How to define the accident risk level of car drivers by combining objective and subjective measures of driving style

Perceived risk can be defined as the expectation of being involved in a traffic accident. Traffic risk perception is often subjectively evaluated being risk perception as highly individual, and depending on experiences with accidents. For this reason, a subjective measure of risk level can be useful. On the other hand, accident risk level can be measured also through objective measures, consisting in kinematic parameters defining the driving style. At this aim, it is very important to analyse instantaneous and geo-referenced travelling kinematic parameters of the vehicle recorded by real tests on the road.The aim of this paper is to identify the accident risk level of each driver based on a methodology combining both subjective and objective parameters. Specifically, by using the proposed methodology the risk can be measured immediately by using only the kinematic parameters adopted when driving; we defined three levels of risk of being involved in a road accident (low, medium, and high risk). We retain that our research can give a contribution in terms of improvement of road safety, because the distorted perception of risk level is one of the main causes of road accidents. Very often, drivers are not aware of the risk taking; therefore, it is necessary to raise the awareness of drivers to have safer driving. The proposed method can be useful for informing the driver on the level of risk he/she assumes, so that he/she can take the necessary precautions next time that will cover that stretch of road.     

Identifying effects of driving and secondary task demands,passenger presence,and driver characteristics on driving errors and traffic violations – Using naturalistic driving data segments preceding both safety critical events and matched baselines

Researchers have identified various factors that likely affect aberrant driving behaviors and therefore crash risk. However, it remains unclear which of these factors poses the greatest risk for committing either errors or violations under naturalistic driving conditions. This study investigated important variables contributing to driving errors and traffic violations based on naturalistic driving data from the second Strategic Highway Research Program (SHRP 2). The analyzed driving segments preceded both safety critical events and matched baselines. Results showed that intersection influence, high-risk visually distracting secondary tasks, and the severities of the safety critical events were the main factors associated with driving errors. The primary factors linked to violations were intersection influence, persistent individual differences in driver behavior, and the severities of the safety critical events. Furthermore, the number of aberrant driving behaviors in trip segments preceding crashes was higher than in the matched segments unrelated to safety critical events. However, the most common aberrant driving behavior types in the respective segment groups appeared to resemble each other. This suggests that crashes became more likely due to drivers committing more violations and errors overall as opposed to drivers making one certain type of error or violation.     

What and how to tell beforehand: The effect of user education on understanding,interaction and satisfaction with driving automation

The success of introducing automated driving systems to consumers will depend on an appropriate understanding and human-automation interaction with this technology. Educating users on driving automation technology bears the potential to attain these two requirements. In a driving simulator study, we investigated the effects of user education on mental models, human-automation interaction performance (i.e., time on task, error rate, experimenter rating) and satisfaction with a Human-Machine Interface (HMI) for automated driving. N = 80 participants were randomly assigned to one of three different user education conditions or to a baseline. Subsequently, they completed several driver-initiated control transitions between manual, Level 2 (L2), and Level 3 (L3) automated driving. The results revealed that user education promoted an accurate evolution of mental models for driving automation. These, in turn, facilitated interaction performance in transitions from manual to both L2 and L3 automated driving. There was no comparable influence of prior education on performance in transitions between the automation levels. Due to the performance enhancing effects of user education, no further improvements of interaction performance were observed for educated users in comparison to uneducated users. There was no effect of user education on satisfaction. The current findings emphasize the necessity to provide information about automated vehicle HMIs to first-time users to support accurate understanding and behavior. Based on the current findings, we propose conceptual approaches to teach users and derive implications for user studies on automated vehicle HMIs.     

Effects of secondary tasks and display position on glance behavior during partially automated driving

The driving task is becoming increasingly automated, thus changing the driver’s role. Moreover, in-vehicle information systems using different display positions and information processing channels might encourage secondary task engagement. During manual driving scenarios, varying secondary tasks and display positions could influence driver’s glance behavior. However, their impact on the driver’s capability to monitor the partially automated driving system has not yet been determined. The current study assessed both the effects of different secondary tasks (Surrogate Reference Task (SuRT) vs. text reading) and display positions (head-up display (HUD) vs. center console) on driver’s glance behavior during partially automated driving in a simulated car following task. Different automation system failures regarding the lateral and longitudinal control occurred while driving. Furthermore, participants’ reported advantages, disadvantages and preferences regarding the investigated display positions as well as regarding the secondary task engagement during partially automated driving in general. Mixed design ANOVAs revealed that the HUD yielded considerably longer eyes-on display time (total and mean glance durations) than the center console. Moreover, the text reading task resulted in longer total and mean glance durations than the SuRT. Similar to manual driving scenarios, the results showed a consistent effect of display position and secondary task on the driver’s glance behavior. Despite the longer eyes-on display time for the HUD, its proximity to the driving environment might enable a faster identification of and reaction to critical situations (e.g., due to system failures). Participants would prefer the HUD as display position compared to the center console. Regarding secondary task engagement during partially automated driving participants seemed to be aware of the benefits but also of the risks.     

Behavioral changes to repeated takeovers in automated driving: The drivers’ ability to transfer knowledge and the effects of takeover request process

Only a couple of studies evaluated whether drivers of automated vehicles change their takeover behavior when they experience takeover requests repeatedly. Even less evidence was accumulated regarding the question whether drivers are able to transfer learned behavior to takeover situations with varying visibility characteristics and whether drivers’ takeover behavior depends on the takeover process in these situations. This paper therefore examines three research questions. First, it assesses how drivers change their behavior with the repeated experience of a takeover situation with the same visibility (fog or no fog). Second, it tests whether drivers can transfer their learned takeover behavior from a takeover situation with high or low visibility to the same takeover situation with different visibility conditions. Third, it assesses whether drivers’ takeover behavior and their experience of the situation differ between a one-step and a two-step takeover request process. Forty participants experienced a takeover situation three times. Experimental trials varied between-subjects concerning the permanent presence or absence of fog in the adaptation condition, the change of visibility conditions from fog to no fog or vice versa in the transfer condition, and the design of the takeover process with one-step or two-steps. Dependent variables included participants’ takeover time, minimum time-to-collision (TTC_min) with the construction site, deceleration and maximum steering behavior, and their ratings of criticality of the driving situation and perceived effort. Results show that participants adapted their deceleration behavior when repeatedly experiencing a takeover situation with the same visibility characteristics (adaptation condition). Changing these characteristics (transfer condition) lead to increased minimum TTCs, criticality and perceived effort ratings. In general, participants were able to maintain their takeover behavior in takeover situations with varying visibility characteristics indicating that they can transfer their takeover behavior across situations. Finally, the two-step takeover request process was associated with longer takeover times. However, minimum TTCs were larger and maximum steering movements and criticality ratings were lower compared to the one-step process. We conclude that drivers transfer their behavior across takeover situations. However, this performance comes at higher costs in terms of perceived effort and criticality. In addition, two-step takeover request processes should be favored over one-step processes when designing takeover requests. Future studies should examine the validity of the results in various takeover situations and on-the-road studies.     

Effectiveness investigation of travel demand management measures in Beijing: Existing measures and a potential measure–tradable driving credit

Facing serious challenges of traffic congestion and air pollution, Beijing has implemented a series of traffic polices. In this paper, we first investigate the effectiveness of existing traffic measures in Beijing and analyse the underlying factors from the perspective of drivers, which provide insights for the traffic regulations in other cities worldwide. While the car ownership restriction has effectively limited the total number of vehicle in Beijing, the effect of the car use restriction only lasts for a short period. A survey on drivers’ opinions and attitudes towards the traffic situation and a potential measure, i.e., tradable driving credit (TDC), is conducted. Although traffic data shows that congestion improved and traffic was slightly congested in recent years in Beijing, most respondents believe congestion is still serious in Beijing. However, they think the impact of traffic congestion on their personal car use is relatively low, which could partly explain why so many travellers still depend on driving given the relative convenient transit facilities in Beijing. In addition, a large proportion of respondents treat personal cars as a representation of social status and think driving bring them pleasures, which indicates that it may be hard to further decrease the share of car trips in Beijing. According to their attitudes and social-economical characters, respondents’ willingness on their changes of travel behaviours under TDC is investigated. The results show that individuals’ social-economic characters such as income level, education and family size and the symbolic meaning of personal cars have significant influence on respondents’ willingness to switch to other transport modes.     

Drivers’ driving style and their take-over-control judgment

Advanced driver assistance systems (ADASs), which help a driver drive a car safely and easily (e.g., warning alerts, steering control, and brake/acceleration pedal operation), have increased in popularity. However, such systems have not yet been perfected. Sometimes, humans must take over control from the systems; otherwise, they can cause an accident. In this study, we focused on one of the ADASs, adaptive cruise control (ACC), which automatically maintains a selected distance from the preceding car, and investigated individual differences in take-over-control judgment and related factors. The candidate factors included driver’s manual driving style, driving performance without the ACC, and the usability evaluation of ACC. Ten participants repeated the short, strictly controlled trials in a driving simulator (DS), with a varying value of only one parameter (deceleration of the preceding car) affecting the need for intervention. First, we confirmed that the participants made the judgment based on the dangerousness of the situation and that there were individual differences in the take-over-control judgments. Some participants intervened in the ACC control in less dangerous trials, whereas other participants did not, even if their own car got very close to the preceding car. We conducted a correlation analysis and confirmed the results with the estimation of the confidence interval using a bootstrap method. As a result, we found that driving style and driving performance without ACC had a stronger relationship to the number of interventions, rather than the usability evaluation. In particular, methodical drivers, who obeyed traffic rules and manners, began to intervene in less dangerous situations. The tendency to avoid utilizing brake operations was also related to take-over-control judgment. This might be because the participants intervened by pressing the brake pedal. Our study showed that drivers’ driving style could affect the usage of ACC independently from the performance of the ACC.     

Overall performance impairment and crash risk due to distracted driving: A comprehensive analysis using structural equation modelling

Distracted driving leads to performance changes in both longitudinal and lateral control. However, driving performance, being a multidimensional phenomenon, is very difficult to be interpreted from individual performance measures. The present study aimed to exhibit the distraction impacts on overall driving performance estimated by a single measure rather than assessing distraction effects on individual performance metrics such as speed, acceleration, lateral variation, etc. The study also focused on modelling and quantifying the impacts of overall deteriorated driving performance on crash risk. To achieve the objectives, a comparative analysis of investigating phone and music player usage was conducted using Structural Equation Modelling (SEM). In total, 90 drivers’ demographic details and driving performance data in distracted and non-distracted driving environments were collected. Firstly, the latent variable “performance degradation” was derived from longitudinal and lateral performance measures. Then the structural model revealed a positive relationship between the distractions and the overall performance degradation. Finally, the crash risk was modelled against the presence of distraction and performance degradation. With a factor loading of 0.29, the impact of deteriorated driving performance (i.e., indirect impact) was found to be the highest among all the contributory factors of the crash risk. Further, the results showed that among the distractions (i.e., direct impact), texting had the highest impact (factor loading = 0.28) on crash risk followed by visual-manual tasks related to music player (factor loading = 0.21). Thus, the present study quantified the effects of deteriorated driving performance caused by distracted driving on the crash risk. Further, the study also presented quantified effects of each distracting activity on the crash risk which accounted for the factors that could not be considered through the performance degradation measure. The approach used in the present study can be adopted in designing the countermeasures using advanced driver warning systems.     

Fear and avoidance of driving among drivers involved in a road traffic crash. The role of traumatic fear and driving cognitions

Fear and avoidance of driving are possible consequences of involvement in road traffic crashes (RTCs). Few studies have assessed the factors associated with fear and avoidance of driving after an RTC. The aim of this present study is to investigate the relations among trauma appraisals of fear, negative driving cognitions, fear and avoidance of driving in a sample of people who experienced vehicle crashes. Further, the mediating role of negative driving cognitions in the relation between trauma appraisals, fear of driving and avoidance of driving was assessed. The sample was comprised of 116 drivers who had been involved in an RTC in the past two years. Negative driving cognitions positively predicted fear of driving and avoidance of driving. Moreover, negative driving cognitions mediated the relation among trauma appraisals of fear with fear of driving and avoidance of driving. The theoretical and practical implications of these results are discussed.     

The prevalence of cognitive disengagement in automobile crashes

The current study analysed crashes identified in a large-scale naturalistic driving database to assess the prevalence of cognitive disengagement (i.e., purely cognitive distraction and mind wandering/microsleep) or episodes wherein the driver did not look away from the roadway during secondary task completion or wherein another clearly observable contributing crash factor was not present, and the driver's reaction to the crash showed symptoms of cognitive disengagement. The study found that <1% (95% CI [0.45, 1.66]) of higher severity crashes had a potential contributing crash factor of mind wandering/microsleep; ~1.5% (95% CI [0.83, 2.32]) had a potential contributing factor of purely cognitive distraction. The results suggest a relatively low prevalence of cognitive disengagement among automotive crashes compared with visual/manual secondary tasks. However, the current methodology is limited in that it does not account for instances where cognitive disengagement, in combination with other factors, may have led to crashes, potentially leading to conservative estimates.