Drivers' Ratings of Different Components of Their Own Driving Skill: A Greater Illusion of Superiority for Skills That Relate to Accident Involvement1

Different components of driving skill relate to accident involvement in different ways. For instance, while hazard‐perception skill has been found to predict accident involvement, vehicle‐control skill has not. We found that drivers rated themselves superior to both their peers and the average driver on 18 components of driving skill (N= 181 respondents). These biases were greater for hazard‐perception skills than for either vehicle‐control skills or driving skill in general. Also, ratings of hazard‐perception skill related to self‐perceived safety after overall skill was controlled for. We suggest that although drivers appear to appreciate the role of hazard perception in safe driving, any safety benefit to be derived from this appreciation may be undermined by drivers' inflated opinions of their own hazard‐perception skill. We also tested the relationship between illusory beliefs about driving skill and risk taking and looked at ways of manipulating drivers' illusory beliefs.     

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.     

Analysis of the factors influencing overtaking in two-lane highways: A driving simulator study

BackgroundHuman factors are among the leading causes of frontal collision accidents. Therefore, understanding the factors that affect driver behaviour during overtaking is essential.ObjectiveThis research proposed a methodology to observe passing manoeuvres on two-lane highways in a driving simulator and to investigate the effects of the speed of an impeding vehicle, the type of the vehicle to be overtaken and the passing sight distance on the following gap distance as an indicator of driver behaviour.MethodThe repeated measures experiment allowed for 640 possible overtakings with a sample of 80 participants. The speed of the impeding vehicle, the type of the impeding vehicle and the passing sight distance were used as within-subject factors with eight treatments. The driver’s age, gender, and experience were considered the between-subject factors.ResultsWhen the speed of the impeding vehicle was 60 km/h, the participants adopted a following gap in passing sight distance of 446 m (M = 81.32 m), which was larger than the following gap in passing sight distance of 560 m (M = 70.84 m).ConclusionAmong the factors that were considered, the effect of the speed of an impeding vehicle on the following gap at the beginning of overtaking was higher than the effect of the type of the impeding vehicle or the passing sight distance. Together, these combination values can describe the driver behaviour and help to improve the standards-based design values to increase safety.     

Analysis of the factors affecting drivers’ queue-jumping behaviors in China

In China, drivers’ queue-jumping behaviors are very common. To explore the factors affecting drivers’ queue-jumping, first, a driving attitude scale, a risk perception scale and a queue-jumping behavior scale were designed, and an existing Type A behavior pattern scale and a driver skill scale were also introduced. Second, these scales were used to collect empirical data, and 202 valid samples were obtained. Third, the reliability and validity of the developed scales were verified, and a structural equation model of drivers’ queue-jumping behaviors was established to explore the interrelationships among the Type A personality trait, cognitions (including attitudes and risk perception), driver skills (including driving skill and safety motivation) and queue-jumping behaviors. The results showed that drivers’ cognitions significantly affected their queue-jumping behaviors; specifically, drivers’ attitudes toward queue-jumping and traffic violations positively correlated with their queue-jumping behaviors (β = 0.323, t = 3.470; β = 0.277, t = 3.072), while drivers’ risk perception toward queue-jumping negatively correlated with their queue-jumping behaviors (β = −0.297, t = −3.889). Drivers’ driving skill and safety motivation also significantly affected their queue-jumping behaviors (β = 0.198, t = 2.385; β = −0.355, t = −4.101). Although Type A personality trait had no significant effects on drivers’ queue-jumping behaviors, it had significant effects on drivers’ attitudes toward queue-jumping and traffic violations (β = 0.336, t = 4.720; β = 0.215, t = 2.873) and their risk perceptions toward queue-jumping (β = −0.232, t = −3.279). In other words, the Type A personality trait can indirectly affect drivers’ queue-jumping behaviors through drivers’ attitudes and risk perceptions toward queue-jumping. The results provided offer traffic administration departments a theoretical foundation for governing this behavior.     

Should older people be considered a homogeneous group when interacting with level 3 automated vehicles?

Exploring the future mobility of older people is imperative for maintaining wellbeing and quality of life in an ageing society. The forthcoming level 3 automated vehicle may potentially benefit older people. In a level 3 automated vehicle, the driver can be completely disengaged from driving while, under some circumstances, being expected to take over the control occasionally. Existing research into older people and level 3 automated vehicles considers older people to be a homogeneous group, but it is not clear if different sub-groups of old people have different performance and perceptions when interacting with automated vehicles. To fill this research gap, a driving simulator investigation was conducted. We adopted a between-subjects experimental design with subgroup of old age as the independent variable. The differences in performance, behaviour, and perception towards level 3 automated vehicles between the younger old group (60–69 years old) and older old group (70 years old and over) was investigated. 15 subjects from the younger old group (mean age = 64.87 years, SD = 3.46 years) and 24 from the older old group (mean age = 75.13 years, SD = 3.35 years) participated in the study. The findings indicate that older people should not be regarded as a homogeneous group when interacting with automated vehicle. Compared to the younger old people, the older old people took over the control of the vehicle more slowly, and their takeover was less stable and more critical. However, both groups exhibited positive perceptions towards level 3 automation, and the of older old people’s perceptions were significantly more positive. This study demonstrated the importance of recognising older people as a heterogeneous group in terms of their performance, capabilities, needs and requirements when interacting with automated vehicles. This may have implications in the design of such systems and also understanding the market for autonomous mobility.     

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.     

Key factors associated with Australian parents’ willingness to use an automated vehicle to transport their unaccompanied children

This study aimed to identify the key factors associated with Australian parents' willingness to use an automated vehicle to transport their unaccompanied child(ren). Seven hundred and seventy-five participants completed the online survey (M = 40.7 years, SD = 8.9 years, Range = 18.0–65.0 years; Female: 56.4%). Most participants reported that they would 'never' use an automated vehicle to transport their unaccompanied child(ren) (43.5%). The results of a logistic regression model showed that participants' age, gender, level of education, propensity for technology adoption, aberrant driving behaviours, awareness of advanced driver assistance safety (ADAS) technologies, perceived knowledge regarding automated vehicles, as well their requirements for assurance-related vehicle features were significantly associated with their willingness to use an automated vehicle to transport their unaccompanied child(ren), χ2(12) = 137.41, p < 0.001). Overall, the findings suggest that Australian parents are mostly unwilling to use an automated vehicle to transport their unaccompanied child(ren) unless various reassurance features and technologies (i.e., microphones, camera, ability to summon assistance if the vehicle breaks down) are embedded in the vehicle. Therefore, automated vehicle manufacturers are encouraged to consider these requirements when prototyping their designs so that all user groups, including child occupants, can benefit from the impending arrival of automated technologies.     

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.     

Hazard perception abilities of experienced and novice motorcyclists: An interactive simulator experiment

This study reports an experiment that compared the hazard perception abilities of experienced and novice motorcycle riders using an interactive, closed-loop, simulator. Participants (n = 49) were categorized into four groups: experienced motorcycle riders with full driver licence, inexperienced motorcycle riders with full driver licence, novice motorcycle riders with full driver licence, and novice motorcycle riders with probationary driver licence. The participants were tested on three scenarios, each consisting of eight hazardous events. They were instructed to ride normally, but to respond appropriately to avoid the hazards. Under certain conditions in the simulator, we found that experienced riders (relative to inexperienced or novice riders) crashed less often, received better performance evaluations, and approached hazards at more appropriate speeds. Interestingly, we also found that some novice riders were overconfident in their riding ability. We discuss how this overconfidence might be related to hazard perception.     

The role of drivers’ social interactions in their driving behavior: Empirical evidence and implications for car-following and traffic flow

Models for describing the microscopic driving behavior rarely consider the “social effects” on drivers’ driving decisions. However, social effect can be generated due to interactions with surrounding vehicles and affect drivers’ driving behavior, e.g., the interactions result in imitating the behavior of peer drivers. Therefore, social environment and peer influence can impact the drivers’ instantaneous behavior and shift the individuals’ driving state. This study aims to explore empirical evidence for existence of a social effect, i.e., when a fast-moving vehicle passes a subject vehicle, does the driver mimic the behavior of passing vehicle? High-resolution Basic Safety Message data set (N = 151,380,578) from the Safety Pilot Model Deployment program in Ann Arbor, Michigan, is used to explore the issue. The data relates to positions, speeds, and accelerations of 63 host vehicles traveling in connected vehicles with detailed information on surrounding environment at a frequency of 10 Hz. Rigorous random parameter logit models are estimated to capture the heterogeneity among the observations and to explore if the correlates of social effect can vary both positively and negatively. Results show that subject drivers do mimic the behavior of passing vehicles –in 16 percent of passing events (N = 18,099 total passings occurred in freeways), subject vehicle drivers are observed to follow the passing vehicles accelerating. We found that only 1.2 percent of drivers normally sped up (10 km/hr in 10 s) during their trips, when they were not passed by other vehicles. However, if passed by a high speed vehicle the percentage of drivers who sped up is 16.0 percent. The speed change of at least 10 km/hr within 10 s duration is considered as accelerating threshold. Furthermore, the acceleration of subject vehicle is more likely if the speed of subject driver is higher and more surrounding vehicles are present. Interestingly, if the difference with passing vehicle speed is high, the likelihood of subject driver’s acceleration is lower, consistent with expectation that if such differences are too high, the subject driver may be minimally affected. The study provides new evidence that drivers’ social interactions can change traffic flow and implications of the study results are discussed.     

Development and validation of the traffic displaced aggression questionnaire

Displaced aggression is defined as the aggression conducted against someone or something that is not considered to be the trigger of the emotional state of the aggressor. Whereas it has been deeply studied in a general context, to date, very few studies have analysed it in the specific context of driving. Considering the inexistence of instruments to assess it, the aim of the current research was to develop and validate a self-report in order to assess displaced aggression in the driving context. A sample of 467 participants (66.8% female, M_age = 34.74) filled in a set of questionnaires including the 29 items that were designed to assess traffic displaced aggression, as well as other instruments measuring different ways of expressing both general and driving aggression. The results of both Parallel Analysis (PA, sub-sample 1) and Confirmatory Factor Analysis (CFA, sub-sample 2) showed a good fit of the two-factor model, retaining 20 out of the initial 29 items. The first factor was labelled as Displaced aggression due to the anger generated outside the vehicle (6 items, α = 0.83), whereas the second factor was labelled as Displaced aggression due to the anger generated inside the vehicle (14 items, α = 0.91). Both factors, as well as the total score, showed good results regarding convergent and divergent validity. Practical implications of the results, future research lines and limitations of the current study are discussed.     

The potential of gamification for user education in partial and conditional driving automation: A driving simulator study

Drivers must establish adequate mental models to ensure safe driver-vehicle interaction in combined partial and conditional driving automation. To achieve this, user education is considered crucial. Since gamification has previously shown positive effects on learning motivation and performance, it could serve as a measure to enhance user education on automated vehicles. We developed a tablet-based instruction involving gamified elements and compared it to instruction without gamification and a control group receiving a user manual. After instruction, participants (N = 57) experienced a 30-minute automated drive on a motorway in a fixed-base driving simulator. Participants who received the gamified instruction reported a higher level of intrinsic motivation to learn the provided content. The results also indicate that gamification promotes mental model formation and trust during the automated drive. Taken together, including gamification in user education for automated driving is a promising approach to enhance safe driver-vehicle interaction.     

Attention all ‘drivers’: You could be to blame,no matter your behaviour or the level of vehicle automation

Vehicle crashes are one of the leading causes of human deaths worldwide, with crashes predominately attributed to failures of human drivers. Whilst increasing vehicle automation is argued to reduce road crashes via decreased driver involvement, automation also raises concerns around driver blame and stakeholder responsibility. This study examines blame for crash scenarios across four different forms of driver distraction behaviours (phone, sleep, work and driving under the influence), and across four levels of vehicle automation (no automation [manual], partially automated, highly automated, fully automated), using a mixed (qualitative and quantitative) methods approach. Participants (n = 205) were randomised into one of the four levels of vehicle automation and were presented with vignette crash scenarios involving a pedestrian being hit by a vehicle. Results revealed that scenarios varying driver behavior at the time of the crash, had no significant impact on participants’ blame attribution or selected course of action. The qualitative analysis revealed that despite semantic distinction between some driver behaviours, drivers were deemed responsible for the crash. As automation increased, attribution of blame towards the driver decreased, but did not disappear. Blame simultaneously increased towards other stakeholders including the manufacturer and the government, as level of automation increased. These findings mirror that of previous research and further highlight the need for legal frameworks for crashes with automated vehicles, irrespective of driver behaviours.     

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.     

Situational influencing factors on understanding cooperative actions in automated driving

Cooperative interacting vehicles are a promising approach in the context of automated driving. To ensure understanding and acceptance of such systems, the underlying mechanisms of human cooperation in the context of traffic must be understood. In a driving simulator study, we investigated how situational factors influence cooperative behaviour in a lane change situation on a two-lane German highway during automated driving in the left lane. When another car in the right lane was approaching a slower truck, participants (N = 32) were asked by an automated system to either accelerate, decelerate or maintain speed. The driver’s scope of action, the situation’s criticality for the lane-changing vehicle and the display of intention to change lanes were manipulated. A hierarchical multinomial logistic regression revealed that a wider scope of action, a higher situation’s criticality and signalling the intention to change lanes positively influenced cooperative behaviour by accelerating and decelerating. These results might be applied to design user-centred automated cooperatively interacting vehicles.     

Effect of multimodal takeover request issued through A-pillar LED light,earcon, speech message,and haptic seat in conditionally automated driving

The driver of a conditionally automated vehicle equivalent to level 3 of the SAE is obligated to accept a takeover request (TOR) issued by the vehicle. Considerable research has been conducted on the TOR, especially in terms of the effectiveness of multimodal methods. Therefore, in this study, the effectiveness of various multimodalities was compared and analyzed. Thirty-six volunteers were recruited to compare the effects of the multimodalities, and vehicle and physiological data were obtained using a driving simulator. Eight combinations of TOR warnings, including those implemented through LED lights on the A-pillar, earcon, speech message, or vibrations in the back support and seat pan, were analyzed to clarify the corresponding effects. When the LED lights were implemented on the A-pillar, the driver reaction was faster (p = 0.022) and steering deviation was larger (p = 0.024) than those in the case in which no LED lights were implemented. The speech message resulted in a larger steering deviation than that in the case of the earcon (p = 0.044). When vibrations were provided through the haptic seat, the reaction time (p < 0.001) was faster, and the steering deviation (p = 0.001) was larger in the presence of vibrations in the haptic seat than no vibration. An interaction effect was noted between the visual and auditory modalities; notably, the earcon resulted in a small steering deviation and skin conductance response amplitude (SCR amplitude) when implemented with LED lights on the A-pillar, whereas the speech message led to a small steering deviation and SCR amplitude without the LED lights. In the design of a multimodal warning to be used to issue a TOR, the effects of each individual modality and corresponding interaction effects must be considered. These effects must be evaluated through application to various takeover situations.     

Perception of unsafe driving behaviour and reported driving behaviour among commercial motorcyclists in Dar es Salaam,Tanzania

Commercial motorcycle drivers are recognised to take high risks while driving, but little is known about their perception of these risks. This paper presents the results of a survey of 400 commercial motorcyclists' perception of unsafe driving behaviours and then determine the association between the perception of unsafe driving behaviour and reported driving behaviour. The study was carried out in Dar es Salaam between December 8th 2018 and March 24th 2019. Drivers aged 18 years and above were selected from 90 parking stages within the city and completed a structured interview. Modified Poisson regression was used to estimate the prevalence ratios (PRs). We found that close to 60% of drivers considered unsafe driving to be unsafe. However, reported unsafe driving behaviour was moderately common. Our results showed that reported unsafe driving behaviour (adjusted PR = 1.7; 95% CI 1.3–2.2) was associated with a low perception of the dangers of unsafe driving among motorcyclists. The higher the earnings a driver had (adjusted PR = 1.6; 95% CI 1.2–2.0), the higher the likelihood of having a low perceived risk of unsafe behaviour. These findings suggest that commercial motorcyclists' unsafe behaviour are, for the most part, not the result of a poor perception of the involved risks.     

Driving demands,stress reactivity and driving behavior: An interactional approach

The aim of the present study was to investigate the role of driving demands, neuroticism, and their interaction when predicting driving behavior. More precisely, we strived to examine how driving behaviors (i.e., speeding, winding, tailgating and jerky driving) unfold across low and high driving demands and whether they are contingent on a personality factor that has previously been linked to stress reactivity. In a driving simulator, 50 participants with a valid driver’s license (56.6% female, age: M = 30.13, SD = 10.16) were exposed to driving scenarios of different levels of information processing and vehicle handling demands. Additionally, they filled-out a self-report questionnaire that measured their neuroticism. We found that driving behavior became safer in scenarios that were highly demanding in terms of information processing, while this pattern did not emerge with vehicle handling demands. Moreover, tentative support was found for the notion that individuals high in neuroticism are less able to adapt their behavior to higher information processing demands. The present study offers new insights on driving demands in a simulated driving context and points to the potential importance of exploring interactions between personality and situational factors when understanding driving behavior. Additionally, the results of the present study may be used to adapt driver’s education programs.     

The differences in hazard response time and driving styles of violation-involved and violation-free taxi drivers

The present study aimed to investigate the relationships between taxi drivers’ traffic violations in past driving and two domains: driving skill (hazard perception skill) and driving style. Five hundred and fifty taxi drivers aged 25 – 59 were recruited to finish a video-based hazard perception test and the Chinese version of the Multidimensional Driving Style Inventory (MDSI). The relationships between hazard response time, driving style and traffic violations were examined, and the differences in hazard response times and driving styles of violation-involved drivers (n = 220) and violation-free drivers (n = 330) were compared. The results showed that taxi drivers’ traffic violations are closely related to their driving styles and hazard response time. Violation-involved drivers scored significantly higher in hazard response time and maladaptive driving styles (i.e., anxious, risky and angry styles) and lower in careful driving style than violation-free drivers. More importantly, drivers’ hazard response time and driving styles can effectively predict their violation involvement in the last 12 months with an overall classification accuracy of 66.4%. The findings provide evidence for the usefulness of video-based hazard perception tests and the MDSI in taxi driver testing and training.     

Pro-social behaviour in young passengers: Predictive utility of the social reaction pathway of the prototype willingness model

This study examined the social reaction pathway of the Prototype Willingness Model (PWM) to assess how attitudes, subjective norms, prototype perception (favourability and similarity), and risk perception influenced young passengers’ willingness to speak up to a driver exceeding the posted speed limit by either 5 km/h or 10 km/h. It was hypothesised that participants would be more willing to speak up to drivers’ travelling 10 km/h over the posted speed limit than 5 km/h over the posted speed limit. Further, it was hypothesised that the PWM constructs would significantly predict passenger willingness to speak up to a driver travelling 5 km/h over the posted speed limit and 10 km/h over the posted speed limit. Young Australians aged 17 to 25 years (N = 136, Mage = 19.32, 77.9% female) were recruited to complete a 30-minute online questionnaire. As predicted, passengers were significantly more willing to speak up to drivers travelling at 10 km/h over the posted speed limit than drivers travelling 5 km/h over the posted speed limit. Further, the results from a linear regression revealed that some of the PWM constructs were effective in explaining the variance in willingness to speak-up to drivers travelling either 5 km/h or 10 km/h over the posted speed limit. These findings address a gap in road safety research by shifting focus from the behaviour of drivers to the behaviour of passengers. The focus on the pro-social behaviour of speaking up to drivers performing risk taking behaviours may help to inform future educational campaigns and interventions designed to reduce young peoples’ involvement in road crashes.