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Assessing driver status: A demonstration experiment on the road
Accid. Printed
& Prev. in Great Britain.
23, No.
pp. 297-307.
ocnl-4575/91 f3.OO + .w 0 1991 Pergamon Press plc
1991
ASSESSING DRIVER STATUS: A DEMONSTRATION EXPERIMENT ON THE ROAD DICK DE WAARD AND KAREL A. BROOKHUIS Traffic Research Centre, University of Groningen, P.O. Box 69, 9750 AB Haren (Gn), The Netherlands (Received 22 February 1990) Abstract-Twenty subjects completed an on-the-road experiment that consisted of two parts on two separate days. One was a one-hour driving test under the influence of alcohol (BAC < = 0.05%). the second a two-and-a-half-hour driving test under vigilance conditions. Impairment of driving performance was measured in a car-following test as well as in a standard driving test. Changes in relevant physiological parameters, such as ECG and EEG, reflected changes in driver status and predicted driving performance impairment.
INTRODUCTION
In 1988 the Commission of the European Communities formally adopted DRIVE (Dedicated Road Infrastructure for Vehicle Safety in Europe) as a European Community research programme. DRIVE encompasses research and development in the field of road transport informatics and telecommunication. Part of it is the development of an intelligent electronic co-driver, a computer system integrated in the car of the very near future. DRIVE envisages a complete general European road transport environment in which individual drivers are better informed and monitored, in which “intelligent” vehicles communicate and cooperate with each other, the road users, and the road infrastructure itself. One of DRIVE’s primary objectives is improvement of road safety. The ultimate goal of one of the present DRIVE projects, V1004 or DRIVER STATUS MONITORING, is to develop a device with the acronym DREAM (Driver Related Evaluation And Monitoring), whose sole assignment is to monitor driver status continuously through vehicle parameters. This paper is a report of an initial experiment to determine the feasibility of such a device. A decrement in driver’s performance may result from different sources. First, degradations in personal well-being might influence performance, for instance by illness or sleep deprivation. External factors, such as alcohol or drug intake, can certainly be responsible for vast performance deterioration (Seppala et al. 1979; Smiley and Brookhuis 1987). Alcohol alone is perhaps the prime malefactor of all traffic accidents. Another important factor, resulting in, for instance, falling-asleep accidents, is driver underload. Underload is a time-related factor, leading to reduced arousal and to inattention. Inattention is found to be a factor of major importance in accident causation (Smiley and Smith 1985). Finally, driver overload is a factor of concern in the field of accident causation. For instance, approaching and crossing intersections requires sufficient lookout, but also paying attention to what one is looking at, and thus the ability for divided attention (Smiley and Brookhuis 1987). Ideally, a driver acts on the top of a hypothetical inverted-U curve of performance (Wiener et al. 1984). Likewise ideally, an electronic driver monitoring system detects future serious deviations from that position on the curve. However, changes in driver status (i.e. physiological changes), which are the precursors of such (behavioural) deviations to come, cannot be measured by an electronic in-vehicle device that will be widely used, but only in experimental vehicles under special conditions. So, the natural restriction for this research project is demonstrating the feasibility of detection of early deviations from the ideal curve position by behaviourial measures that are simply measured by an electronic monitoring device.
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Alcohol and drugs It has been established
by means of epidemiological studies that alcohol and, probably to a lesser degree, drugs have a considerable negative effect on traffic safety (Seppala et al. 1979; Smiley and Brookhuis 1987). Epidemiological studies assessed to what extent drivers under the influence of alcohol are overrepresented in accident statistics. Laboratory studies showed an actual impairment of driving skills. Reaction time and detection performance, especially, deteriorate as a function of alcohol dose (Peeke et al. 1980; Gustafson 1986a, 1986b; Rohrbaugh et al. 1987) or degree of sedation by (mostly medicinal) drugs (Hink et al. 1978; Gaillard and Verduin 1983). Driving impairment was demonstrated in real traffic for alcohol (Louwerens et al. 1987; Smiley et al. 1987) as well as for drugs (O’Hanlon et al. 1982; O’Hanlon 1984; Louwerens et al. 1986; Brookhuis et al. 1987; Volkerts et al. 1987; Smiley et al. 1987; Brookhuis et al. in press). Specifically the control over lateral position of a motor vehicle is a measure that appears to be highly sensitive to the level of blood alcohol concentration and to sedation by drugs. Recently, car-following ability shows promise as an indicator of driving ability impairment. The ability to react quickly to speed changing manoeuvres of a leading vehicle was found to be impaired by a number of sedative anxiolytics or tranquillizers (Brookhuis et al. 1987). The measurement of car-following performance can reveal impairments in attention and perception, which vastly predominate over response errors in accident causation (Smiley and Brookhuis 1987; Brookhuis et al., in preparation). Furthermore, a number of physiological changes have been reported as a result of alcohol and drug intake (Gaillard and Trumbo 1976; Gaillard and Verduin 1983; Peeke et al. 1980). For instance, alcohol dose-related changes in event-related potential components (Peeke et al. 1980; Rohrbaugh et al. 1987) are an indication of changes in central information processing capacity. Driver underload
Monotony is an important aspect of the driving task. The reason for this is that driving a car under relatively quiet conditions is relatively simple and monotonous for experienced drivers. Therefore, driving a car on a quiet highway is often considered to be a vigilance task (Wertheim 1978), resulting from underload. The time-on-task related deactivation is making the driver accident-prone (Lisper et al. 1986). Underload will result in a deviation from the top of the inverted-U curve towards low activation and poor performance. This is nicely illustrated in two experiments that combine performance measures and physiology (Thorsvall and Akerstedt. 1987; Brookhuis et al. 1986). After prolonged driving of a train or a car, a driver’s activation tends to diminish rapidly, as may be found by means of spectral analysis of the EEG. An increase in power in the alpha-band of the EEG-spectrum (8-13 Hz) has been found. to coincide with performance decrement while driving a car (Brookhuis et al. 1986). Alcohol and medicinal drugs mostly aggravate these effects (Peeke et al. 1980; Gawron and Ranney, 1988; Brookhuis et al. 1985), although this is not necessarily the case, depending on dose and type of drug (Fitzpatrick et al. 1988). Heart rate and heart rate variability can also serve as an index of mental load (Mulder 198.5; Aasman et al 1987). Time-on-task effects were found on performance, heart rate, heart rate variability, and the .lO Hz component of the power spectrum of inter-beat-interval sequences (Coles 1983; Brouwer and Van Wolffelaar 1985). Driver overload
Although the primary concern in driving performance research has been with mental underload, high workload situations resulting in mental overload might well be a major cause in, for instance, intersection-related accidents (Smrley and Smith 1985; Smiley and Brookhuis 1987). Overload emerges when two or more aspects of the driving task compete for attention (Michon 1985), and this is often the case at intersections. Secondary or subsidiary tasks have been used to measure spare mental capacity while driving a motor vehicle (Brown et al. 1969; Wetherell 1981; Lisper et al. 1986). The results of this type of measurement did not look very promising. In so far as the
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experiments involved relevant aspects of the driving task situation, they indicated that various levels of workload, e.g. induced by levels of steering difficulty, can be demonstrated weakly by performance on the secondary task. Heart rate and heart rate variability might be more promising. In particular heart rate variability and the earlier mentioned .lO Hz component are reported to be reliable indices of increased workload (Egelund 1985; Pruyn et al, 1985; Aasman et al. 1987). Objectives of the study In order to determine the mental load of driving a motor vehicle from driving alone (i.e. ~havioural parameter), it is necessary to establish the relationship between changes in driver status and behavioural parameters. Dingus et al. (1987) demonstrated in a simulator study that it may be possible to predict changes in behavioural parameters under the influence of alcohol and/or drowsiness. However, the lack of car dynamics and the inadequate visual scene of a simulator might both contaminate the changes in performance that are measured (see Smiley and Brookhuis 1987). Moreover, Dingus used only six subjects. The on-the-road demonstration experiment reported here, is devised to show the feasibility of measuring changes in driver behaviour as a consequence of changes in driver status. The monitoring device (DREAM) should be able to detect driving impairment on the basis of a limited set of vehicle parameters. We measured physiological parameters as control variables and inco~orated two of the major factors with respect to accident causation, i.e. the use of alcohol and the effects of time-ontask, potentially leading to driver underload.
METHOD
Subjects Twenty subjects participated in the experiment. They were male volunteers, 25-40 years old, having a driver’s license for at least five years and driving at least 5000 kilometres a year. Exclusion criteria for selection were: (i) history of alcohol or drug abuse, (ii) overt cardiovascular, respiratory, gastrointestinal, hepatic, renal, endocrine or haematological disorder, (iii) suffering of chronic (non-specific) lung disease (CNLD), (iv) present use of medication, and (v) working in night shifts. Apparatus Tests were conducted in a modified Volvo 245 GLD. Structural modifications included redundant controls at the front passenger’s seat for use, if necessary, by the accompanying licensed driving instructor to control the car in case of an extreme deterioration in the subject’s performance. Other modifications are listed in Fig. 1. Continuously measured during the test rides were: (i) Lateral position of the car, relative to the white delineation of the traffic lane, (ii) steering wheel position, amplitude of deviation from zero degrees (i.e. mid position), (iii) speed of the test vehide, (iv) distance to the car in front, (v) speed of the car in front, (vi) electroencephalogram (EEG, PzOZ lead), and (vii) electrocardiogram (ECG). The first five signals were sampled online by a DEC LSI 11/23 computer at 4 Hz and stored on flexy disk. EEG was registered on an intrumented FM tape recorder and sampled offline, at 125 Hz. The inter-beatintervals of the ECG are registered as intervals between R-tops in milliseconds and stored on flexy disk. Procedure A practice session served to habituate the subjects to the test vehicle and to being monitored physiologically and psychologically. The experiment itself consisted of two conditions for each subject on two separate days. One was a one-hour driving test under the influence of alcohol (BAC = <*OS%), the second was a two and a half-hour driving test under vigilance conditions. The order of conditions was balanced across subjects. AAP
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Fig. 1. Model of the instrumented
test vehicle.
Features: 0 an electro-optical device (lanetracker) for measuring the vehicle’s lateral position, relative to the painted stripe road delineation; 8 event-recorder for the registration of key-inputs from a keyboard and cardiac inter-beat-intervals; 0 differential amplifier unit for the measurement and amplification of physiological signals; @ potentiometer, mounted on the steering wheel column for measuring steering wheel movements; 0 a doppler radar, mounted under the rear bumper for measuring speed of the test vehicle; 0 an infrared laser, enabling the measurement of the distance to a car in front; 0 radio-transmitter/receiver, to collect data, including the speed, from an instrumented car in front.
Subjects were served a light lunch at the institute. Alcohol was administered half an hour later, vodka mixed with orange juice, dependent upon body weight, sufficient for a BAC of not more than .05% (which is the legal limit in the Netherlands). Control over the lateral position of the test vehicle and car following ability were tested. Experimental conditions: A. The first part of the experiment consisted of the car-following test. For this the subjects were requested to follow a second instrumented car at a close but safe constant distance, on a heavy traffic ring-road around the city of Groningen. The leading car occasionally varied its speed between 50 and 80 km/h. This procedure took about 15 minutes. B. This was followed by a standard driving test: the subjects were instructed to drive a quiet 75 km motorway track, keeping the test vehicle in the middle of the right traffic lane at a steady speed of 100 km/h. This test part lasted about 50 minutes. C. A second motorway track of about 2 x 75 km, of low to mediocre traffic density, was driven by the subjects thereupon, under the same instructions as in B. This took about 100 minutes. D. A second car-following test, same procedure as in A, was the fourth part of the total test. This took again about 15 minutes. In the alcohol condition, only parts A and B were included; in the vigilance condition, all parts, A to D, were run. The first section of the vigilance test, i.e. parts A and B, were taken as the baseline for the vigilance condition (parts C and D), as well as for the alcohol condition, (parts A and B with alcohol). Data reduction and analysis
An extensive editing procedure ensued, involving both automatic and interactive routines. All of the data were thus analyzed in order to remove parts containing passing manoeuvres or artifacts. Descriptive statistics were calculated, including mean and standard deviation (SD) of lateral position and steering wheel movements from selected tracks on the motorway without curvature. Performance with respect to the car following
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task was evaluated by calculating the coherence between the two speed signals of the instrumented vehicles (Brookhuis et al., in preparation; Porges et al. 1980). The cardiac inter-beat-intervals were analyzed by means of a spectral analysis (Mulder et al. 1987). After off-line sampling (125 Hz) and filtering of the EEG (low-pass at 35 Hz), Fourier spectral analysis was carried out on the EEG data in order to derive energy values for theta-band (4-8 Hz), alpha-band (8-12 Hz) and beta-band (13-20 Hz). A state of subject’s activation was calculated as the relative energy parameter [(theta + alpha)/ beta]. Total test scores for each parameter were analyzed by a multivariate analysis of variance using SPSSX. RESULTS
Alcohol
The equivalent of BAC was measured with a breathalyser. The amount of alcohol administered to the subjects was sufficient for an average BAC of 0.046% at the start (A), and an average BAC of 0.035% just after the test ride (B). Car following
Car-following performance was tested in the first part, as a baseline (A) or under the influence of alcohol (A), and in the last part, in case of vigilance testing (D). In Fig. 2, delay in reaction (converted from the phase shift between the two speed signals) is depicted. The coherence between the speed signals is above .90 in all three conditions (a coherence of 1.0 being perfect), which shows that all subjects were well able to carry out the instructions. Alcohol slightly affected the phase shift; this implies that the time needed to perceive and respond to variations in speed of the leading car was increased by alcohol intake (F(1, 18) = 4.39, p < 0.051). The absolute effect was a 168 msec increase in delay, i.e. 19% impairment by alcohol compared to baseline. Time-on-task had no effect on delay; however, subjects kept a shorter distance between vehicles after two and a half hours of driving. This is represented by the time-headway subjects chose. Time-headway is defined as the time interval between the rear of the first car passing a point on the road and the front of the experimental vehicle reaching the same point (Fuller 1984). Compared to the baseline condition, the average time-headway to the
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Fig. 2. Delay in reaction to variations in speed of baseline, vigilance and alcohol condition in the carfollowing test.
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first car decreased from 959 msec to 853 msec in the vigilance condition 141, 15) = 12.43, p < O.OOS]. Alcohol did not affect time-headway significantly (increase to 983 msec) .
Standard driving test The SD of the lateral position across the motorway tracks (B as baseline and B under alcohol, C as vigilance condition) is shown in Fig. 3. Both alcohol [F(l, 18) = 7.75, p < 0.011 and prolonged driving [F(l, 18) = 24.58, p < O.OOl] affect the subject’s control over the test-vehicle’s lateral position. The impairment in both cases is about 30%.
Although the SD of the lateral position in the alcohol condition is affected, the SD of the steering wheel movements on the straight tracks is not. Only time-on-task does affect the SD of the steering wheel movements [F(l, 14) = 22.56, p < O.OOl]. The number of steering reversals per minute with a gap size of 10 degrees (McLean and Hoffman 1975) was significantly decreased during the vigilance task [F(l, 15) = 20.24, p < O.OOl]. Figure 4 shows the SD of the steering wheel data. Heart rate (variability) The inter-beat-intervals (IBI) of the ECG were registered during the car-following test (parts A and D) and on the motorway (parts B and C). In Fig. 5 all conditions are depicted. In order to show a more detailed time-on-task effect, each of the conditions is divided into two equal parts (actually to and from the turning points). Clearly visible is the effect of 150 minutes of driving and habituation. Multivariate trend analysis showed a significant linear increase in IBI [F(3, 54) = 34.92, p < O.OOl] on the motorway. The return to the busy ring-road brought this gradual decrease in heart rate to a halt. In the alcohol condition, a completely different effect appeared; at the start of the (car-following) test ride, when the alcohol level was high, heart rate was slow, gradually increasing towards the end, when the alcohol level was lower [F(l, 16) = 9.93, p < 0.0061. Heart rate variability showed similar effects and will not be treated separately.
I)*
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Fig. 3. SD lateral position of baseline condition compared to prolonged driving (vigilance) and to driving under the influence of alcohol (BAG 0.046%-0.035%).
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(demees)
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Fig. 4. SD steering wheel from selected tracks on the motorway without curvature. baseline, vigilance, and alcohol condition.
Depicted
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EEG The activation of the subjects as measured by the relative energy parameter [(theta + alpha)/beta], shows a similar picture as the heart rate in the vigilance condition. With time-on-task activation gradually diminishes until the second car following test. Multivariate trend analysis confirms the linear energy increase [F(5, 65) = 3.45, p < O.OOS].On the ring-road in the second car-following test, activation immediately increases compared to the activation on the motorway. Although the data in Fig. 6 suggest that alcohol decreased subjects’ activation, this effect was not significant. The EEG and ECG, as treated in this experiment, represent rather global indices, that reliably reflect gradual changes in driver status. These physiological signs of changes in driver status are readily followed by changes in driver behaviour. Fig. 7 illustrates this point, IBI (ms)
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of the ECG for all conditions, separated in to- and from-the-turning point in each condition.
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of the EEG for all conditions, separated in to- and from-the-turningpoint in each condition.
Subjects’ activation starts to decrease rather quickly from the ring-road (15 minutes) to the quiet motorway, as shown by the first three bars of the EEG representation (Fig. 7). Once on the motorway, the activation continues to decrease (significantly!), but rather slowly now. The control over lateral position decreases as well but non-monotonically in the sense that not until the second motorway section (C), performance deteriorates quickly to a point of being really impaired (see Allen and Stein, in press). The signs of approaching impairment are visible long before that point. As expected, correlation between averages of SD lateral position and EEG is moderately low but significant (I = .30, p < 0.01).
(alpha + theta)
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Fig. 7. Average SD lateral position and EEG [(theta + alpha)/beta] vigilance test.
RING-ROAD
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DISCUSSION Demonstrating the feasibility of monitoring driver status by monitoring driver behaviour hinges on demonstrating co-occurrence of changes in behaviour preceded by changes in status. Signs of changing driver status may be manyfold from many sources.
A first discrimination could be found in phasic versus tonic changes in the driver’s physiology, a second in localized versus global measurement. In this study we found that the tonic changes in EEG preceded changes in behavioural parameters, such as the SD of the lateral position. Compared to previous studies, which showed that alcohol impairs driving behaviour with respect to the lateral position control (Louwerens et al. 1987), the impairment found in this experiment at a BAC between .046% and .035% is unexpected. In the present experiment, an increase in the SD of the lateral position of more than 5 cm, i.e. 30% impairment, was shown, whereas Louwerens et al. reported such an impairment after a BAC over .lO%. However, we did not select our subjects on drinking habits. The effects of alcohol were additionally present when subjects had to respond to speed changes of a leading car; alcohol resulted in an impairment of 19% compared to baseline-delay. Time-on-task, i.e. 150 minutes of continuous driving, did not cause an effect on delay in the car-following test, which is in line with findings of other studies (see Haworth et al. 1988). At least a part of the explanation for finding no effect lies in the closer distance our subjects kept to the leading car after 150 minutes, thus facilitating the perception of speed changes (Janssen et al. 1976). In this experiment, gradual (tonic) changes in global physiological measures are followed by gradual changes in behaviour. In this way the effects of a (potential) driver underload are replicated. Besides, the effect of one possible external factor (alcohol) is shown to be deleterious in some respects. It does not cause a gradual change but starts at a different offset. The monitoring device, DREAM, should be able to cope with that. For this, it is necessary that each driver together with his or her capabilities and driving style should be identified by DREAM at the moment he or she enters the vehicle. This could be accomplished by, for instance, a driver’s license on a smart-card (DRIVE project SMART), that has to be read by DREAM before starting a ride. This card should contain a recdrd of vehicle parameters of a driver’s normal performance. In this way, individual differences between drivers are taken care of at the same time. The third important factor, mentioned in the introduction, is driver overload. Global tonic measurements are not sufficient to detect driver’s overload, because driver overload usually arises quickly. In order to accomplish the monitoring of short-term driver overload, at first phasic physiological measurements will be necessary, such as galvanic skin response (GSR), eye-movements, event-related potentials of the EEG and perhaps online digital filtering of the ECG. Additional information from the world outside the car will be necessary for DREAM to predict potential overload. Other DRIVE projects, such as V1041 GIDS (generic intelligent driver support systems), aim at such interactions between the driver and information from the outside world. In conclusion, it can be said that it is feasible to develop a monitoring device on the basis of unobtrusive vehicle parameters alone. However, further experimental research is still needed, for instance to find out what is an acceptable driver status and which measures are the most adequate to be used in a monitoring device.
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Porges, S. W.; Bohrer, R. E.; Cheung, M. N.; Drasgow, F.; McCabe, P. H.; Keren, G. New time-series statistics for detecting rhythmic co-occurrence in the frequency domain: The weighted coherence and its application to psychophysiological research. Psychol. Bull. 88: 580-587; 1980. Pruyn. A., Aasman, J. & Wyers, B., Social influences on mental processes and cardiovascular activity. In Orlebeke, J. E, Muider, G. & Vandoornen, L. J. P. eds. Psychophysiology of Cardiovascular Control, 865-877. New York, London: Plenum Press, 1985. Rohrbaugh, J. W. et al. Dose-related effects of ethanol on visual sustained attention and event-related potentials. Alcohol 4: 293-300; 1987. Seppala, T.; Linnoiia, M.; Mattiia, M. J. Drugs, alcohol and driving. Drugs 17: 389-408: 1979. Smiiey, A.; Smith, R. L. Driving task monito~ng to reduce inattention-related accidents. Technical report. Washington, DC: National Highway Traffic Safety Administration; 1985. Smiiey, A.: Brookhuis, K. A. Alcohol; drugs and traffic safety. In: J. A. Rothengatter; R. A. de Bruin, eds. Road users and traffic safetv. Assen, The Netherlands: Van Gorcum: 1987: 83-105. Smiiey, A.; Noy, 1.; Tostowaryk, W. The effects of marihuana alone and in’combination with alcohol on driving performance. In: P. C. Noordzij; R. Roszbach, eds. Alcohol, drugs and traffic safety-T86. Amsterdam: Excerpta Medica; 1987; 203-207. Thorsvali, L.; Akerstedt, T. Sleepiness on the job: Continuously measured EEG changes in train drivers, EEG Journal 66: 502-511; 1987. Voikerts, E. R.; Brookhuis, K. A.; O’Hanlon, J. F. The effects of treatment with buspirone, diazepam and lorazepam on driving performance in real traffic. In: P. C. Noordzij; R. Roszbach, eds. Alcohol, drugs and traffic safety-T86. Amsterdam: Excerpta Medica; 1987; 217-223. Wertheim, A. H. Explaining highway hypnosis: Experimental evidence for the role of eye movements. Accid. Anal Prev., 10: 111-129; 1987. Wethereii, A. The efficacy of some auditory-vocal subsidiary tasks as measures of the mental load on male and female drivers. Ergonomics 24: 197-214; 1981. Wiener, E. L.; Curry, R. E.; Faustina, M. L. Vigilance and task load: in search of the inverted-U. Hum. Factors 26: 215-222; 1984.
& Prev. in Great Britain.
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ASSESSING DRIVER STATUS: A DEMONSTRATION EXPERIMENT ON THE ROAD DICK DE WAARD AND KAREL A. BROOKHUIS Traffic Research Centre, University of Groningen, P.O. Box 69, 9750 AB Haren (Gn), The Netherlands (Received 22 February 1990) Abstract-Twenty subjects completed an on-the-road experiment that consisted of two parts on two separate days. One was a one-hour driving test under the influence of alcohol (BAC < = 0.05%). the second a two-and-a-half-hour driving test under vigilance conditions. Impairment of driving performance was measured in a car-following test as well as in a standard driving test. Changes in relevant physiological parameters, such as ECG and EEG, reflected changes in driver status and predicted driving performance impairment.
INTRODUCTION
In 1988 the Commission of the European Communities formally adopted DRIVE (Dedicated Road Infrastructure for Vehicle Safety in Europe) as a European Community research programme. DRIVE encompasses research and development in the field of road transport informatics and telecommunication. Part of it is the development of an intelligent electronic co-driver, a computer system integrated in the car of the very near future. DRIVE envisages a complete general European road transport environment in which individual drivers are better informed and monitored, in which “intelligent” vehicles communicate and cooperate with each other, the road users, and the road infrastructure itself. One of DRIVE’s primary objectives is improvement of road safety. The ultimate goal of one of the present DRIVE projects, V1004 or DRIVER STATUS MONITORING, is to develop a device with the acronym DREAM (Driver Related Evaluation And Monitoring), whose sole assignment is to monitor driver status continuously through vehicle parameters. This paper is a report of an initial experiment to determine the feasibility of such a device. A decrement in driver’s performance may result from different sources. First, degradations in personal well-being might influence performance, for instance by illness or sleep deprivation. External factors, such as alcohol or drug intake, can certainly be responsible for vast performance deterioration (Seppala et al. 1979; Smiley and Brookhuis 1987). Alcohol alone is perhaps the prime malefactor of all traffic accidents. Another important factor, resulting in, for instance, falling-asleep accidents, is driver underload. Underload is a time-related factor, leading to reduced arousal and to inattention. Inattention is found to be a factor of major importance in accident causation (Smiley and Smith 1985). Finally, driver overload is a factor of concern in the field of accident causation. For instance, approaching and crossing intersections requires sufficient lookout, but also paying attention to what one is looking at, and thus the ability for divided attention (Smiley and Brookhuis 1987). Ideally, a driver acts on the top of a hypothetical inverted-U curve of performance (Wiener et al. 1984). Likewise ideally, an electronic driver monitoring system detects future serious deviations from that position on the curve. However, changes in driver status (i.e. physiological changes), which are the precursors of such (behavioural) deviations to come, cannot be measured by an electronic in-vehicle device that will be widely used, but only in experimental vehicles under special conditions. So, the natural restriction for this research project is demonstrating the feasibility of detection of early deviations from the ideal curve position by behaviourial measures that are simply measured by an electronic monitoring device.
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Alcohol and drugs It has been established
by means of epidemiological studies that alcohol and, probably to a lesser degree, drugs have a considerable negative effect on traffic safety (Seppala et al. 1979; Smiley and Brookhuis 1987). Epidemiological studies assessed to what extent drivers under the influence of alcohol are overrepresented in accident statistics. Laboratory studies showed an actual impairment of driving skills. Reaction time and detection performance, especially, deteriorate as a function of alcohol dose (Peeke et al. 1980; Gustafson 1986a, 1986b; Rohrbaugh et al. 1987) or degree of sedation by (mostly medicinal) drugs (Hink et al. 1978; Gaillard and Verduin 1983). Driving impairment was demonstrated in real traffic for alcohol (Louwerens et al. 1987; Smiley et al. 1987) as well as for drugs (O’Hanlon et al. 1982; O’Hanlon 1984; Louwerens et al. 1986; Brookhuis et al. 1987; Volkerts et al. 1987; Smiley et al. 1987; Brookhuis et al. in press). Specifically the control over lateral position of a motor vehicle is a measure that appears to be highly sensitive to the level of blood alcohol concentration and to sedation by drugs. Recently, car-following ability shows promise as an indicator of driving ability impairment. The ability to react quickly to speed changing manoeuvres of a leading vehicle was found to be impaired by a number of sedative anxiolytics or tranquillizers (Brookhuis et al. 1987). The measurement of car-following performance can reveal impairments in attention and perception, which vastly predominate over response errors in accident causation (Smiley and Brookhuis 1987; Brookhuis et al., in preparation). Furthermore, a number of physiological changes have been reported as a result of alcohol and drug intake (Gaillard and Trumbo 1976; Gaillard and Verduin 1983; Peeke et al. 1980). For instance, alcohol dose-related changes in event-related potential components (Peeke et al. 1980; Rohrbaugh et al. 1987) are an indication of changes in central information processing capacity. Driver underload
Monotony is an important aspect of the driving task. The reason for this is that driving a car under relatively quiet conditions is relatively simple and monotonous for experienced drivers. Therefore, driving a car on a quiet highway is often considered to be a vigilance task (Wertheim 1978), resulting from underload. The time-on-task related deactivation is making the driver accident-prone (Lisper et al. 1986). Underload will result in a deviation from the top of the inverted-U curve towards low activation and poor performance. This is nicely illustrated in two experiments that combine performance measures and physiology (Thorsvall and Akerstedt. 1987; Brookhuis et al. 1986). After prolonged driving of a train or a car, a driver’s activation tends to diminish rapidly, as may be found by means of spectral analysis of the EEG. An increase in power in the alpha-band of the EEG-spectrum (8-13 Hz) has been found. to coincide with performance decrement while driving a car (Brookhuis et al. 1986). Alcohol and medicinal drugs mostly aggravate these effects (Peeke et al. 1980; Gawron and Ranney, 1988; Brookhuis et al. 1985), although this is not necessarily the case, depending on dose and type of drug (Fitzpatrick et al. 1988). Heart rate and heart rate variability can also serve as an index of mental load (Mulder 198.5; Aasman et al 1987). Time-on-task effects were found on performance, heart rate, heart rate variability, and the .lO Hz component of the power spectrum of inter-beat-interval sequences (Coles 1983; Brouwer and Van Wolffelaar 1985). Driver overload
Although the primary concern in driving performance research has been with mental underload, high workload situations resulting in mental overload might well be a major cause in, for instance, intersection-related accidents (Smrley and Smith 1985; Smiley and Brookhuis 1987). Overload emerges when two or more aspects of the driving task compete for attention (Michon 1985), and this is often the case at intersections. Secondary or subsidiary tasks have been used to measure spare mental capacity while driving a motor vehicle (Brown et al. 1969; Wetherell 1981; Lisper et al. 1986). The results of this type of measurement did not look very promising. In so far as the
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experiments involved relevant aspects of the driving task situation, they indicated that various levels of workload, e.g. induced by levels of steering difficulty, can be demonstrated weakly by performance on the secondary task. Heart rate and heart rate variability might be more promising. In particular heart rate variability and the earlier mentioned .lO Hz component are reported to be reliable indices of increased workload (Egelund 1985; Pruyn et al, 1985; Aasman et al. 1987). Objectives of the study In order to determine the mental load of driving a motor vehicle from driving alone (i.e. ~havioural parameter), it is necessary to establish the relationship between changes in driver status and behavioural parameters. Dingus et al. (1987) demonstrated in a simulator study that it may be possible to predict changes in behavioural parameters under the influence of alcohol and/or drowsiness. However, the lack of car dynamics and the inadequate visual scene of a simulator might both contaminate the changes in performance that are measured (see Smiley and Brookhuis 1987). Moreover, Dingus used only six subjects. The on-the-road demonstration experiment reported here, is devised to show the feasibility of measuring changes in driver behaviour as a consequence of changes in driver status. The monitoring device (DREAM) should be able to detect driving impairment on the basis of a limited set of vehicle parameters. We measured physiological parameters as control variables and inco~orated two of the major factors with respect to accident causation, i.e. the use of alcohol and the effects of time-ontask, potentially leading to driver underload.
METHOD
Subjects Twenty subjects participated in the experiment. They were male volunteers, 25-40 years old, having a driver’s license for at least five years and driving at least 5000 kilometres a year. Exclusion criteria for selection were: (i) history of alcohol or drug abuse, (ii) overt cardiovascular, respiratory, gastrointestinal, hepatic, renal, endocrine or haematological disorder, (iii) suffering of chronic (non-specific) lung disease (CNLD), (iv) present use of medication, and (v) working in night shifts. Apparatus Tests were conducted in a modified Volvo 245 GLD. Structural modifications included redundant controls at the front passenger’s seat for use, if necessary, by the accompanying licensed driving instructor to control the car in case of an extreme deterioration in the subject’s performance. Other modifications are listed in Fig. 1. Continuously measured during the test rides were: (i) Lateral position of the car, relative to the white delineation of the traffic lane, (ii) steering wheel position, amplitude of deviation from zero degrees (i.e. mid position), (iii) speed of the test vehide, (iv) distance to the car in front, (v) speed of the car in front, (vi) electroencephalogram (EEG, PzOZ lead), and (vii) electrocardiogram (ECG). The first five signals were sampled online by a DEC LSI 11/23 computer at 4 Hz and stored on flexy disk. EEG was registered on an intrumented FM tape recorder and sampled offline, at 125 Hz. The inter-beatintervals of the ECG are registered as intervals between R-tops in milliseconds and stored on flexy disk. Procedure A practice session served to habituate the subjects to the test vehicle and to being monitored physiologically and psychologically. The experiment itself consisted of two conditions for each subject on two separate days. One was a one-hour driving test under the influence of alcohol (BAC = <*OS%), the second was a two and a half-hour driving test under vigilance conditions. The order of conditions was balanced across subjects. AAP
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Fig. 1. Model of the instrumented
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Features: 0 an electro-optical device (lanetracker) for measuring the vehicle’s lateral position, relative to the painted stripe road delineation; 8 event-recorder for the registration of key-inputs from a keyboard and cardiac inter-beat-intervals; 0 differential amplifier unit for the measurement and amplification of physiological signals; @ potentiometer, mounted on the steering wheel column for measuring steering wheel movements; 0 a doppler radar, mounted under the rear bumper for measuring speed of the test vehicle; 0 an infrared laser, enabling the measurement of the distance to a car in front; 0 radio-transmitter/receiver, to collect data, including the speed, from an instrumented car in front.
Subjects were served a light lunch at the institute. Alcohol was administered half an hour later, vodka mixed with orange juice, dependent upon body weight, sufficient for a BAC of not more than .05% (which is the legal limit in the Netherlands). Control over the lateral position of the test vehicle and car following ability were tested. Experimental conditions: A. The first part of the experiment consisted of the car-following test. For this the subjects were requested to follow a second instrumented car at a close but safe constant distance, on a heavy traffic ring-road around the city of Groningen. The leading car occasionally varied its speed between 50 and 80 km/h. This procedure took about 15 minutes. B. This was followed by a standard driving test: the subjects were instructed to drive a quiet 75 km motorway track, keeping the test vehicle in the middle of the right traffic lane at a steady speed of 100 km/h. This test part lasted about 50 minutes. C. A second motorway track of about 2 x 75 km, of low to mediocre traffic density, was driven by the subjects thereupon, under the same instructions as in B. This took about 100 minutes. D. A second car-following test, same procedure as in A, was the fourth part of the total test. This took again about 15 minutes. In the alcohol condition, only parts A and B were included; in the vigilance condition, all parts, A to D, were run. The first section of the vigilance test, i.e. parts A and B, were taken as the baseline for the vigilance condition (parts C and D), as well as for the alcohol condition, (parts A and B with alcohol). Data reduction and analysis
An extensive editing procedure ensued, involving both automatic and interactive routines. All of the data were thus analyzed in order to remove parts containing passing manoeuvres or artifacts. Descriptive statistics were calculated, including mean and standard deviation (SD) of lateral position and steering wheel movements from selected tracks on the motorway without curvature. Performance with respect to the car following
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task was evaluated by calculating the coherence between the two speed signals of the instrumented vehicles (Brookhuis et al., in preparation; Porges et al. 1980). The cardiac inter-beat-intervals were analyzed by means of a spectral analysis (Mulder et al. 1987). After off-line sampling (125 Hz) and filtering of the EEG (low-pass at 35 Hz), Fourier spectral analysis was carried out on the EEG data in order to derive energy values for theta-band (4-8 Hz), alpha-band (8-12 Hz) and beta-band (13-20 Hz). A state of subject’s activation was calculated as the relative energy parameter [(theta + alpha)/ beta]. Total test scores for each parameter were analyzed by a multivariate analysis of variance using SPSSX. RESULTS
Alcohol
The equivalent of BAC was measured with a breathalyser. The amount of alcohol administered to the subjects was sufficient for an average BAC of 0.046% at the start (A), and an average BAC of 0.035% just after the test ride (B). Car following
Car-following performance was tested in the first part, as a baseline (A) or under the influence of alcohol (A), and in the last part, in case of vigilance testing (D). In Fig. 2, delay in reaction (converted from the phase shift between the two speed signals) is depicted. The coherence between the speed signals is above .90 in all three conditions (a coherence of 1.0 being perfect), which shows that all subjects were well able to carry out the instructions. Alcohol slightly affected the phase shift; this implies that the time needed to perceive and respond to variations in speed of the leading car was increased by alcohol intake (F(1, 18) = 4.39, p < 0.051). The absolute effect was a 168 msec increase in delay, i.e. 19% impairment by alcohol compared to baseline. Time-on-task had no effect on delay; however, subjects kept a shorter distance between vehicles after two and a half hours of driving. This is represented by the time-headway subjects chose. Time-headway is defined as the time interval between the rear of the first car passing a point on the road and the front of the experimental vehicle reaching the same point (Fuller 1984). Compared to the baseline condition, the average time-headway to the
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first car decreased from 959 msec to 853 msec in the vigilance condition 141, 15) = 12.43, p < O.OOS]. Alcohol did not affect time-headway significantly (increase to 983 msec) .
Standard driving test The SD of the lateral position across the motorway tracks (B as baseline and B under alcohol, C as vigilance condition) is shown in Fig. 3. Both alcohol [F(l, 18) = 7.75, p < 0.011 and prolonged driving [F(l, 18) = 24.58, p < O.OOl] affect the subject’s control over the test-vehicle’s lateral position. The impairment in both cases is about 30%.
Although the SD of the lateral position in the alcohol condition is affected, the SD of the steering wheel movements on the straight tracks is not. Only time-on-task does affect the SD of the steering wheel movements [F(l, 14) = 22.56, p < O.OOl]. The number of steering reversals per minute with a gap size of 10 degrees (McLean and Hoffman 1975) was significantly decreased during the vigilance task [F(l, 15) = 20.24, p < O.OOl]. Figure 4 shows the SD of the steering wheel data. Heart rate (variability) The inter-beat-intervals (IBI) of the ECG were registered during the car-following test (parts A and D) and on the motorway (parts B and C). In Fig. 5 all conditions are depicted. In order to show a more detailed time-on-task effect, each of the conditions is divided into two equal parts (actually to and from the turning points). Clearly visible is the effect of 150 minutes of driving and habituation. Multivariate trend analysis showed a significant linear increase in IBI [F(3, 54) = 34.92, p < O.OOl] on the motorway. The return to the busy ring-road brought this gradual decrease in heart rate to a halt. In the alcohol condition, a completely different effect appeared; at the start of the (car-following) test ride, when the alcohol level was high, heart rate was slow, gradually increasing towards the end, when the alcohol level was lower [F(l, 16) = 9.93, p < 0.0061. Heart rate variability showed similar effects and will not be treated separately.
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Fig. 3. SD lateral position of baseline condition compared to prolonged driving (vigilance) and to driving under the influence of alcohol (BAG 0.046%-0.035%).
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Fig. 4. SD steering wheel from selected tracks on the motorway without curvature. baseline, vigilance, and alcohol condition.
Depicted
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EEG The activation of the subjects as measured by the relative energy parameter [(theta + alpha)/beta], shows a similar picture as the heart rate in the vigilance condition. With time-on-task activation gradually diminishes until the second car following test. Multivariate trend analysis confirms the linear energy increase [F(5, 65) = 3.45, p < O.OOS].On the ring-road in the second car-following test, activation immediately increases compared to the activation on the motorway. Although the data in Fig. 6 suggest that alcohol decreased subjects’ activation, this effect was not significant. The EEG and ECG, as treated in this experiment, represent rather global indices, that reliably reflect gradual changes in driver status. These physiological signs of changes in driver status are readily followed by changes in driver behaviour. Fig. 7 illustrates this point, IBI (ms)
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Subjects’ activation starts to decrease rather quickly from the ring-road (15 minutes) to the quiet motorway, as shown by the first three bars of the EEG representation (Fig. 7). Once on the motorway, the activation continues to decrease (significantly!), but rather slowly now. The control over lateral position decreases as well but non-monotonically in the sense that not until the second motorway section (C), performance deteriorates quickly to a point of being really impaired (see Allen and Stein, in press). The signs of approaching impairment are visible long before that point. As expected, correlation between averages of SD lateral position and EEG is moderately low but significant (I = .30, p < 0.01).
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DISCUSSION Demonstrating the feasibility of monitoring driver status by monitoring driver behaviour hinges on demonstrating co-occurrence of changes in behaviour preceded by changes in status. Signs of changing driver status may be manyfold from many sources.
A first discrimination could be found in phasic versus tonic changes in the driver’s physiology, a second in localized versus global measurement. In this study we found that the tonic changes in EEG preceded changes in behavioural parameters, such as the SD of the lateral position. Compared to previous studies, which showed that alcohol impairs driving behaviour with respect to the lateral position control (Louwerens et al. 1987), the impairment found in this experiment at a BAC between .046% and .035% is unexpected. In the present experiment, an increase in the SD of the lateral position of more than 5 cm, i.e. 30% impairment, was shown, whereas Louwerens et al. reported such an impairment after a BAC over .lO%. However, we did not select our subjects on drinking habits. The effects of alcohol were additionally present when subjects had to respond to speed changes of a leading car; alcohol resulted in an impairment of 19% compared to baseline-delay. Time-on-task, i.e. 150 minutes of continuous driving, did not cause an effect on delay in the car-following test, which is in line with findings of other studies (see Haworth et al. 1988). At least a part of the explanation for finding no effect lies in the closer distance our subjects kept to the leading car after 150 minutes, thus facilitating the perception of speed changes (Janssen et al. 1976). In this experiment, gradual (tonic) changes in global physiological measures are followed by gradual changes in behaviour. In this way the effects of a (potential) driver underload are replicated. Besides, the effect of one possible external factor (alcohol) is shown to be deleterious in some respects. It does not cause a gradual change but starts at a different offset. The monitoring device, DREAM, should be able to cope with that. For this, it is necessary that each driver together with his or her capabilities and driving style should be identified by DREAM at the moment he or she enters the vehicle. This could be accomplished by, for instance, a driver’s license on a smart-card (DRIVE project SMART), that has to be read by DREAM before starting a ride. This card should contain a recdrd of vehicle parameters of a driver’s normal performance. In this way, individual differences between drivers are taken care of at the same time. The third important factor, mentioned in the introduction, is driver overload. Global tonic measurements are not sufficient to detect driver’s overload, because driver overload usually arises quickly. In order to accomplish the monitoring of short-term driver overload, at first phasic physiological measurements will be necessary, such as galvanic skin response (GSR), eye-movements, event-related potentials of the EEG and perhaps online digital filtering of the ECG. Additional information from the world outside the car will be necessary for DREAM to predict potential overload. Other DRIVE projects, such as V1041 GIDS (generic intelligent driver support systems), aim at such interactions between the driver and information from the outside world. In conclusion, it can be said that it is feasible to develop a monitoring device on the basis of unobtrusive vehicle parameters alone. However, further experimental research is still needed, for instance to find out what is an acceptable driver status and which measures are the most adequate to be used in a monitoring device.
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