Assessing the feasibility of the theory of planned behaviour in predicting drivers’ intentions to operate conditional and full automated vehicles

Guided by the Theory of Planned Behaviour (TPB), this study examined the beliefs underpinning, and feasibility of the factors in predicting, individuals’ intentions to use a conditional (Level 3) automated vehicle (AV) and a full (Level 5) AV. Australian drivers (N = 505) aged 17–81 years (Mean age = 33.69, SD = 18.79) were recruited and completed a 20 min online survey which featured both quantitative and qualitative items. For the quantitative data, two linear regressions revealed that the TPB standard constructs of attitudes, subjective norm, and perceived behavioural control (PBC) accounted for 66% of the variance in intentions to use a conditional AV and 68% of the variance in intentions to use a full AV. Of the TPB constructs, attitudes and subjective norms were significant positive predictors of future intentions to use conditional and full AVs. For the qualitative data, some differences emerged for the underlying behavioural beliefs that underpinned intentions to use conditional and full AVs. For example, having beliefs about control over the conditional AV was identified by many participants as an advantage, while not being in full control of the full AV was identified as a disadvantage. For underlying control beliefs, participants identified similar barriers for both vehicle types, including; high costs, lack of trust, lack of control over the vehicle, lack of current legislation to support the mainstream introduction of these vehicles, and concerns of safety for self and for other road users when operating AVs. Overall, these findings provide some support for applying the TPB to understand drivers’ intended use of AVs. However, while the current study showed that the constructs of attitudes and subjective norms might reflect intended use of AVs, more research is required to further examine the role of PBC. Additionally, the findings provide initial insights into the underlying behavioural and control beliefs that may motivate drivers to use AVs and highlight the similarities and differences in drivers’ perceptions towards two levels of vehicle automation.     

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.     

Crash risk evaluation of aggressive driving on motorways: Microscopic traffic simulation approach

This paper provides quantitative evaluation of safety implications of aggressive driving (speeding, following closely and weaving through traffic) by using microscopic traffic simulation approach. Combination of VISSIM and Surrogate Safety Assessment Model (SSAM) were used to model motorway and assess safety of the simulated vehicle. The use of vehicle conflicts was validated by correlating it to historic crashes. Crash risk, severity levels and the magnitude of the perceived benefits of aggressive driving were quantified relative to normal drivers under two scenarios: (1) congested, and (2) non-congested traffic conditions. Involvement in vehicle conflicts is used to determine crash-risk while reductions in Post Encroachment Time (PET) and travel time were used to determine the severity levels of the expected crashes and the magnitude of the perceived benefits. The results indicated that the crash risk of aggressive drivers was found to be in the range 3.10–5.8 depending on traffic conditions and type of road aggression. PET of the conflicts involving aggressive drivers reduced by 7–61% indicating high severity levels of the expected crashes. Moreover, the magnitude of the perceived benefit in terms of reduction in travel time was found to be as little as 1–2%. The study concluded that aggressive driving is entailed with a massive risk while its benefits are actually very little.     

Lessons learned from pedestrian-driver communication and yielding patterns

Understanding the hidden patterns of tacit communication between drivers and pedestrians is crucial for improving pedestrian safety. However, this type of communication is a result of the psychological processes of both pedestrians and drivers, which are very difficult to understand thoroughly. This study utilizes a naturalistic field study dataset and explores the hidden patterns from successful and failed communication events using a pattern recognition method known as Taxicab Correspondence Analysis (TCA). The successful communication scenarios indicate the combinations of variable attributes such as eye contact, facial expression, the assertion of crossing, and effective traffic control devices are strongly associated with successful scenarios. The patterns for failed scenarios are most likely to be on the roadway with a relatively higher speed limit (e.g., 35 mph) and a relatively lower speed limit (e.g., 15 mph) under different conditions. On roadways with a higher speed limit, the failed scenarios are highly associated with passive and undecisive pedestrians, pedestrians far away from the crosswalk, regardless of pedestrian-driver eye contact and facial expression of the pedestrians. Instead of waiting for pedestrians to making a crossing decision, overspeeding drivers are more likely to speed up and pass the crosswalk. On roadways with a lower speed limit, the failed scenarios are often associated with distracted pedestrians, vehicles having the right of way, and the absence of effective traffic control devices. These findings could help transportation agencies identify appropriate countermeasures to reduce pedestrian crashes. The findings on driver-pedestrian communication patterns could provide scopes for improvement in computer vision-based algorithms designed for autonomous vehicle industries.     

SAD as a mathematical assistant—how should we go from here to there?

The System for Automated Deduction (SAD) is developed in the framework of the Evidence Algorithm research project and is intended for automated processing of mathematical texts. The SAD system works on three levels of reasoning: (a) the level of text presentation where proofs are written in a formal natural-like language for subsequent verification; (b) the level of foreground reasoning where a particular theorem proving problem is simplified and decomposed; (c) the level of background deduction where exhaustive combinatorial inference search in classical first-order logic is applied to prove end subgoals.

We present an overview of SAD describing the ideas behind the project, the system's design, and the process of problem formalization in the fashion of SAD. We show that the choice of classical first-order logic as the background logic of SAD is not too restrictive. For example, we can handle binders like Σ or lim without resort to second order or to a full-powered set theory. We illustrate our approach with a series of examples, in particular, with the classical problem .     

Étude des liens entre conduite automobile agressive et transgressive et impulsivité : compréhension empirique et clinique à l’aide du modèle UPPS de l’impulsivité

The aim of this study is to clarify the relationship between impulsiveness and aggressive and transgressive driving using the UPPS Impulsive Behavior Scale. A total of 353 participants (laypersons) filled the Aggressive Driving Behavior Scale (ADBS) and the UPPS. Main results indicate: positive correlations between the four facets of impulsiveness and aggressive and transgressive driving and urgency and sensation-seeking are significant predictors of aggressive driving and its dimensions. These results highlight the need to develop treatment based on an in-depth clinical evaluation of prime behaviours.     

The different effects of personality on prosocial and aggressive driving behaviour in a Chinese sample

Dangerous driving behaviours, as a direct cause of accidents and death, are the focus of considerable research attention. However, unlike unsafe driving behaviours, few studies have explored safe driving behaviours and their effects on road traffic. This study aims to verify the Chinese version of the Prosocial and Aggressive Driving Inventory (PADI) and then investigate the relationship between personality and aggressive/prosocial driving behaviours. A total of 303 licensed drivers were recruited, and they voluntarily and anonymously completed the PADI, the Driving Behaviours Questionnaire (DBQ), and personality scales (anger, sensation-seeking and altruism). The results of this research confirmed the reliability and validity of the Chinese PADI. Most importantly, it was found that different relationships between different personalities and aggressive/prosocial driving behaviours. Specifically, individuals with high altruism exhibited more prosocial driving behaviours, while individuals with high sensation seeking presented more aggressive driving behaviours. The importance of these findings lies in two main potential implications: developing an effective measurement of prosocial driving behaviours in China and providing favourable evidence to guide drivers toward more prosocial driving behaviours.     

Interaction between pedestrians and automated vehicles: A Wizard of Oz experiment

Automated vehicles (AVs) will be introduced on public roads in the future, meaning that traditional vehicles and AVs will be sharing the urban space. There is currently little knowledge about the interaction between pedestrians and AVs from the point of view of the pedestrian in a real-life environment. Pedestrians may not know with which type of vehicle they are interacting, potentially leading to stress and altered crossing decisions. For example, pedestrians may show elevated stress and conservative crossing behavior when the AV driver does not make eye contact and performs a non-driving task instead. It is also possible that pedestrians assume that an AV would always yield (leading to short critical gaps). This study aimed to determine pedestrians’ crossing decisions when interacting with an AV as compared to when interacting with a traditional vehicle. We performed a study on a closed road section where participants (N = 24) encountered a Wizard of Oz AV and a traditional vehicle in a within-subject design. In the Wizard of Oz setup, a fake ‘driver’ sat on the driver seat while the vehicle was driven by the passenger by means of a joystick. Twenty scenarios were studied regarding vehicle conditions (traditional vehicle, ‘driver’ reading a newspaper, inattentive driver in a vehicle with “self-driving” sign on the roof, inattentive driver in a vehicle with “self-driving” signs on the hood and door, attentive driver), vehicle behavior (stopping vs. not stopping), and approach direction (left vs. right). Participants experienced each scenario once, in a randomized order. This allowed assessing the behavior of participants when interacting with AVs for the first time (no previous training or experience). Post-experiment interviews showed that about half of the participants thought that the vehicle was (sometimes) driven automatically. Measurements of the participants’ critical gap (i.e., the gap below which the participant will not attempt to begin crossing the street) and self-reported level of stress showed no statistically significant differences between the vehicle conditions. However, results from a post-experiment questionnaire indicated that most participants did perceive differences in vehicle appearance, and reported to have been influenced by these features. Future research could adopt more fine-grained behavioral measures, such as eye tracking, to determine how pedestrians react to AVs. Furthermore, we recommend examining the effectiveness of dynamic AV-to-pedestrian communication, such as artificial lights and gestures.