.5s 0.544 [0.572] 0.456 [0.428] 0.221 [0.363] 0.580 [0.625] 0 [0] 0 [0]

1s 0.514 [0.542] 0.486 [0.458] 0.070 [0.211] 0.527 [0.577] 0 [0] 0 [0]

0.5s 0.523 [0.537] 0.477 [0.463] 0.115 [0.186] 0.543 [0.568] 0 [0] 0 [0]

65 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 15. Results of experiments using NBCs to discriminate between lane change left and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 5 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Left / Right 5 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

5s 0.965 [0.956] 0.035 [0.044] 3.623 [3.412] 0.981 [0.976] 0 [0] 0 [0]

4.5s 0.947 [0.898] 0.053 [0.102] 3.232 [2.540] 0.972 [x] 0 [0] 0 [0]

4s 0.914 [0.840] 0.086 [0.160] 2.731 [1.988] 0.952 [0.904] 0 [0] 0 [0]

3.5s 0.829 [0.674] 0.171 [0.326] 1.900 [0.902] 0.896 [0.758] 0 [0] 0 [0]

3s 0.783 [0.607] 0.217 [0.393] 1.564 [0.543] 0.861 [0.676] 0 [0] 0 [0]

2.5s 0.715 [0.729] 0.285 [0.271] 1.136 [1.219] 0.800 [0.814] 0 [0] 0 [0]

2s 0.653 [0.687] 0.347 [0.313] 0.786 [0.974] 0.734 [0.772] 0 [0] 0 [0]

1.5s 0.625 [0.644] 0.375 [0.356] 0.637 [0.738] 0.700 [0.723] 0 [0] 0 [0]

1s 0.579 [0.618] 0.421 [0.382] 0.398 [0.600] 0.636 [0.690] 0 [0] 0 [0]

0.5s 0.560 [0.551] 0.440 [0.449] 0.302 [0.256] 0.607 [0.592] 0 [0] 0 [0]

66 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 16. Results of experiments using NBCs to discriminate between lane keeping and lane change left manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Left 10 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

10s 0.865 [0.872] 0.135 [0.128] 2.206 [2.271] 0.921 [0.926] 0 [0] 0 [0]

9.5s 0.851 [0.836] 0.149 [0.164] 2.081 [1.956] 0.912 [0.901] 0 [0] 0 [0]

9s 0.806 [0.795] 0.194 [0.205] 1.726 [1.647] 0.879 [0.871] 0 [0] 0 [0]

8.5s 0.787 [0.762] 0.213 [0.238] 1.592 [1.425] 0.864 [0.843] 0 [0] 0 [0]

8s 0.757 [0.741] 0.243 [0.259] 1.393 [1.292] 0.839 [0.825] 0 [0] 0 [0]

7.5s 0.715 [0.757] 0.285 [0.243] 1.136 [1.393] 0.800 [0.839] 0 [0] 0 [0]

7s 0.687 [0.713] 0.313 [0.287] 0.974 [1.124] 0.772 [0.798] 0 [0] 0 [0]

6.5s 0.652 [0.671] 0.348 [0.329] 0.781 [0.885] 0.733 [0.754] 0 [0] 0 [0]

6s 0.619 [0.659] 0.381 [0.341] 0.605 [0.819] 0.692 [0.741] 0 [0] 0 [0]

5.5s 0.601 [0.640] 0.399 [0.360] 0.512 [0.717] 0.668 [0.718] 0 [0] 0 [0]

-5s

-5.5s

-6s

-6.5s

-7s

-7.5s

-8s

-8.5s

-9s

-9.5s

5s 0.591 [0.633] 0.409 [0.367] 0.460 [0.679] 0.653 [0.710] 0 [0] 0 [0]

4.5s 0.568 [0.615] 0.432 [0.385] 0.342 [0.584] 0.619 [0.686] 0 [0] 0 [0]

4s 0.559 [0.605] 0.441 [0.395] 0.297 [0.532] 0.605 [0.673] 0 [0] 0 [0]

3.5s 0.552 [0.584] 0.448 [0.416] 0.261 [0.424] 0.594 [0.643] 0 [0] 0 [0]

3s 0.537 [0.582] 0.463 [0.418] 0.186 [0.414] 0.568 [0.640] 0 [0] 0 [0]

2.5s 0.533 [0.552] 0.467 [0.448] 0.165 [0.261] 0.561 [0.594] 0 [0] 0 [0]

2s 0.535 [0.533] 0.465 [0.467] 0.175 [0.165] 0.565 [0.561] 0 [0] 0 [0]

1.5s 0.533 [0.554] 0.467 [0.446] 0.165 [0.271] 0.561 [0.597] 0 [0] 0 [0]

1s 0.526 [0.542] 0.474 [0.458] 0.130 [0.211] 0.549 [0.577] 0 [0] 0 [0]

0.5s 0.505 [0.497] 0.495 [0.503] 0.025 [-0.015] 0.509 [0.494] 0 [0] 0 [0]

67 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 17. Results of experiments using NBCs to discriminate between lane keeping and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Right 10 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

10s 0.787 [0.829] 0.213 [0.171] 1.592 [1.900] 0.864 [0.896] 0 [0] 0 [0]

9.5s 0.739 [0.775] 0.261 [0.225] 1.280 [1.510] 0.823 [0.854] 0 [0] 0 [0]

9s 0.690 [0.704] 0.310 [0.296] 0.991 [1.071] 0.775 [0.789] 0 [0] 0 [0]

8.5s 0.643 [0.643] 0.357 [0.357] 0.733 [0.733] 0.722 [0.722] 0 [0] 0 [0]

8s 0.563 [0.604] 0.437 [0.396] 0.317 [0.527] 0.611 [0.672] 0 [0] 0 [0]

7.5s 0.569 [0.583] 0.431 [0.417] 0.347 [0.419] 0.621 [0.642] 0 [0] 0 [0]

7s 0.563 [0.562] 0.437 [0.438] 0.317 [0.312] 0.611 [0.610] 0 [0] 0 [0]

6.5s 0.560 [0.555] 0.440 [0.445] 0.302 [0.276] 0.607 [0.599] 0 [0] 0 [0]

6s 0.555 [0.542] 0.445 [0.458] 0.276 [0.211] 0.599 [0.577] 0 [0] 0 [0]

5.5s 0.546 [0.526] 0.454 [0.474] 0.231 [0.130] 0.584 [0.549] 0 [0] 0 [0]

-5s

-5.5s

-6s

-6.5s

-7s

-7.5s

-8s

-8.5s

-9s

-9.5s

5s 0.556 [0.521] 0.444 [0.479] 0.281 [0.105] 0.600 [0.540] 0 [0] 0 [0]

4.5s 0.540 [0.516] 0.460 [0.484] 0.201 [0.080] 0.574 [0.531] 0 [0] 0 [0]

4s 0.546 [0.514] 0.454 [0.486] 0.231 [0.070] 0.584 [0.527] 0 [0] 0 [0]

3.5s 0.526 [0.518] 0.474 [0.482] 0.130 [0.090] 0.549 [0.534] 0 [0] 0 [0]

3s 0.528 [0.519] 0.472 [0.481] 0.140 [0.095] 0.553 [0.536] 0 [0] 0 [0]

2.5s 0.544 [0.512] 0.456 [0.488] 0.221 [0.060] 0.580 [0.523] 0 [0] 0 [0]

2s 0.551 [0.540] 0.449 [0.460] 0.256 [0.201] 0.592 [0.574] 0 [0] 0 [0]

1.5s 0.533 [0.553] 0.467 [0.447] 0.165 [0.266] 0.561 [0.595] 0 [0] 0 [0]

1s 0.537 [0.551] 0.463 [0.449] 0.186 [0.256] 0.568 [0.592] 0 [0] 0 [0]

0.5s 0.528 [0.540] 0.472 [0.460] 0.140 [0.201] 0.553 [0.574] 0 [0] 0 [0]

68 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Table 18. Results of experiments using NBCs to discriminate between lane change left and lane change right manoeuvres at increasing times before the beginning of a manoeuvre using an encoding scheme which represents data from a window of 10 seconds of gaze behaviour denoting all five viewing zones [four viewing zones, i.e., without the windscreen] with data drawn from trials on either type of road and all drivers who took part in the trials where sensitivity is denoted d’ and A’, bias C and B"D . Lane Change Left / Right 10 seconds Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D Time before beginning of manoeuvre Size of data sample True Positive Rate False Positive Rate d' A’ C B"D

-0s

-0.5s

-1s

-1.5s

-2s

-2.5s

-3s

-3.5s

-4s

-4.5s

10s 0.935 [0.935] 0.065 [0.065] 3.028 [3.028] 0.965 [0.965] 0 [0] 0 [0]

9.5s 0.912 [0.894] 0.088 [0.106] 2.706 [2.496] 0.951 [0.940] 0 [0] 0 [0]

9s 0.873 [0.843] 0.127 [0.157] 2.281 [2.013] 0.927 [0.906] 0 [0] 0 [0]

8.5s 0.831 [0.718] 0.169 [0.282] 1.916 [1.153] 0.898 [0.803] 0 [0] 0 [0]

8s 0.769 [0.687] 0.231 [0.313] 1.471 [0.974] 0.849 [0.772] 0 [0] 0 [0]

7.5s 0.722 [0.662] 0.278 [0.338] 1.177 [0.835] 0.807 [0.744] 0 [0] 0 [0]

7s 0.683 [0.671] 0.317 [0.329] 0.952 [0.885] 0.767 [0.754] 0 [0] 0 [0]

6.5s 0.664 [0.655] 0.336 [0.345] 0.846 [0.797] 0.746 [0.736] 0 [0] 0 [0]

6s 0.646 [0.646] 0.354 [0.354] 0.749 [0.749] 0.726 [0.726] 0 [0] 0 [0]

5.5s 0.629 [0.643] 0.371 [0.357] 0.658 [0.733] 0.705 [0.722] 0 [0] 0 [0]

-5s

-5.5s

-6s

-6.5s

-7s

-7.5s

-8s

-8.5s

-9s

-9.5s

5s 0.618 [0.627] 0.382 [0.373] 0.600 [0.647] 0.690 [0.702] 0 [0] 0 [0]

4.5s 0.613 [0.592] 0.387 [0.408] 0.574 [0.465] 0.684 [0.655] 0 [0] 0 [0]

4s 0.595 [0.572] 0.405 [0.428] 0.480 [0.363] 0.659 [0.625] 0 [0] 0 [0]

3.5s 0.574 [0.560] 0.426 [0.440] 0.373 [0.302] 0.628 [0.607] 0 [0] 0 [0]

3s 0.563 [0.537] 0.437 [0.463] 0.317 [0.186] 0.611 [0.568] 0 [0] 0 [0]

2.5s 0.546 [0.533] 0.454 [0.467] 0.231 [0.165] 0.584 [0.561] 0 [0] 0 [0]

2s 0.555 [0.549] 0.445 [0.451] 0.276 [0.246] 0.599 [0.589] 0 [0] 0 [0]

1.5s 0.549 [0.551] 0.451 [0.449] 0.246 [0.256] 0.589 [0.592] 0 [0] 0 [0]

1s 0.546 [0.526] 0.454 [0.474 ] 0.231 [0.130] 0.584 [0.549] 0 [0] 0 [0]

0.5s 0.533 [0.551] 0.467 [0.449] 0.165 [0.256] 0.561 [0.592] 0 [0] 0 [0]

69 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Authors’ Vitae

Firas Lethaus received his MSc degree in Psychology from the Braunschweig University of Technology, Germany, in 2001, and his MSc degree in Computer Science from the University of Birmingham, UK, in 2004. From 2001 to 2002, he worked at the Behavioural Brain Sciences Centre, University of Birmingham, UK. Since 2005, he has been a research scientist and project manager at the Institute of Transportation Systems, German Aerospace Center (DLR). His main interests are in the fields of driver modelling, machine learning, eye tracking, and human factors. He is a member of IEEE, the Human Factors and Ergonomics Society - Europe Chapter (HFES-EC), and the German Psychological Society (DGPs).

Martin Baumann studied Psychology at the University of Regensburg and received his PhD at the Chemnitz University of Technology in 2001. There he worked as research assistant until 2006. From 2006 to 2007 he had a post-doc position at the Federal Highway Research Institute (BASt) funded by the Humanist Network of Excellence. Since 2008, he works at the Institute of Transportation Systems at German Aerospace Center (DLR) leading the team „Driver Cognition and Modelling“. His main research interests are the causes and effects of driver distraction, situation awareness, cognitive driver models, and the impact of ADAS and IVIS on driver behaviour.

Frank Köster holds a diploma in Computing Sciences (subsidiary subject Psychology). He finished his PhD thesis in 2001 and finished his advanced doctoral dissertation (Habilitationsschrift) in 2007 at the University of Oldenburg. Currently, he is employed at the Institute of Transportation Systems at the German Aerospace Center (DLR). He is Head of the Institute’s department of Automotive Systems. In addition, Frank Köster gives lectures at the University of Osnabrück and at the Carl von Ossietzky University of Oldenburg.

Karsten Lemmer is Director of the Institute of Transportation Systems at the German Aerospace Center (DLR) and Professor at the Faculty of Mechanical Engineering at the Braunschweig University of Technology, Germany.

70 Lethaus et al. – A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data