Factors correlated with traffic accidents as a basis for evaluating Advanced Driver Assistance Systems

Accident Analysis and Prevention 41 (2009) 1025–1033

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Factors correlated with traffic accidents as a basis for evaluating Advanced Driver Assistance Systems Maria Staubach ∗ Volkswagen AG, Department Driving Assistance & Integrated Safety, Accident Research, Letter Box 1777, 38436 Wolfsburg, Germany

a r t i c l e

i n f o

Article history: Received 19 May 2008 Received in revised form 26 April 2009 Accepted 13 June 2009 Keywords: Accident Causation In depth Errors Pre-crash

a b s t r a c t This study aims to identify factors which influence and cause errors in traffic accidents and to use these as a basis for information to guide the application and design of driver assistance systems. A total of 474 accidents were examined in depth for this study by means of a psychological survey, data from accident reports, and technical reconstruction information. An error analysis was subsequently carried out, taking into account the driver, environment, and vehicle sub-systems. Results showed that all accidents were influenced by errors as a consequence of distraction and reduced activity. For crossroad accidents, there were further errors resulting from sight obstruction, masked stimuli, focus errors, and law infringements. Lane departure crashes were additionally caused by errors as a result of masked stimuli, law infringements, expectation errors as well as objective and action slips, while same direction accidents occurred additionally because of focus errors, expectation errors, and objective and action slips. Most accidents were influenced by multiple factors. There is a safety potential for Advanced Driver Assistance Systems (ADAS), which support the driver in information assimilation and help to avoid distraction and reduced activity. The design of the ADAS is dependent on the specific influencing factors of the accident type. © 2009 Elsevier Ltd. All rights reserved.

1. Introduction and questions The number of fatalities and injuries from traffic accidents has fallen steadily since the 1970s in Germany and many other European countries but, despite this, around 5000 people still die each year as a result of traffic accidents in Germany, with more than 400,000 suffering injuries. Younger drivers (18–24 years) are particularly at risk, with traffic accidents being the most frequent cause of death in this age group (German Federal Statistical Office, 2007). According to figures from the German Federal Statistical Office (2007) the main cause of traffic accidents is human error, which plays a part in over 90% of traffic accidents. From the point of view of vehicle manufacturers, there is huge potential to reduce the frequency of this kind of accident through use of ADAS. Therefore, as part of this study, typical errors that lead to accidents were identified and quantified with the aid of comprehensive accident analysis. Interviews were carried out with drivers regarding their experiences and behaviour when traffic accidents occurred and these were combined with information from traffic accident reports and technical accident reconstructions. Traffic accident analysis is associated with a long tradition of error research within industrial environments (e.g. Rasmussen,

∗ Tel.: +49 5361 896 2367; fax: +49 5361 896 2399. E-mail addresses: [email protected], [email protected]. 0001-4575/$ – see front matter © 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.aap.2009.06.014

1982; Reason, 1990). Triggered by the momentous incidents involving nuclear power plants (e.g. Chernobyl, 1986), typical human error in interaction with machines was observed and described with the aim of generating conditions and processes to ensure safety in the workplace. Great emphasis was placed on aspects of human perception, awareness and cognitive thought to make human–machine interaction more reliable. In the model of prevention-related classification of human error according to Hacker (1998), a system classifying types of human error was drawn up which follows the process of human information processing. For the purposes of this study, this model was adapted to the context of traffic, with the aim of identifying those aspects of driving which need to be supported through use of ADAS in order to prevent accidents. The driver error categories are represented in Table 1. Sight obstruction is the evident cause of driver error if vision was impaired at the time of the accident by buildings, plants or other objects, particularly parked, stationary or moving vehicles. Masked stimuli are considered to be the main factor if there is an evidence of adverse weather conditions. This includes (heavy) sleet, rain and snow, fog and/or dusk or darkness, but also if the driver is being dazzled by the sun or other vehicles. The failure to notice oncoming vehicles or relevant traffic signs is a significant reason for not using information correctly, frequently associated with inattention. This may be caused by distraction, including secondary activities in the vehicle such as conversations, phone calls and operating devices, objects or events outside the vehicle which

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M. Staubach / Accident Analysis and Prevention 41 (2009) 1025–1033

Table 1 Driver error classification (adapted in accordance with Hacker, 1998). Driver error category

Specific influence

Examples of conditions experienced

(A) Objective lack of information

Sight obstruction Masking stimuli

Buildings, plants, other vehicles Darkness, being dazzled, rain, snow, fog

(B) Failure to use information

Failure to spot danger through inattention

Distraction by objects inside/outside of the vehicle, reduced activity as a result of drowsiness/medication, attention focused incorrectly Omissions of using the turn signal, shifting gears or checking the blind spot because of absent-mindedness Deliberate speed violations or disregarding right of way Errors caused by false expectations and stereotyping, i.e. assuming that a wide road is a major road Perception of acceleration

Omissions

Disregard/infringements Reduction of information processing

Processing deficits (C) Misuse of information

Orientation and expectation errors

Misjudgements Objective slips, incorrect responses

are not related to the driving task, or internal distraction, such as negative emotions or stress. If the driver observes the traffic or traffic-relevant information, but not that which is relevant for carrying out the planned manoeuvre, his or her attention is not focused accurately. This may be due to information overload (for example, when the driver is not familiar with the traffic situation) or deficits concerning selective attention on the driving task. Dingus et al. (2006) refer to this as ‘driver-related inattention to the forward roadway’. They specify another important aspect of an inaccurate attention focus as ‘non-specific eye-glances’. Thus drivers look away from the roadway but not at a specific object. Reduced activity as a result of drowsiness, use or consumption of intoxicating substances (alcohol, drugs and medication) may also reduce driver attention. Similar to driver inattention, omissions can also have a negative influence on assimilating important information if, for example, automatic actions when driving, such as operating the gears, using the turn signals or checking the blind spot, are omitted. Errors which arise through deliberate infringement of traffic regulations (e.g. breaking the speed limit by more than 10 km/h or disregarding rules on the right of way), are grouped under the category disregards/infringements. If drivers have reached the limits of their ability to process information, errors are caused by processing deficits. For example, a study by Doerner (2003) identified that subjects had big difficulties estimating exponential or acceleration processes. Thus many people tend to underestimate the velocity of other vehicles which are accelerating. Incorrect reduction of information processing relates to errors of expectation and stereotyping. Thus they do not appreciate a possible change of circumstances. For example, drivers disregard the right of way because they believe that, given the appearance of the road, they are on a major road or they do not look the other way for cyclists when turning into one-way streets. In contrast to this, errors in orientation or expectation are classified as an inadequate use of information, which is often determined by the driver’s familiarity with the scene of the accident in question or a similar place. Therefore, people sometimes do not take into account crossing-traffic at minor intersections if no one has come from that direction ever before. Drivers also do not use information adequately when they misjudge speeds or distances. Another error category relates to incorrect responses such as swerving instead of braking, or objective slips when drivers

Errors in expectations as a result of habituation, e.g. not looking on quiet roads for vehicles approaching from the right Miscalculation of speeds and distances Swerving instead of braking vs. confusion between accelerator and brakes

execute wrong actions like mixing up the accelerator and brake pedal. Previous studies into the causes of and influences on traffic accidents emphasise in particular the influence of errors when assimilating information (e.g. Gründl, 2005; Chiellino, 2007). Accordingly errors caused by distractions or secondary activities play an important role (e.g. Stutts et al., 2001; Klauer et al., 2006; McEvoy et al., 2007). There is further evidence of correlations between stress as well as other negative emotions and road accidents (e.g. Shinar et al., 1978; Matthews et al., 1998; Groeger, 2000). Another type of driver inattention has been referred as “looked but failed to see” errors which are a result of failures in drivers’ visual search strategy and/or mental processing (Herslund and Jorgensen, 2003). Thus drivers do look in the direction where other road users are but do not perceive their presence (Koustanai et al., 2008). Such failures have been found especially for accidents involving older drivers (e.g. Brown, 2005). Other studies outline the failures of observation errors and situation awareness for older drivers (e.g. Langford and Koppel, 2006; Clarke et al., 1999), which is a consequence of a decrease in information processing capacity and deficits in selective attention (Schlag, 1993; Fastenmeier and Gstalter, 2005). Younger drivers, on the other hand, frequently have difficulty concerning the hazard perception (e.g. Quimby and Watts, 1981; Deery, 1999; Underwood et al., 2003; Garay-Vega and Fisher, 2005). Risky styles of driving and traffic violations are also often cited as a cause of traffic accidents (e.g. Reason et al., 1990; Iversen and Rundmo, 2004), especially in the case of single vehicle accidents on country roads and motorways. Furthermore there are several studies concerning the deterioration of driving performance due to alcohol (e.g. Arnedt et al., 2001; Williamson et al., 2001; Ronen et al., 2008), fatigue (Philip et al., 2005; Connor et al., 2001) or drowsiness (e.g. Ingre et al., 2006; Sagberg, 1999). Correlations between taking medication and the deterioration of driving performance have also often been discussed recently (e.g. Dionne et al., 1995; McGwin et al., 2000; Vernon et al., 2002; Alvarez and Fierro, 2008). In this study, questions should be answered about which influences have an effect in different types of traffic accidents and which requirements driver assistance systems and measures have to meet in order to offset negative influencing factors.

M. Staubach / Accident Analysis and Prevention 41 (2009) 1025–1033 Table 2 Results of the responder analysis. Responder

Non-responder

n

%

n

%

Accident causation Originator Opponent

363 258

63.1 72.9

212 96

36.9 27.1

Accident type Intersection accident Lane departure crash Same direction accident

297 161 134

67.8 65.2 70.9

141 86 55

32.2 34.8 29.1

Age (at fault) Up to 24 years 25–64 years 65 years and older

109 423 83

56.8 73.6 68.0

83 152 39

43.2 26.4 32.0

Sex (at fault) Male Female

426 194

67.9 67.4

201 94

32.1 32.6

Alcohola Yes No

13 608

50 67.8

13 289

50 32.2

Light conditions Daylight Darkness Dusk

444 143 27

67.5 67.1 73.0

214 70 10

32.5 32.9 27. 0

Location Urban areas Non-urban areas

376 238

69.0 65.6

169 125

31.0 34.4

a

2

p

9.40

0.002

3.30

0.348

19.13

0.000

0.03

0.861

3.63

0.057

0.51

0.774

1.17

0.280

Noted in the accident report by the police.

2. Method 2.1. Sample survey A total of 622 road users involved in 506 accidents in Dresden, Hanover and Wolfsburg participated in the study. However, the data of 38 interviews was excluded from the analysis, because they did

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not belong to any of the three considered accident type groups, or, in some cases the drivers’ statements were contradictory. Thus the interviews of 584 drivers involved in 474 accidents were included in the analysis. The participants were recruited as part of either the GIDAS project or Volkswagen accident research. GIDAS (German In Depth Accident Study) is a project financed by the Federal Highway Research Institute (BASt) and the German Association for Research on Automobile Technology (FAT) to collect data on traffic accidents in and around the German cities of Dresden and Hanover. The research teams investigate accidents involving personal injury according to a statistical sampling process. Accident investigation takes place daily during two shifts in a 2-week cycle (first week from 00:00 to 06:00 and 12:00 to 18:00; second week from 06:00 to 12:00 and 18:00 to 24:00). The accidents investigated by the Volkswagen accident research team concern only new VW-Models and also involve personal injury. For the GIDAS project, members of the police report the accidents to the research teams who immediately drive to the accident sites and start investigation. Volkswagen accident research also cooperates with the police but the investigation of the accident site is sometimes delayed. The information collected at the accident site includes factors such as environmental conditions, road design, traffic control, technical crash information (driving and collision speed, steering manoeuvres) and accident causes. Thus a reconstruction of the impact events becomes possible. The response of the drivers was voluntary and since the research sample was selected by convenience, a response bias cannot be foreclosed. Thus responder analyses were carried out. The results are shown in Table 2. The return to participation ratio was 67%. People who did not cause the accident participated significantly more often (2 = 9.40, p = 0.002). There were no significant differences between the rate of responders and non-responders regarding the accident type (2 = 3.30, p = 0.348), the location of the accident (2 = 1.17, p = 0.280), the light conditions (2 = 0.51, p = 0.774) and the sex of the drivers at-fault (2 = 0.03, p = 0.861). There was a significant difference considering the age of the responders and non-responders. People up to 24 years were significantly less willing to cooperate

Table 3 Characteristics of accidents studied. Crossroad accidents (n = 224)

Lane departure crashes (n = 141)

Same direction accidents (n = 109)

n

%

n

%

n

174 50

77.7 22.3

41 100

29.1 70.9

69 40

63.3 36.7

Time of accident 00:00–05:59 06:00–08:59 9:00–14:59 15:00–18:59 19:00–23:59

7 34 86 63 32

3.1 15.2 38.4 28.1 14.3

14 19 43 36 27

10.1 13.7 30.9 25.9 19.4

3 18 34 35 16

2.8 17.0 32.1 33.0 15.1

Light conditions Daylight Darkness Dusk

176 39 9

78.6 17.4 4.0

88 46 7

62.4 32.6 5.0

80 24 5

73.4 22.0 4.6

Gender (drivers at-fault) Male Female

117 49

70.5 29.5

74 37

66.7 33.3

37 18

67.3 32.7

Age (drivers at-fault) Up to 24 years 25–64 years 65 years and older

29 103 33

17.6 62.4 20.0

32 74 4

29.1 67.3 3.6

10 34 10

18.5 63.0 18.5

Number of injured and fatalities Slightly injured Seriously injured Fatalities Accidents without injuries

264 72 1 29

78.3 21.4 0.0 12.9

125 80 8 18

58.7 37.6 0.0 12.8

133 20 2 12

85.8 12.9 0.0 10.9

Location Urban roads Non-urban roads

%

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M. Staubach / Accident Analysis and Prevention 41 (2009) 1025–1033 Table 4 Inter-rater reliability. Driver error



Sight obstruction Masking stimuli Distraction Reduced activity Incorrect focus Forgetfulness, omission Disregard, infringements Processing deficits Reduction of information processing Errors in orientation Misjudgements Objective slips/incorrect responses

0.949 0.875 0.872 0.721 0.694 – 1.000 – 0.707 0.280 0.634 0.671

with the interviewers (2 = 19.13, p = 0.000). Further there was a nearly significant difference considering the intoxication with alcohol (2 = 3.63, p = 0.057). Thus people driving under the influence of alcohol were less willing to participate in this study. Drivers who declined to accept the interviews often feared negative consequences with regards to their insurance even though anonymity was guaranteed by the interviewers. Other non-responders were too emotionally traumatised in order to deal with the accident through an in-depth interview. Some also complained about the repeated questioning through police officers, insurance staff and the accident research teams. Of the 474 accidents, 224 took place at intersections when turning off, turning in and crossing. There were further 141 lane departure crashes and 109 same direction accidents, mainly rearend collisions and some overtaking crashes. Head-on crashes due to unintentionally moving out of the lane were classified as lane departure crashes. Amongst those taking part in the study, 333 (57%) drivers had caused the accident. All drivers involved in an accident were aged between 13 and 85 years (M = 41.6, SD = 16.2). Table 3 gives a comprehensive overview of the characteristics of the traffic accidents studied, based on the various accident type groups. 2.2. Interview guide and questionnaires The driver surveys were carried out by means of questionnaires and interviews using a structured interview manual. The questions focused on aspects relating to the accident (e.g. driver intent, actions such as signalling, speed, visibility conditions and restrictions as a result of sight obstruction or bad weather, direction of looking, braking and swerving manoeuvres, and distractions), the context of the accident (e.g. daily routine, drowsiness, concentration, emotions, stress, time constraints, taking medication, and familiarity with site of accident), and on background variables (e.g. age, driver experience, driving performance and frequency with

which vehicle is used, vehicle type, length of time vehicle has been owned, and state of health). The interviews were carried out by several interviewers. The structured interview manual made use of a technique known as funnel questioning, whereby questions were always open-ended to begin with and further details were then gradually determined as more specific questions were asked. Further, in order to ensure the reliability of the data acquisition, interviewer instructions took place emphasising the importance of open questioning.

2.3. Implementation The first contact to the accident drivers was made at the accident site. There the participants were asked for their phone numbers and were called back within one month. In Wolfsburg, interviews were carried out face-to-face, by phone or sometimes, if there was no other way to reach the drivers, by questionnaires, which were sent to the people within three days after the accident and usually came back within one month. In Hanover and Wolfsburg, some interviews were also carried out at the accident site. The data taken from the interviews was compared and supplemented with data from accident reports and the technical accident reconstruction. This was in order to take account of respondents making consciously false statements about, for example, the speeds at which the vehicle was travelling or stopping at stop signs. Such cases were coded as infringements. If several witness statements at the scene of the accident (documented in the police traffic accident report) pointed to driver distraction and these assumptions tallied with the technical reconstruction, then this was coded accordingly as distraction through secondary activities. In the next step, data was analysed using the aforementioned classification table, whereby the information from the individual survey statements, observation of the accident sites, reconstruction results, and accident reports was entered into an SPSS data table and hierarchically summarised according to preassigned definition rules in the classification table categories. As an example, sight obstruction was categorised if there was any evidence, either subjective by the driver or by investigation of the accident site, that buildings, plants, vehicles or other circumstances either outside or within the car such as pillars or dirty windows obstructed the sight towards the collision partner. In order to answer the questions posed in this study, descriptive data analyses (frequency distributions) were carried out. Further, the method of quasi-induced exposure (Haight, 1973; Lyles et al., 1991) was used as a means of risk analysis. Thus a logistic regression analysis was conducted in order to compare the error distributions of the “at-fault drivers” with the “innocent drivers”. The assignment of fault was accomplished by the accident research teams using all available sources including interviews, investigation of the accident sites, technical accident reconstruction, and police reports.

Table 5 Factors correlated with the causation of accidents in different class types (multiple factors possible). Driver error due to

Crossroad accidents, n = 167 %[95% CI]

Lane departure crashes, n = 111 %[95% CI]

Same direction accidents, n = 55 %[95% CI]

Sight obstruction Masking stimuli Distraction Reduced activity Incorrect focus Forgetfulness, omission Disregard, infringements Processing deficits Expectation errors Misjudgements Objective slips/incorrect responses

0.40 [0.32–0.47] 0.26 [0.20–0.34] 0.32 [0.25–0.39] 0.28 [0.21–0.35] 0.30 [0.24–0.37] 0.02 [0.01–0.06] 0.11 [0.07–0.17] 0.00 [0.00–0.02] 0.16 [0.11–0.22] 0.05 [0.03–0.10] 0.02 [0.01–0.06]

0.04 [0.01–0.09] 0.26 [0.19–0.35] 0.41 [0.33–0.51] 0.47 [0.38–0.56] 0.05 [0.03–0.11] 0.02 [0.00–0.06] 0.24 [0.17–0.33] 0.01 [0.00–0.05] 0.35 [0.27–0.44] 0.05 [0.02–0.10] 0.23 [0.17–0.32]

0.13 [0.06–0.24] 0.22 [0.13–0.34] 0.38 [0.27–0.51] 0.29 [0.19–0.42] 0.24 [0.14–0.36] 0.05 [0.02–0.15] 0.09 [0.04–0.20] 0.00 [0.00–0.07] 0.36 [0.25–0.50] 0.02 [0.00–0.10] 0.36 [0.25–0.50]

M. Staubach / Accident Analysis and Prevention 41 (2009) 1025–1033

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Table 6 Detailed factors correlated with causation of crossroad accidents. Driver error

Precise details

n

% [95% CI]

Sight obstruction (66 cases)

Other vehicles Buildings, plants Own vehicle

37 22 8

0.56 [0.44–0.67] 0.33 [0.23–0.45] 0.12 [0.06–0.22]

Masking stimuli (44 cases)

Darkness Rain/snow/fog Glare (sun)

10 9 23

0.23 [0.10–0.35] 0.20 [0.11–0.35] 0.52 [0.38–0.66]

Distraction (53 cases)

Secondary activities Negative thoughts/emotions

22 37

0.42 [0.30–0.57] 0.70 [0.56–0.80]

Reduced activity (46 cases)

Medication Drowsiness, fatigue

31 16

0.67 [0.53–0.79] 0.35 [0.23–0.49]

Incorrect focus (51 cases)

Focus on third vehicle Focus on another object

35 15

0.69 [0.55–0.80] 0.29 [0.19–0.43]

Violations (19 cases)

Right of way Overtaking when traffic situation unclear Incorrect turning

7 2 3

0.37 [0.19–0.59] 0.11 [0.03–0.31] 0.16 [0.06–0.38]

All reported confidence intervals in this paper were computed using the score method (Wilson, 1927 quoted by Newcombe, 2000).

Therefore these two categories are merged to form the category ‘expectation errors’ in the following chapter.

2.4. Reliability of the frequency distributions

3. Results

In order to ensure reproducible results, data on inter-observer reliability was collected using a partial sample of 50 at-fault drivers. A novice observer received an observer training with explanation of the definition rules and the processing of example cases and then classified the data from the partial sample according to the preassigned definition rules in the classification table.  was used as a measure to calculate the inter-observer agreement. The results are shown in Table 4. The missing data concerning forgetfulness, omission, and processing deficits relates to missing assignment of the categories. Thus the raters agreed perfectly that these factors of influence were not important in any of the cases. For the driver errors caused by sight obstruction, masked stimuli, distraction, and disregard/infringements there was an (almost) perfect agreement ( > 0.8, Landis and Koch, 1977). For reduced activity, incorrect focus, reduction of information processing, misjudgements, and objective slips/incorrect responses there is substantial agreement (0.8 >  > 0.6). The only error category with fair agreement is errors in orientation (0.2 >  > 0.4). This is probably due to the fuzzy definition rules concerning this category, particularly compared to errors through reduction of information processing.

3.1. Which factors influence traffic accidents? The results for answering this question are summarised in Table 5. It should be noted that this is a purely positive representation, i.e. cases were not included if either there were no manifestations of the corresponding error category or there was not enough information for classifying the corresponding error category. In the case of crossroad accidents, errors correlated with accident causation are found particularly as a result of sight obstruction (40%), distractions (32%), masked stimuli (26%), reduced activity (28%), and attention focused inaccurately (30%). The other error categories played a less important role. Many of the lane departure crashes were caused by errors as a result of masked stimuli (26%), errors through distraction (41%), reduced activity (47%), infringements (24%), expectation errors (35%), and objective slips and incorrect responses (23%). Same direction accidents were primarily related to errors as a result of masked stimuli (22%), errors caused by distraction (38%), reduced activity (29%), attention being focused inaccurately (24%),

Table 7 Detailed factors correlated with causation of lane departure accidents. Driver error

Precise details

n

% [95% CI]

Masking stimuli (29 cases)

Darkness Rain/snow/fog Glare (headlights and sun)

25 4 5

0.86 [0.69–0.95] 0.14 [0.05–0.31] 0.17 [0.08–0.35]

Distraction (46 cases)

Secondary activities Negative thoughts/emotions

21 28

0.46 [0.32–0.60] 0.61 [0.46–0.74]

Reduced activity (52 cases)

Drowsiness, fatigue Physical breakdown Alcohol Medication

32 14 7 12

0.62 [0.48–0.74] 0.27 [0.17–0.40] 0.13 [0.07–0.25] 0.23 [0.14–0.36]

Infringements (26 cases)

Speed Alcohol, drugs

21 7

0.81 [0.62–0.91] 0.27 [0.14–0.46]

Expectation errors (39 cases)

Expectation error concerning the road conditions Expectation error concerning the right of way

38 1

0.97 [0.87–1.00] 0.03 [0.00–0.13]

Objective slips/incorrect responses (33 cases)

Incorrect objective Oversteering reactions

8 25

0.24 [0.13–0.41] 0.76 [0.59–0.87]

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M. Staubach / Accident Analysis and Prevention 41 (2009) 1025–1033

Table 8 Detailed factors correlated with causation of same direction accidents. Driver error

Precise details

Masking stimuli (12 cases)

Darkness Glare (sun)

Distraction (21 cases)

n

% [95% CI] 9 3

0.75 [0.47–0.91] 0.25 [0.09–0.53]

Secondary activities Negative thoughts/emotions

13 15

0.62 [0.41–0.79] 0.71 [0.50–0.86]

Reduced activity (16 cases)

Drowsiness, fatigue Medication

11 4

0.69 [0.44–0.86] 0.25 [0.10–0.49]

Incorrect focus (13 cases)

Focus on third vehicle Focus on another object

10 3

0.77 [0.50–0.92] 0.23 [0.08–0.50]

Expectation errors (20 cases)

Expectation error concerning the road conditions Expectation error concerning the behaviour of other road users

4 13

0.20 [0.08–0.42] 0.65 [0.43–0.82]

Objective slips/incorrect responses (21 cases)

Safe distance Incorrect objective

14 6

0.67 [0.45–0.83] 0.29 [0.14–0.50]

expectation errors (36%), and objective slips or incorrect actions taken (36%). Tables 6–8 show detailed descriptions of the various correlating factors concerning the different accident type groups. Table 6 points out that, in the case of crossroad accidents, sight obstruction relates particularly to other vehicles in flowing traffic or parked on the side of the road (56%). There are fewer cases of sight obstruction caused by buildings and plants on the edge of the road (33%) as well as by the driver’s own vehicle (12%), e.g. roof pillars. Masked stimuli were described as sun glare for the driver in half of all cases (52%). The other half is divided between conditions faced when driving in the dark (23%) and heavy rain-related masked stimuli (20%). In terms of distractions, two main groups can be identified: distractions caused by secondary activities (42%) and negative thoughts or emotions, such as stress (70%). Reduced activity was attributed in 35% of cases to what the respondent cited as drowsiness as well as fatigue and in 67% of cases as taking medication because of medical impairments such as cardiovascular disease, hypertension or diabetes. In the case of errors related to attention being focused inaccurately, third vehicles played a part on the one hand (69%) and traffic-relevant objects, such as information signs (29%) on the other. The most frequent violations committed by those who caused the accident were disregard of the right of way (37%), overtaking when the traffic situation was unclear (11%), and incorrect turning (16%). Lane departure crashes frequently occurred where there were masked stimuli conditions, such as darkness (86%). Heavy rain, snow or fog (14%) and glare by headlights of oncoming cars or sun (16%) were less often of significance. Distractions can be divided into secondary activities (46%) and negative thoughts or emotions (61%). Reduced activity of the driver involved in the accident was caused in particular by drowsiness, fatigue and falling asleep (62%), physical breakdown (27%), e.g. as a result of a hyperglycaemic shock, the consumption of alcohol (13%) or medication (23%). Violations in lane departure crashes resulted mainly from disregard for the

permissible top speeds (81%) as well as driving under the influence of alcohol or drugs (27%), whereby these are no longer counted in the subsequent analysis, because the sub-category has appeared twice (activity and infringements). Expectation errors concerned basically the road conditions, such as slippery or wet roads (97%). Most of the accidents which were coded as objective and action slips happened after an overreaction of the driver when steering back into lane after having moved out of it (76%). In contrast, having an incorrect objective (24%) led to, for example, the decision to make risky evasion manoeuvres, which resulted in skidding, instead of braking to avoid a threat of a rear-end collision on a motorway. For same direction accidents masked stimuli were relevant, in particular in form of darkness (75%) and sun glare (25%). Distractions in the form of secondary activities (62%) and negative thoughts and emotions (71%), which often appeared as post-work stress, also played an important role. In the same context, those affected often spoke of drowsiness and fatigue (69%). Further, taking medication contributed in some cases to a reduced level of activity (25%). As is the case for crossroad accidents, errors caused by attention being focused inaccurately either on third vehicles on the road (77%) or on other traffic-relevant objects (23%) were also of significance. The most frequently used sub-categories for expectation errors were expectation errors concerning the behaviour of other road users (76%) and expectation errors concerning the road conditions (24%). In terms of objective and action slips, mention was frequently made of drivers not keeping a safe distance (67%) and those involved opted for an incorrect manoeuvre, e.g. swerving or steering instead of braking (29%). 3.2. Of the various factors of influence, how high is the relative risk in contributing to a traffic accident? A logistic regression model has been applied in order to identify risk parameters for the causation of traffic accidents. In Table 9, the risk parameters for crossroad accidents are presented. Objec-

Table 9 Risk parameters for the causes of crossroad accidents. Variables of the equation

Regr. coef., B

SD

Wald

df

Sig.

Exp(B)

95% CI

Masking stimuli Distraction Reduced activity Incorrect focus Disregard/infringements Expectation errors Sex Age under 25 years Age over 65 years Exposure

1.31 0.83 0.53 2.41 −0.11 3.29 0.45 0.32 −0.02 1.09

0.40 0.36 0.44 0.58 0.52 1.05 0.36 0.48 0.56 0.54

10.86 5.32 1.44 17.24 0.04 9.75 1.55 0.46 0.00 4.09

1 1 1 1 1 1 1 1 1 1

0.00 0.02 0.23 0.00 0.83 0.00 0.21 0.50 0.97 0.04

3.69 2.29 1.70 11.11 0.90 26.85 1.56 1.38 0.98 2.96

1.70–8.03 1.13–4.64 0.71–4.06 3.57–34.62 0.32–2.47 3.40–211.72 0.77–3.15 0.54–3.51 0.33–2.93 1.03–8.84

M. Staubach / Accident Analysis and Prevention 41 (2009) 1025–1033

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Table 10 Risk parameters for the causes of lane departure accidents. Variables of the equation

Regr. coef., B

SD

Wald

df

Sig.

Exp(B)

95% CI

Masking stimuli Distraction Reduced activity Objective slips Sex Age under 25 years Age over 65 years Exposure

0.01 1.78 1.70 2.69 −0.11 1.69 −1.16 0.21

0.55 0.61 0.55 1.10 0.52 0.87 0.83 0.75

0.00 8.41 9.42 5.98 0.05 3.81 1.96 0.08

1 1 1 1 1 1 1 1

0.99 0.00 0.00 0.01 0.83 0.05 0.16 0.77

1.01 5.94 5.47 14.73 0.89 5.42 0.31 1.24

0.34–2.98 1.78–19.79 1.85–16.17 1.71–127.14 0.32–2.48 0.99–29.51 0.06–1.59 0.29–5.36

tive errors (n = 3) and misjudgements (n = 9) had to be excluded from the analysis due to their small number. The results show that masking stimuli factors, distraction, incorrect focus, and expectation errors were attributed more frequently to the at-fault drivers than the innocently involved drivers. In addition, the risk of causing an accident at intersections with less driving experience (i.e. a total distance covered of less than 8000 km/year) was increased by a factor of 2.96. Errors through reduced activity and infringements were committed by at-fault drivers as well as innocently involved drivers. Table 10 shows the risk parameters for lane departure accidents. The driver errors caused by an incorrect focus, misjudgements, infringements, and expectation errors had to be excluded from the analysis, because there were no cases in which the innocently involved drivers committed these errors. While the first two occurred only rarely in the sample of at-fault drivers, infringements and expectation errors were significantly associated with an increased risk of accident causation. Distraction, errors through reduced activity, and objective slips were also significantly associated with an increased risk of accident causation and there were also effects of driver experience. Drivers under the age of 25 years had an above 5-fold increased risk of causing an accident. The risk parameters for same direction accidents are presented in Table 11. The driver errors caused by an incorrect attention focus, misjudgements, and infringements were excluded from the analysis because there were no cases in which these errors were committed by the innocently involved drivers. Amongst those, focus errors were significantly associated with accident causation as well as distraction, expectation errors and objective slips. There were no significant differences between the groups concerning masking stimuli and reduced activity. The age of the drivers and the exposure did also not contribute to distinguish at-fault drivers and innocently involved drivers. 4. Discussion and derivation of requirements for driver assistance systems After this representation of numerous individual results, it is now possible to make a statement about how drivers should best be supported in order to avoid accidents. Thus it must be possible to avoid accidents which are caused by multiple influences if one of these influences is removed.

As expected, in the case of crossroad accidents errors in the course of information assimilation are of particular importance. In addition to errors resulting from distractions, which various other studies have already been able to prove, many errors were caused by sight obstruction, particularly by other vehicles. ADAS should support the driver in assimilating information, e.g. by the use of sensors and cameras which help to recognise other vehicles in oncoming traffic or cross-traffic at an earlier stage. However, the systems should also be in a position to “see through” a sight obstruction, such as other vehicles. Another way of offsetting the influence of impaired visibility would be the systems which function by way of direct communication with other vehicles or via infrastructure. In the case of those crossroad accidents studied, further frequent errors occurred because attention was focused inaccurately. The average age of those participants who committed this error is significantly higher than the average age of those who caused the accident without focus errors (T = 1.97, p = 0.05). In addition, coded reduced activity is often found in conjunction with medical impairments like cardiovascular disease or diabetes, which was mainly found to be the case with older drivers who caused accidents (T = 5.381, p = 0.00). Other studies reported similar results (Dionne et al., 1995; McGwin et al., 2000; Vernon et al., 2002). However, the direction of the effect is not definite. It is not clear whether the medical impairments influence how information is processed or whether both are simply a result of age. Another explanation might be a different exposure of drivers with medical impairments compared to drivers without medical impairments: in this study drivers with medical impairments drove significantly less (T = −3.707, p = 0.00) kilometres (M = 13,962 km, SD = 10,496) than drivers without medical impairments (M = 22,215 km, SD = 20,344). Lyman et al. (2001) also found that medical impairments contribute to a reduction of mobility in older drivers. All these results point to an increased risk of older drivers at intersections as a result of a decrease in their capacity to process information. When designing warnings, possible longer reaction times for older drivers need to be taken into consideration. In the case of lane departure accidents, errors caused by reduced activity played an important part. Influencing factors in these cases were drowsiness and alcohol. ADAS should be able to reliably recognise this and correspondingly warn or possibly even prevent driving while drowsy or under the influence of alcohol. Another consideration is objective errors: reports frequently mentioned that drivers

Table 11 Risk parameters for the causes of same direction accidents. Variables of the equation

Regr. coef., B

SD

Wald

df

Sig.

Exp(B)

95% CI

Masking stimuli Distraction Reduced activity Objective slips Expectation errors Sex Age under 25 years Age over 65 years Exposure

1.17 2.09 0.29 3.85 3.02 1.01 −0.67 −0.43 1.40

0.92 0.66 0.74 1.01 0.89 0.73 1.08 0.87 1.08

1.61 9.92 0.16 14.47 11.52 1.91 0.38 0.24 1.68

1 1 1 1 1 1 1 1 1

0.20 0.00 0.69 0.00 0.00 0.17 0.54 0.62 0.19

3.22 8.12 1.34 47.05 20.49 2.75 0.51 0.65 4.04

0.53–19.61 2.21–29.86 0.32–5.67 6.47–342.23 3.58–117.20 0.66–11.52 0.06–4.25 0.12–3.62 0.49–33.37

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M. Staubach / Accident Analysis and Prevention 41 (2009) 1025–1033

oversteered following lane departures. Today there are solutions in place, such as Lane Departure Warning or Heading Control Systems to help drivers stay in the driving lane. However, these systems are currently based on non-disrupted identification of side and middle markings. In this sample survey only 40% of cases had clearly recognisable middle and side markings. ESC (Electronic Stability Control) can also currently help in offering counter-steering assistance. However, those involved in accidents driving vehicles already fitted with ESC also report oversteering movements which resulted in road departure. This concerns in particular accidents involving drowsiness (2 = 4.91, p = 0.04). Same direction accidents were to a large extent caused by errors in assimilating driving information when following another vehicle. In addition to inattention, a combination of expectational and objective errors frequently contributed to the fact that people did not maintain a safe distance on the one hand and did not expect sudden braking by the vehicle travelling in front of them on the other. This is where the driver could be supported either by having his attention drawn to critical aspects through early warnings or through provision of an automated longitudinal guidance system in a city area, e.g. through Adaptive Cruise Control Systems which are able to slow vehicles in response to stopped vehicles ahead and to slow the car down to a standstill. Despite the many views on offering vehicle assistance for the driver which can be derived from accidents, further studies are still necessary in order to predict possible changes in behaviour (behavioural compensation) which are determined through regular use of driver assistance systems. Such behavioural changes have previously been associated with ADAS like antilock brakes (Sagberg et al., 1997). The extent of changes in behaviour is dependent on the conspicuity of safety measures (Underwood et al., 1993) and considering ADAS particularly on the design of the human–machine interface (Weller and Schlag, 2004). Broad testing of such systems is necessary in a driving simulator and on real journeys so that the safety potential of ADAS is not offset by driver behaviour. Standards (ISO 17287) and guidelines are already in place, such as the Code of Practice for the Design and Evaluation of ADAS (2006) compiled in Project Response. The same applies to the effects of habituation on driver assistance systems and high familiarisation with the functionality of systems. For example, a warning about cyclists when turning right could lead to catastrophic results if the act of looking over your shoulder was simply ‘unlearnt’ as a result and consequently forgotten even in vehicles not fitted with this kind of warning. This has to be avoided by means of ergonomic design principles and measures of driving education which emphasise the driver’s individual responsibility. 5. Conclusions and future prospects The present study shows that accident research information, especially a combination of driver interviews with accident site investigation and technical reconstruction, is a useful source for the design and development of ADAS. Now and in the future there is a huge potential for systems which are capable of compensating for sight obstructions, guide the drivers attention to critical aspects, help to detect activity deficits, support the driver to keep safe distances to cars in front, as well as to avoid lane departure. However, the study showed some deficits using error classification concepts in traffic accident research, which have to be overcome. Thus the categories ‘reductions of information processing’ and ‘orientation and expectation errors’ were not always clearly distinguishable. Therefore they were merged in this study. Further, the categories ‘omissions’ and ‘processing deficits’ were not used very often and hence seem to play an unimportant role in traffic accident research. In a next step it would also be important to look for systematic or frequent relations between the single influence factors in order

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