Assessing the potential impacts of connected vehicle systems on Driver’s situation awareness and driving performance

Vehicles equipped with connected vehicle technologies are able to communicate with each other and with infrastructures. Compared to Advanced Driving Assistance Systems (ADAS) using camera systems and sensor technologies, the Connected Vehicle Systems (CVS) leverage the wireless communication networks to detect hazards with a greater range, alert drivers of hazards much earlier, and therefore enhance driving safety. However, drivers’ reliance on the CVS to detect critical situations could negatively affect them maintaining situation awareness (SA) in noncritical situations when no warning is issued by the CVS. The present study conducted a driving simulator experiment with 40 participants to investigate the effect of connected vehicle systems on driver SA in normal, noncritical driving scenarios after they were exposed to the CVS with different designs of collision warning lead time (3 s, 6 s, and no warnings). After drivers experienced the CVS-supported warnings with the assigned design of lead time in critical situations, driver SA was measured in normal driving conditions using the freeze probe technique. Results revealed that drivers who experienced the CVS with early warnings (6 s) showed lower SA for normal driving events compared to those who experienced the CVS with late warnings (3 s) or no warnings. Although early warnings of CVS brought more safety benefits to drivers in critical situations, the degraded driver SA due to drivers’ reliance on such warning systems could endanger drivers when a system failure occurred. These findings highlight the importance of balancing the effects of warning lead time on driver SA and driving performance in designing connected vehicle systems.     

What happens when drivers of automated vehicles take over control in critical lane change situations?

According to legislation, take-overs initiated by the driver must always be possible during automated driving. For example, when drivers mistrust the automation to handle a critical and hazardous lane change, they might intervene and take over control while the automation is performing the maneuver. In these situations, drivers may have little time to avoid an accident and can be exposed to high lateral forces. Due to lacking research, it is yet unknown if they recognize the criticality of the situation and how they behave and perform to manage it. In a driving simulator study, participants (N = 60) accomplished eight double lane changes to evade obstacles in their lane. Time-to-collision and traction usage were varied to establish different degrees of objective criticality. To manipulate these parameters as required, participants were triggered to take over control by an acoustic cue. This setting shows what might happen if drivers disable the automation and complete the maneuver themselves. The results of the experiment demonstrate that drivers rated objectively more critical driving situations as more critical and responded to the hazard very fast over all experimental conditions. However, their behavior was more extreme with respect to decelerating and steering than necessary. This impaired driving performance and increased the risk of lane departures and collisions. The results of the experiment can be used to develop an assistance system that supports driver-initiated take-overs.     

Physiological indicators of driver workload during car-following scenarios and takeovers in highly automated driving

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation (SAE Level 3) or monitored the drive (SAE Level 2). Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ∼ 18 min each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. Results showed that driver workload due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that a lead vehicle maintain a shorter THW can significantly increase driver workload during takeover scenarios, potentially affecting driver safety. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional cognitive or attentional demands on the driver. Our results indicated that ECG and EDA signals are sensitive to variations in workload, which warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help future driver monitoring systems respond appropriately to the limitations of the driver, and predict their performance in the driving task, if and when they have to resume manual control of the vehicle after a period of automated driving.     

Modelling the influence of time pressure on reaction time of drivers

Speeding is a major traffic violation and time pressure is one of the leading contributors to speeding. High-speed driving requires an immediate response to perilous events from the driver to avoid a crash. Reaction time is one of the important driving performance measures to assess the driver’s response to the event. Therefore, the current study examined the influence of time pressure on reaction times of the drivers measured for two different perilous events (pedestrians crossing and obstacle overtaking). Eighty-five Indian licensed drivers participated in a driving simulation study designed for three different time pressure conditions: No Time Pressure (NTP), Low Time Pressure (LTP), and High Time Pressure (HTP). The survival analysis technique was used to model the effect of time pressure and driver characteristics with reaction times of the drivers. It was observed that drivers’ reaction times decreased by 18% and 9% in LTP and 28% and 16% in HTP during the pedestrians crossing and obstacle overtaking events, respectively. Further, 1 m/second increase in approach speed resulted in 2% and 4% reduction in reaction times of the drivers in pedestrians crossing and obstacle overtaking events, respectively. Young drivers responded 21% faster than mature drivers during the pedestrians crossing event. Interestingly, sleeping hours and physical fitness played an important role in driver’s reaction to the events. The drivers performing regular physical exercise and having minimum eight-hours of overnight sleep reacted 16% and 17% earlier in pedestrians crossing and obstacle overtaking events, respectively. The overall findings from this study showed enhanced stimulus-response behaviour of the drivers under time pressure driving conditions. The results obtained from the study can give new insight into various safety-related ITS applications.     

Measured increases in steering entropy may predict when performance will degrade: A driving simulator study

We investigated the effect of time-on-task on driver’s mental workload and driving performance during a simulated driving task. The extent of mental workload was estimated from steering entropy, while driver performance was measured from the Standard Deviation of Lateral Position (SDLP) and the Standard Deviation (SD) Sterring Angle. Seventeen participants underwent a simulated highway driving task for 60 min. The results show that mental workload increased significantly after 15 min, whereas driving performance did not degrade until 30 min. These results suggest that when drivers first affected by time-on-task, they can cope with the situation by increasing mental effort investment and can manage to perform normally for a while (15 min). Since changes in steering entropy precede changes in driving performance, this measure of mental workload may have utility as a predictor of increased accident risk.     

Evaluation of motorized two-wheeler rider responses towards jaywalking pedestrians through mockup control studies for urban streets

This study aims to model the motorized two-wheeler (MTW) riders’ evasive-action behavior towards jaywalking pedestrians using a mockup study. The brake reaction times (BRTs), approach speeds, decelerations, headings, and yaw rates were analyzed for two surprise scenarios (scenarios 1 and 3), one stationary scenario (scenario-2), and one expected scenario (scenario-4). In total, 50 riders participated in the mockup study. The results revealed that the 90th percentile BRT for the expected and surprise scenarios were 3.6 and 1.6 s, respectively. Further, repeated-measures ANOVA was performed followed by mixed effect modeling to ascertain the effect of conflict severity (two groups: group-1 with Time to Collision (TTC) < 1.5 s and group-2 with TTC > 1.5 s) and scenario type (three groups: scenarios 1, 3 and 4) on BRT. The results indicated that the main effects were significant while the interaction effect was not significant. The positive and significant coefficient (0.32) of TTC group-2 indicated higher BRTs than TTC group-1. Considering scenario-1 as the base scenario, the coefficient of scenario 3 (-0.02) indicated that scenario-1 and scenario-3 had a similar effect on BRT, while the coefficient of scenario-4 (1.47) indicated higher BRTs compared to scenario-1. The analysis of evasive action behavior revealed that 32% of riders performed hard braking in surprise scenarios. Further, yaw rate values at the crossing point indicated a loss of control of MTW in 90% of surprise events. The observations from this study provide a basis for developing countermeasures to improve pedestrian and MTW rider safety.     

How impressions of other drivers affect one’s behavior when merging lanes

In recent years, systems have been developed to realize automatic driving based on objective information such as the relative distance and relative speed between vehicles. However, humans still must drive in complex situations, for instance, when merging lanes. In such driving situations, it is possible that people make decisions based not only on objective information, but also on subjective information. This study examined how subjective information, specifically, a driver’s impression of the other vehicle, affects the decision to merge in front of or behind the other vehicle when merging lanes on a highway. Twenty participants (n_male = 10; n_female = 10; M_age = 43.92 [SD_age = 11.40]) joined two experiments, Days 1E and 2E, using a driving simulator. Two months after participating in Day 1E the participants joined Day 2E. In the Day 1E, they drove either on the merging lane or the main lane and merged lanes while considering the other vehicle driving along the adjacent lane. This experiment measured the probability that the participants drove in front of another vehicle upon merging, which is defined as “lead probability.” The Day 2E was similar to 1E, except for the manipulation of the participants’ impression of the other vehicle as being aggressive/cautious via acceleration/deceleration of the other vehicle, and through the contents of the instructions regarding the other vehicle’s driving characteristics. In the Day 2E, the participants were randomly assigned to two: Aggressive or Cautious conditions. As the result of comparing the lead probabilities, it was found that only when the participants were driving on the merging lane and had the impression that the other vehicle is aggressive, the impression lowered the lead probability. The result indicates that people make decisions based not only on objective information but also on subjective information for specific driving situations, such as merging lanes. These findings can help in the development of automated driving systems that allow safer merging.     

Controllability of Partially Automated Driving functions – Does it matter whether drivers are allowed to take their hands off the steering wheel?

Different motor vehicle manufacturers have recently introduced assistance systems that are capable of both longitudinal and lateral vehicle control, while the driver still has to be able to take over the vehicle control at all times (so-called Partial Automation). While these systems usually allow hands-free driving only for short time periods (e.g., 10 s), there has been little research whether allowing longer time periods of hands-off driving actually has a negative impact on driving safety in situations that the automation cannot handle alone. Altogether, two partially automated assistance systems, differing in the permitted hands-off intervals (Hands-off system vs. Hands-on system, n = 20 participants per assistance condition, age 25–70 years) were implemented in the driving simulation with a realistic take-over concept. The Hands-off system is defined by having a permitted hands-off interval of 120 s, while the Hands-on system is defined by a permitted hands-off interval of 10 s. Drivers’ reactions at a functional system limit were tested under conditions of high ecological validity: while driving in a traffic jam, participants unexpectedly encountered a time-critical situation, consisting of a vehicle at standstill that appeared suddenly and required immediate action. A visual-auditory take-over request was issued to the drivers. Regardless of the hands-off interval, all participants brought the vehicle to a safe stop. In spite of a stronger brake reaction with the Hands-on system, no significant differences between assistance levels were found in brake reaction times and the criticality of the situation. The reason for this may be that most of the drivers kept contact with the steering wheel, even in the Hands-off condition. Neither age nor prior experience with ACC was found to impact the results. The study thus demonstrates that permitting longer periods of hands-off driving does not necessarily lead to performance deficits of the driver in the case of take-over situations, if a comprehensive take-over concept is implemented.     

Driving simulator validation with hazard perception

How should we assess the comparability of driving on a road and “driving” in a simulator? If similar patterns of behaviour are observed, with similar differences between individuals, then we can conclude that driving in the simulator will deliver representative results and the advantages of simulators (controlled environments, hazardous situations) can be appreciated. To evaluate a driving simulator here we compare hazard detection while driving on roads, while watching short film clips recorded from a vehicle moving through traffic, and while driving through a simulated city in a fully instrumented fixed-base simulator with a 90 degree forward view (plus mirrors) that is under the speed/direction control of the driver. In all three situations we find increased scanning by more experienced and especially professional drivers, and earlier eye fixations on hazardous objects for experienced drivers. This comparability encourages the use of simulators in drivers training and testing.     

The power and sensitivity of four core driver workload measures for benchmarking the distraction potential of new driver vehicle interfaces

This study evaluated the power and sensitivity of several core driver workload measures in order to better understand their use as a component of future driver distraction potential evaluation procedures of the in-vehicle human machine interface (HMI). Driving is a task that requires visual, manual and cognitive resources to perform. Secondary tasks, such as mobile phone use and interaction with in-built navigation, which load onto any of these three processing resources increase driver workload and can lead to impaired driving. Because workload and distraction potential are interrelated, a comprehensive method to assess driver workload that produces valid and predictive results is needed to advance the science of distraction potential evaluation. It is also needed to incorporate into New Car Assessment Program (NCAP) testing regimes. Workload measures of cognitive (DRT [Detection Response Task] Reaction Time), visual (DRT Miss Rate), subjective (NASA-TLX [driver workload questionnaire]), and temporal demand (Task Interaction Time) were collected as participants drove one of 40 vehicles while completing a variety of secondary tasks with varying interaction requirements. Of the evaluated measures, variance and power analyses demonstrated that Task Interaction Time is the most sensitive in detecting differences in driver workload between different in-vehicle HMIs, followed by DRT Miss Rate, NASA-TLX and finally DRT Reaction Time. There were relatively weak correlations between each of the four measures. These results suggest that Task Interaction Time, coupled with a reliable visual demand metric such as DRT Miss Rate, eye glance coding, or visual occlusion, more efficiently detect differences in driver workload between different HMIs compared to DRT Reaction Time and the NASA-TLX questionnaire. These results can be used to improve the understanding of the utility of each of these core driver workload measures in assessing driver distraction potential.     

A matter of style? Driver attributional ‘style’ in accounting for the driving of others as protective or as predisposing drivers towards retaliatory aggressive driving

Driver cognitions about aggressive driving of others are potentially important to the development of evidence-based interventions. Previous research has suggested that perceptions that other drivers are intentionally aggressive may influence recipient driver anger and subsequent aggressive responses. Accordingly, recent research on aggressive driving has attempted to distinguish between intentional and unintentional motives in relation to problem driving behaviours. This study assessed driver cognitive responses to common potentially provocative hypothetical driving scenarios to explore the role of attributions in driver aggression. A convenience sample of 315 general drivers 16–64 yrs (M = 34) completed a survey measuring trait aggression (Aggression Questionnaire AQ), driving anger (Driving Anger Scale, DAS), and a proxy measure of aggressive driving behaviour (Australian Propensity for Angry Driving AusPADS). Purpose designed items asked for drivers’ ‘most likely’ thought in response to AusPADS scenarios. Response options were equivalent to causal attributions about the other driver. Patterns in endorsements of attribution responses to the scenarios suggested that drivers tended to adopt a particular perception of the driving of others regardless of the depicted circumstances: a driving attributional style. No gender or age differences were found for attributional style. Significant differences were detected between attributional styles for driving anger and endorsement of aggressive responses to driving situations. Drivers who attributed the on-road event to the other being an incompetent or dangerous driver had significantly higher driving anger scores and endorsed significantly more aggressive driving responses than those drivers who attributed other driver’s behaviour to mistakes. In contrast, drivers who gave others the ‘benefit of the doubt’ endorsed significantly less aggressive driving responses than either of these other two groups, suggesting that this style is protective.     

Spatio-temporal accident analysis for accident prevention in relation to behavioral factors in driving: The case of South Anatolian Motorway

Analyzing the pattern of traffic accidents on road segments can highlight the hazardous locations where the accidents occur frequently and help to determine problematic parts of the roads. The objective of this paper is to utilize accident hotspots to analyze the effect of different measures on the behavioral factors in driving. Every change in the road and its environment affects the choices of the driver and therefore the safety of the road itself. A spatio-temporal analysis of hotspots therefore can highlight the road segments where measures had positive or negative effects on the behavioral factors in driving. In this paper 2175 accidents resulted in injury or death on the South Anatolian Motorway in Turkey for the years between 2006 and 2009 are considered. The network-based kernel density estimation is used as the hotspot detection method and the K-function and the nearest neighbor distance methods are taken into account to check the significance of the hotspots. A chi-square test is performed to find out whether temporal changes on hotspots are significant or not. A comparison of characteristics related driver attributes like age, experience, etc. for accidents in hotspots vs. accidents outside of hotspots is performed to see if the temporal change of hotspots is caused by structural changes on the road. For a better understanding of the effects on the driver characteristics, the accidents are analyzed in five groups based on three different grouping schemes. In the first grouping approach, all accident data are considered. Then the accident data is grouped according to direction of the traffic flow. Lastly, the accident data is classified in terms of the vehicle type. The resultant spatial and temporal changes in the accident patterns are evaluated and changes on the road structure related to behavioral factors in driving are suggested.     

Drivers Display Anger‐Congruent Attention to Potential Traffic Hazards

Previous research has suggested that angry drivers may respond differently to potential hazards. This study replicates and extends these findings. Under simulated driving conditions, two groups of drivers experienced conditions that would either increase angry mood (N = 12; men = 6) or not (control group, N = 12; men = 6). All drivers then performed a neutral drive, during which they encountered a number of traffic events not experienced in the initial drive. These included vehicles emerging from driveways into their path and jaywalking pedestrians. Subjective anger, eye‐movement behaviour and driving behaviours (speed and reaction times) were measured as drivers drove. Subjective moods (Profile of Mood States) were assessed before and after each drive. Anger‐provoked drivers reported reliably higher increases in angry mood when compared with the control group after the initial drive, and these increases remained stable across the subsequent neutral drive. During the neutral drive, anger‐provoked drivers demonstrated evidence of more heuristic style processing of potential hazards, with shorter initial gazes at less apparent hazards and longer latencies to look back at jaywalking pedestrians obscured by parked vehicles. Anger‐provoked drivers also took longer to make corrective actions to avoid potential collisions. It is concluded that anger‐provoked drivers may initially make more superficial assessments of certain driving situations and consequently underestimate the inherent risk. Copyright © 2012 John Wiley & Sons, Ltd.     

Keeping the driver in the loop in conditionally automated driving: A perception-action theory approach

In this paper we investigated if keeping the driver in the perception–action loop during automated driving can improve take-over behavior from conditionally automated driving. To meet this aim, we designed an experiment in which visual exposure (perception) and manual control exposure (action) were manipulated. In a dynamic driving simulator experiment, participants (n = 88) performed a non-driving related task either in a head-up display in the windshield (high visual exposure) or on a head-down display near the gear shift (low visual exposure). While driving, participants were either in an intermittent control-mode with four noncritical take-over situations (high manual control exposure), or in a continuous automation-mode throughout the ride (low manual control exposure). In all conditions, a critical take-over had to be carried out after an approximately 13 min ride. Measurements of take-over behavior showed that only high visual exposure had an effect on hands-on reaction time measurements. Both visual exposure and manual control exposure had small to medium sized main effects on time to system deactivation, the maximum velocity of the steering wheel, and the standard deviation of the steering wheel angle. The combined high visual – and high manual control exposure condition led to 0.55 s faster reaction time and 37% less steering variability in comparison to the worst case low visual – and low manual control exposure condition. Together, results corroborate that maintaining visual exposure and manual control exposure during automated driving can be efficacious and suggest that their positive effects are additive.     

Influence of following vehicle’s tailway and classification on subject driver’s response to the circular yellow indication

Traffic crashes at signalized intersections are frequently linked to driver behavior at the onset of the circular yellow (CY) indication. To better understand behavioral factors that influence a driver’s decision to stop or go at an intersection, this study analyzed the behavior of the driver of a subject vehicle at the onset of the CY indication. Driver performance data from 53 participants were collected in the Oregon State University Driving Simulator, simulating scenarios of driving through high-speed intersections under various conditions. Data included interactions where the driver stopped at the stop line (n = 644) or proceeded through the intersection (n = 628) in response to a CY indication. Data were analyzed as panel data while considering 12 indicator variables related to the driver’s stop/go decision. These indicator variables included time to stop line (TTSL), tailway time, following vehicle type, vehicle speed at the onset of the CY indication, and demographics (age, gender, driving experience, level of education, personal vehicle type, number of times driving per week, number of miles driving last year, participation in previous simulation studies. A random-parameter binary logit model was used to determine contributing factors for driver decision making at the onset of CY indication while accounting for unobserved heterogeneity. Four indicator variables were significantly related to the driver’s stop/go decision, but three factors varied across observations. Findings showed that a driver’s stop/go decision in response to a CY indication was associated with the time to the stop line (TTSL), tailway time to the following vehicle, subject vehicle speed at the onset of the CY indication, and driver’s age (20–36 years), but was not significantly associated with classification of the following vehicle. Also, the findings indicated that a shorter tailway increased a subject driver’s red-light running frequency. These findings provide insights into variables that affect driver decisions in a vehicle-following situation at the onset of the CY indication. This information can help make better decisions in smart traffic control systems such as to extend/decrease the green interval slightly to avoid decisions that are more difficult.     

Eye movements predict driver reaction time to takeover request in automated driving: A real-vehicle study

For automated driving at SAE level 3 or lower, driver performance in responding to takeover requests (TORs) is decisive in providing system safety. A driver state monitoring system that can predict a driver’s performance in a TOR event will facilitate a safer control transition from vehicle to driver. This experimental study investigated whether driver eye-movement measured before a TOR can predict driving performance in a subsequent TOR event. We recruited participants (N = 36) to obtain realistic results in a real-vehicle study. In the experiment, drivers rode in an automated vehicle on a test track for about 32 min, and a critical TOR event occurred at the end of the drive. Eye movements were measured by a camera-based driver monitoring system, and five measures were extracted from the last 2-min epoch prior to the TOR event. The correlations between each eye-movement measure and driver reaction time were examined, and a multiple regression model was built using a stepwise procedure. The results showed that longer reaction time could be significantly predicted by a smaller number of large saccades, a greater number of medium saccades, and lower saccadic velocity. The implications of these relationships are consistent with previous studies. The present real-vehicle study can provide insights to the automotive industry in the search for a safer and more flexible interface between the automated vehicle and the driver.     

Effects of explanation-based knowledge regarding system functions and driver’s roles on driver takeover during conditionally automated driving: A test track study

The present study was designed to examine the influence of explanation-based knowledge regarding system functions and the driver’s role in conditionally automated driving (Level 3, as defined in SAE J3016). In particular, we studied how safely and successfully drivers assume control of the vehicle when encountering situations that exceed the automation parameters. This examination was conducted through a test-track experiment. Thirty-two younger drivers (mean age = 37.3 years) and 24 older drivers (mean age = 71.1 years) participated in Experiments 1 and 2, respectively. Adopting a between-participants design, in each experiment the participants were divided into two age- and sex-matched groups that were given differing levels of explanation-based knowledge concerning the system limitations of automated driving. The only information given to the less-informed groups was that, during automated driving, drivers may be required to occasionally assume control of the vehicle. The well-informed groups were given the same information, as well as details regarding the auditory-visual alerts produced by the human–machine interface (HMI) during requests to intervene (RtIs), and examples of situations where RtIs would be issued. Ten and nine RtI events were staged for each participant in Experiment 1 and 2, respectively; the participants performed a non-driving-related task while the automated driving system was functioning. For both experiments it was found that, for all RtI events, more participants in the well-informed groups than the less-informed groups successfully assumed control of the vehicle. These results suggest that, in addition to providing information regarding the possible occurrence of RtIs, explanations of HMI and RtI-related situations are effective for helping both younger and older drivers safely and successfully negotiate such events.     

Personality and demographic differences in the perceived risks of potentially timid driving behaviours

Timid driving behaviours can be described as overly cautious and hesitant driving behaviours. Little research has examined behaviours that potentially resemble timid driving and how these behaviours are perceived by other drivers. This is despite the potential for these behaviours to be perceived in a way that leads to angry and aggressive retaliatory behaviours in some drivers (e.g., in anger-prone drivers). We conducted an online survey examining the perceived road safety risks of several behaviours that could potentially result from timid driving and their relationships with driver personality (trait anxiety, trait driving anger), behaviour (anxious driving, angry driving), and demographic (age, gender, annual mileage) background. Drivers (N = 439, M_age = 49.41 ± 5.59 years, aged 18–89) perceived excessively cautious and unpredictable braking behaviours as posing moderate levels of risk. Multiple linear regression analyses also indicated higher perceived risks of slow and excessively cautious behaviours in older, male, and anger prone drivers. No meaningful associations were found between driver characteristics and the risks of unpredictable braking behaviours. These results suggest that safety campaigns to reduce aggressive behaviour may benefit from targeting the perceptions of other drivers’ behaviours.     

Driver behaviour and traffic accident involvement among professional urban bus drivers in China

The present research aimed to investigate specific behaviors of professional urban bus drivers in China with the revised Driver Behavior Questionnaire (DBQ), and to define the relationships among various driving behaviors (errors, positives, inattention errors, violations), background information (age, years of driving experience, mobility, etc.), self-assessment, and traffic accident. To achieve such goals, the present research designed a four-dimensional DBQ with 20 items for professional urban bus drivers in China. The KMO coefficient of the whole scale was 0.835, and Bartlett’s test was statistically significant (p < 0.000), which demonstrated strong validity of the scale and should be suitable for factor analysis. The four loading factors accounted for 58.991%. In addition, the reliability and effectiveness of the present 20-item scales were measured. The coefficient of internal consistency-Cronbach’s alpha coefficient was 0.881 and the Cronbach’s Alpha Based on Standardized Items was 0.911. This showed that driving behavior scale of professional bus drivers in China was of high reliability and validity. The analysis showed that among the four factors, positive driving behaviors were significantly associated with errors, inattention errors and violations, respectively. Errors, inattention errors and violations correlated positively with each other. This verified that the correlation coefficient of each factor was medium and high, which indicated that the scale had good difference validity. The test content of the total scale was also highly consistent with the test content of each factor, which indicated that the revised scale had good standard related validity. According to the accident prediction model, the variables that significantly affected the occurrence of traffic accidents were daily driving time, positive driving behavior, SE2 (Driving safety), SE3 (Aberrant driver behaviors). The results showed that professional bus drivers often working overtime were most likely to have accidents. The probability of traffic accidents decreased by 53% for every unit of positive driving behavior frequency of professional bus drivers. The more they felt that they had the tendency of aberrant driving behavior, the more likely they were to have traffic accidents. To summary, the present research contributed to validating and improving the DBQ for professional urban drivers in China.     

The development and validation of a hazard perception test for Thai drivers

Thailand is a developing country with a high traffic accident fatality rate. However, few attempts have been made to understand crash risks in Thai drivers from a psychological perspective. The purpose of the present study was to develop and validate a latency-based hazard perception test for Thai drivers. The initial test comprised our full item pool of 77 clips containing traffic conflicts captured on video from the driver’s perspective on Thai roads. We evaluated the validity of this test by examining whether performance differed as a function of driving experience in a sample of 135 Thai drivers. We found that experienced drivers (n = 87) performed significantly better than novice drivers (n = 48) after adjusting for individual differences in computer mouse skill, mirroring crash risk differences between these groups. The final 30-item version of the test, which comprised the best items from the initial test, yielded novice/experienced driver differences with or without adjusting for computer mouse skill. These results offer preliminary support for the validity of the latency-based test as a measure of hazard perception ability in Thai drivers.