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Untreated patients with obstructive sleep apnea (OSA) are at increased risk for motor vehicle collisions; however, it is unclear how this should be translated into fitness-to-drive recommendations. Accordingly, the Canadian Thoracic Society (CTS) Sleep Disordered Breathing Clinical Assembly and the Canadian Sleep Society (CSS) assembled a CTS-CSS working group to propose recommendations with regard to driving in patients with OSA. Recommendations for assessing fitness to drive in noncommercial drivers: 1. Severity of OSA alone is not a reliable predictor of collision risk and, therefore, should not be used in isolation to assess fitness to drive; 2. The severity of sleep apnea should be considered in the context of other factors to assess fitness to drive; 3. The decision to restrict driving is ultimately made by the motor vehicle licensing authority; however, they should take into account the information and recommendations provided by the sleep medicine physician and should follow provincial guidelines; 4. For patients prescribed continuous positive airway pressure (CPAP) therapy, objective CPAP compliance should be documented. Efficacy should also be documented in terms of reversing the symptoms and improvement in sleep apnea based on physiological monitoring; 5. For patients treated with surgery or an oral appliance, verification of adequate sleep apnea treatment should be obtained; and 6. A driver diagnosed with OSA may be recertified as fit to drive based on assessment of symptoms and demonstrating compliance with treatment. The assessment should be aligned with the provincial driver's license renewal period. Commercial vehicles: Assessment of fitness to drive should be more stringent for patients operating commercial vehicles. In general, the CTS-CSS working group was in agreement with the Medical Expert Panel recommendations to the Federal Motor Carrier Safety Administration in the United States; these recommendations were adapted for Canadian practitioners.

This article evaluates the driver's reaction times in a driving simulator environment. The research focused mainly on young drivers under the age of 26, who cause many accidents. Each participating driver provided basic information later used for mathematical-statistical analysis. The main advantage of driving simulators is limitless usage. It is possible to simulate situations that would be unacceptable in real road traffic. Therefore, this study is also able to examine drunk driving. The main goal of the article is to evaluate if gender, practice, or alcohol significantly affected the reaction time of 30 drivers. We also focused on drinking before driving for a smaller number of the drivers; ten of them performed driving under the influence of alcohol. For these mathematical-statistical purposes, we used a one-sample t -test, a paired-samples t -test, an independent-sample t -test, and a correlation analysis together with the assessment of its statistical significance.

Train drivers work long hours on 24 h schedules and many factors impact their fatigue risk at work, creating a clear imperative for good rostering practice. Adopting a systems approach, this study investigated the relationship between multiple interrelated factors (train drivers' schedule, sleep, wellbeing, and fatigue) and the perceived influence of these factors on train driving performance and safety using an online survey distributed in Australia and New Zealand. In addition to demographics and work schedule, passenger and freight train drivers (n = 751) answered questions about: (1) Sleep duration; (2) wellbeing, including physical and mental health, the extent to which shiftwork causes sleep, social, domestic, and work problems, and (3) the extent to which ten fatigue, health and wellbeing factors in the work and home environment negatively impact their driving performance. The key factor that emerged from analyses, with the largest and most consistent negative effects (and controlling for other factors) was schedule irregularity. Approved rosters were ranked as having the most important impact on day-to-day driving performance, followed by physical and mental health, and outside work factors. Results also suggested that schedule irregularity may amplify the negative impacts of the roster, impaired physical and mental health, and outside work factors on driving performance. As shift variability and schedule irregularity are often poorly represented in existing industry guidance, these results provide evidence for increased reflection on current fatigue management guidelines for train drivers and suggest a need for greater focus on schedule irregularity through the lens of a systems approach., (© 2022. The Author(s).)

As automated vehicles require human drivers to resume control in critical situations, predicting driver takeover behaviour could be beneficial for safe transitions of control. While previous research has explored predicting takeover behaviour in relation to driver state and traits, little work has examined the predictive value of manual driving style. We hypothesised that drivers' behaviour during manual driving is predictive of their takeover behaviour when resuming control from an automated vehicle. We assessed 38 drivers with varying experience in a high-fidelity driving simulator. After completing manual driving sessions to assess their driving style, participants performed an automated driving task, typically on a subsequent date. Measures of driving style from manual driving sessions, including headway and lane change speed, were found to be predictive of takeover behaviour. The level of driving experience was associated with the behavioural measures, but correlations between measures of manual driving style and takeover behaviour remained after controlling for driver experience. Our findings demonstrate that how drivers reclaim control from their automated vehicle is not an isolated phenomenon but is associated with manual driving behaviour and driving experience. Strategies to improve takeover safety and comfort could be based on driving style measures, for example by the automated vehicle adapting its behaviour to match a driver's driving style., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Effectively regulating anger driving has become critical in ensuring road safety. The existing research lacks a feasible exploration of anger-driving regulation. This paper delves into the effect and neural mechanisms of emotional olfactory stimuli (EOS) on regulating anger driving based on EEG. First, this study designed an angry driving regulation experiment based on EOS to record EEG signals. Second, brain activation patterns under various EOS conditions are explored by analyzing functional brain networks (FBNs). Additionally, the paper analyzes dynamic alterations in anger-related characteristics to explore the intensity and persistence of regulating anger driving under different EOS. Finally, the paper studies the frequency energy of EEG changes under EOS through time-frequency analysis. The results indicate that EOS can effectively regulate a driver's anger emotions, especially with the banana odor showing superior effects. Under banana odor stimulus, synchronization between the parietal and temporal lobes significantly decreased. Notably, the regulatory effect of banana odor is optimal and exhibits sustained efficacy. The regulatory effect of banana odor on anger emotions is persistent. Furthermore, the impact of banana odor significantly reduces the distribution of high-energy activation states in the parietal lobe region. Our findings provide new insights into the dynamic characterization of functional connectivity during anger-driving regulation and demonstrate the potential of using EOS as a reliable tool for regulating angry driving.

Mode awareness is important for the safe use of automated vehicles, yet drivers' understanding of mode transitions has not been sufficiently investigated. In this study, we administered an online survey to 838 respondents to examine their understanding of control responsibilities in partial and conditional driving automation with four types of interventions (brake pedal, steering wheel, gas pedal, and take-over request). Results show that most drivers understand that they are responsible for speed and distance control after brake pedal interventions and steering control after steering wheel interventions. However, drivers have mixed responses regarding the responsibility for speed and distance control after steering wheel interventions and the responsibility for steering control after gas pedal interventions. With a higher automation level (conditional driving automation), drivers expect automation to remain responsible more often compared to a lower automation level (partial driving automation). Regarding Hands-on requirements, more than 99% of respondents answered that drivers would keep their hands on the steering wheel after all intervention types in partial automation, while 60-95% would place their hands on the wheel after various intervention types in conditional automation. A misalignment between actual logic and drivers' expectations regarding control responsibilities is observed by comparing survey responses to the mode transition logic of commercial partially automated vehicles. To resolve confusion about control responsibilities and ensure consistent expectations, we propose implementing a consistent mode design and providing enhanced information to drivers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Acute stress exposure can significantly impact a train driver's capacity to maintain safe train operations. However, research examining how train drivers perceive the impacts of acute stressors is limited. This study investigated train driver perceptions regarding performance impacts of stressful events and potential strategies for reducing negative impacts. 71 Australian train drivers were presented with three stressful event scenarios via an online survey and asked to rate the impacts on driving performance. Results showed that participants perceived that stress would enhance performance, but that impacts differed depending on the event type. The findings suggest that train drivers may not be subjectively aware of negative impacts of acute stress, which has important practical implications for risk management following an incident. Qualitative results revealed the most frequently reported stress impact related to cognition. Practical implications and future research directions to prevent and manage stressful event exposure are discussed., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Stress impacts driving-related cognitive functions like attention and decision-making, and may arise in automated vehicles due to non-driving tasks. Unobtrusive relaxation techniques are needed to regulate stress without distracting from driving. Tactile wearables have shown efficacy in stress regulation through respiratory guidance, but individual variations may affect their efficacy. This study assessed slow-breathing tactile guidance under different stress levels on 85 participants. Physiological, behavioral and subjective data were collected. The influence of individual variations (e.g., driving habits and behavior, personality) using logistic regression analysis was explored. Participants could follow the guidance and adjust breathing while driving, but subjective efficacy depended on individual variations linked to different efficiency in using the technique, in relation with its attentional cost. An influence of factors linked to the evaluation of context criticality was also found. The results suggest that considering individual and contextual variations is crucial in designing and using such techniques in demanding driving contexts. In this line some design recommendations and insights for further studies are provided., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Currently, the number of vehicles in circulation continues to increase steadily, leading to a parallel increase in vehicular accidents. Among the many causes of these accidents, human factors such as driver drowsiness play a fundamental role. In this context, one solution to address the challenge of drowsiness detection is to anticipate drowsiness by alerting drivers in a timely and effective manner. Thus, this paper presents a Convolutional Neural Network (CNN)-based approach for drowsiness detection by analyzing the eye region and Mouth Aspect Ratio (MAR) for yawning detection. As part of this approach, endpoint delineation is optimized for extraction of the region of interest (ROI) around the eyes. An NVIDIA Jetson Nano-based device and near-infrared (NIR) camera are used for real-time applications. A Driver Drowsiness Artificial Intelligence (DD-AI) architecture is proposed for the eye state detection procedure. In a performance analysis, the results of the proposed approach were compared with architectures based on InceptionV3, VGG16, and ResNet50V2. Night-Time Yawning-Microsleep-Eyeblink-Driver Distraction (NITYMED) was used for training, validation, and testing of the architectures. The proposed DD-AI network achieved an accuracy of 99.88% with the NITYMED test data, proving superior to the other networks. In the hardware implementation, tests were conducted in a real environment, resulting in 96.55% and 14 fps on average for the DD-AI network, thereby confirming its superior performance.

Monitoring heart rate (HR) through photoplethysmography (PPG) signals is a challenging task due to the complexities involved, even during routine daily activities. These signals can indeed be heavily contaminated by significant motion artifacts resulting from the subjects' movements, which can lead to inaccurate heart rate estimations. In this paper, our objective is to present an innovative necklace sensor that employs low-computational-cost algorithms for heart rate estimation in individuals performing non-abrupt movements, specifically drivers. Our solution facilitates the acquisition of signals with limited motion artifacts and provides acceptable heart rate estimations at a low computational cost. More specifically, we propose a wearable sensor necklace for assessing a driver's well-being by providing information about the driver's physiological condition and potential stress indicators through HR data. This innovative necklace enables real-time HR monitoring within a sleek and ergonomic design, facilitating seamless and continuous data gathering while driving. Prioritizing user comfort, the necklace's design ensures ease of wear, allowing for extended use without disrupting driving activities. The collected physiological data can be transmitted wirelessly to a mobile application for instant analysis and visualization. To evaluate the sensor's performance, two algorithms for estimating the HR from PPG signals are implemented in a microcontroller: a modified version of the mountaineer's algorithm and a sliding discrete Fourier transform. The goal of these algorithms is to detect meaningful peaks corresponding to each heartbeat by using signal processing techniques to remove noise and motion artifacts. The developed design is validated through experiments conducted in a simulated driving environment in our lab, during which drivers wore the sensor necklace. These experiments demonstrate the reliability of the wearable sensor necklace in capturing dynamic changes in HR levels associated with driving-induced stress. The algorithms integrated into the sensor are optimized for low computational cost and effectively remove motion artifacts that occur when users move their heads.

Traffic signs detection is an important and challenging task in intelligent driving perception system. This paper proposes an improved lightweight traffic signs detection framework based on YOLOv5. Firstly, the YOLOv5's backbone is replaced with ShuffleNet v2, which simplifies the calculation complexity and reduces the parameters of backbone network. Secondly, aiming at the problem of inconspicuous traffic sign characteristics in complex road environment, we use the CA attention mechanism in this paper to improve the saliency of the object. Finally, aiming at the large-scale difference between the traffic signs and the high proportion of small objects, we design the BCS-FPN to fuse multi-scale features and improve the representation ability of the small-scale objects. The TT-100K dataset is also analyzed and the dataset is collated. We test on the collated TT-100K dataset for the improved YOLOv5 in this paper. And the results show that compared with YOLOv5s, the mAP of our algorithm is equivalent to that of YOLOv5s, and the speed is improved by 20.8%. This paper also has carried on the experiment on embedded devices, experimental results show that our framework in computing power less embedded devices has a better effect., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: In 2016, large-scale 20 miles per hour speed limits were introduced in the United Kingdom cities of Edinburgh and Belfast. This paper investigates the role that scientific evidence played in the policy decisions to implement lower speed limits in the two cities., Methods: Using a qualitative case study design, we undertook content analysis of a range of documents to explore and describe the evolution of the two schemes and the ways in which evidence informed decision-making. In total, we identified 16 documents for Edinburgh, published between 2006 and 2016, and 19 documents for Belfast, published between 2002 and 2016., Findings: In both cities, evidence on speed, collisions and casualties was important for initiating discussions on large-scale 20 mph policies. However, the narrative shifted over time to the idea that 20 mph would contribute to a wider range of aspirations, none of which were firmly grounded in evidence, but may have helped to neutralize opposing discourses., Discussion and Conclusions: The relationship between evidence and decision-making in Edinburgh and Belfast was neither simple nor linear. Widening of the narrative appears to have helped to frame the idea in such a way that it had broad acceptability, without which there would have been no implementation, and probably a lot more push back from vested interests and communities than there was., (© 2024. The Author(s).)

Introduction: This study addresses the lack of methods to quantify driver familiarity with roadways, which poses a higher risk of crashes., Method: We present a new approach to assessing driving route diversity and familiarity using data from the DrivingApp, a smartphone-based research tool that collects trip-level information, including driving exposure and global positioning system (GPS) data, from young novice drivers (15-19 years old) to older drivers (67-78 years old). Using these data, we developed a GPS data-based algorithm to analyze the uniqueness of driving routes. The algorithm creates same route trip (SRT) arrays by comparing each trip of an identified user, employing statistically determined thresholds for GPS coordinate proximity and trip overlap. The optimal thresholds were established using a General Linear Model (GLM) to examine distance, and repeated observations. The Adjusted Breadth-First Search method is applied to the SRT arrays to prevent double counting or trip omission. The resulting list is classified as geographically distinct routes, or unique routes (URs)., Results: Manual comparison of algorithm output with geographical maps yielded an overall precision of 0.93 and accuracy of 0.91. The algorithm produces two main outputs: a measure of driving diversity (number of URs) and a measure of route-based familiarity derived from the Rescorla-Wagner model. To evaluate the utility of these measures, a Gaussian mixture model clustering algorithm was used on the young novice driver dataset, revealing two distinct groups: the low-frequency driving group with lower route familiarity when having higher route diversity, whereas the high-frequency driving group with the opposite pattern. In the older driver group, there was a significant correlation found between the number of URs and Geriatric Depression Score, or walking gait speed., Practical Applications: These findings suggest that route diversity and familiarity could complement existing measures to understand driving safety and how driving behavior is related to physical and psychological outcomes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Introduction: An on-road study was conducted to examine the effects of level 2 automation on the stressfulness and enjoyment of driving and driving attention following prolonged usage. The study also examined the changes in the automated driving experience and attention over time as well as important predictors such as pre-driving trust in technology and attitudes toward automated systems., Method: Motorists who had never used automated systems drove a level 2 automation vehicle for a 6-8 week period., Results: Participants reported that the automated systems reduced the stress of driving and made traveling more enjoyable and relaxing. They also reported that the automation did not make traveling boring and take the fun out of driving. Participants indicated that their minds tended to wander when the automation was operating. The stressfulness of the automated driving experience decreased over time. Participants also reported feeling increasingly comfortable driving with the automation without monitoring it closely. The enjoyment and stress of automated driving is important because it shapes the willingness to use the automation and, hence, the safeness of driving. As expected, intentions to use and purchase automated systems were strongly predicted by the perceived favorableness of driving with the automation. Participants' pre-driving beliefs about automated systems, rather than their trust, appears to have shaped their experiences with the automation., Practical Applications: Although some of the findings suggest that automated systems increase unsafe behavior by novice users, other facets of the surveys suggest that motorists are cognizant of the risks of automated driving and discreet in their usage of the automation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Ltd.)

Introduction: Trait driving anger is a widely studied personality variable in the field of road safety, due to its strong relationship with both risky behavior on the road and crash-related events. The Deffenbacher's Driving Anger Scale theoretical approach has underlined different situations that could provoke anger in drivers, although trait driving anger is usually analyzed as a whole. Trait general anger has been proposed as one of the most relevant predictors of trait driving anger, showing moderate relationships with it., Method: The current research aimed to analyze the relationship between trait general anger and each one of the situations provoking anger, as well as to search for personality variables that could moderate these relationships. Based on literature review, it was expected that self-esteem would moderate both Discourtesy and Hostile gestures, Type-A behavior pattern would moderate both Slow driving and Traffic obstructions, and conscientiousness would moderate both Police presence and Illegal driving. A sample of 417 drivers (M age = 31.24, SD age = 13.59, 64.5% females) taken from the Spanish general population completed a set of self-reports., Results: The results showed significant moderation effects in the case of Hostile gestures, Discourtesy, Illegal driving, and Slow driving. Conditional processes of these moderations were analyzed. Lastly, practical implications are discussed, allowing for tailored interventions to be implemented based on individual drivers' tendencies. Therefore, interventions should address different triggers of driving anger: boosting self-esteem for those angered by disrespect, targeting Type-A behavior reduction for those angered by traffic slowdowns, and promoting conscientiousness enhancement for those angered by others' risky driving., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Introduction: Despite deployed efforts to establish strict road safety standards, human factors is still the leading cause of road crashes. To identify determinants of driver's behavior, TPB (Theory of Planned Behavior) is widely used as a prominent theory of behavior change. However, the existence of different aberrant driving behaviors (decision errors, recognition errors, violations, and physical condition related errors) and several studies using TPB to understand driving behavior, makes it important to conduct a literature review and a meta-analysis of existing studies to use their results in effective driving behavior change interventions., Method: The selection process provided 125 relevant studies that were published between 1991 and 2022, and that used TPB for the understanding of aberrant driving behavior. Five fundamental research questions were defined to identify information to be discovered from the literature review and from the meta-analysis., Results: In addition to the standard TPB constructs (attitudes, subjective norms, and perceived behavioral control), past behavior, moral norms, and descriptive norms were used in studies for a more comprehensive understanding of aberrant driving intention. This analysis demonstrated a significant correlation between aberrant driving intentions and past behavior. Also, moral norms construct was correlated with violations and recognition errors, whereas descriptive norms construct was correlated just with recognition errors., Conclusions: The results of this study highlight the strength of TPB in the prediction of aberrant driving intention and its potential effectiveness to guide interventions aimed at changing aberrant driving behaviors. The study contributes to the comprehension of the relevant psychological factors influencing the engagement of drivers in each category of aberrant driving behaviors., Practical Applications: Researchers can use the results of this study to select the relevant psychological factors adapted to their interventions of driving behavior change. The results of the meta-analysis can also be used in the prediction of driver's intentions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 National Safety Council and Elsevier Ltd. All rights reserved.)

Introduction: Lane departure collisions account for many roadway fatalities across the United States. Many of these crashes occur on horizontal curves or ramps and are due to speeding. This research investigates factors that impact the odds of speeding on Interstate horizontal curves and ramps., Method: We collected and combined two unique sources of data. The first database involves comprehensive curve and ramp characteristics collected by an automatic road analyzer (ARAN) vehicle; the second database includes volume, average speed, and speed distribution gathered from probe data provided by StreetLight Insight®. We evaluated the impacts of level of service (LOS), which reflects traffic density or level of congestion, time of the day (morning, evening, and off-peak hours), time of the week (weekdays and weekends), and month of the year (Jan-Dec), and various information about geometric characteristics, such as curve radius, arc angle, and superelevation, on odds of speeding., Results: The results show that the odds of speeding increases at horizontal curves with improved levels of service, as well as those with larger radii and superelevation. The odds of speeding decreases on curves with larger arc angles and during the winter months of the year. The findings indicate a reduction in odds of speeding at diagonal/loop ramps with larger arc angles and narrower lane widths., Conclusion: The results show the importance of using speed enforcement and other countermeasures to reduce speeding on curves with low traffic volumes, high speed limits, and large radius and superelevation, especially for those in rural areas., Practical Application: The results could be used to prioritize locations for the installation of speed countermeasures or dispatch enforcement resources to high-priority locations and times., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 National Safety Council and Elsevier Ltd. All rights reserved.)

Introduction: This study presents a comprehensive analysis of wrong-way driving (WWD) fatal crashes on divided highways in the United States over a 17-year period, from 2004 to 2020. The study aims to uncover trends, distribution patterns, and factors contributing to these fatal crashes. Data were extracted from the National Highway Traffic Safety Administration (NHTSA) Fatality Analysis Reporting System (FARS) database., Methods: Descriptive statistical analysis was used to reveal general crash characteristics, while trends were updated through an examination of the annual occurrence of WWD fatal crashes. The study further employed binomial logistic regression to compute odds ratios, identifying significant contributing factors. These factors encompassed temporal variables, crash characteristics, and driver characteristics. The odds ratios shed light on the relationship between WWD fatal crashes and other fatal crashes, allowing for the identification of key elements that drive WWD incidents., Results: On average, 302 WWD fatal crashes occurred annually, resulting in 6,953 fatalities during the study period. The frequency of WWD fatal crashes remained relatively stable, with a slight increase over time. According to the model, variables include day of week, time of day, month, lighting conditions, weather conditions, roadway profile, collision type, passenger presence, driver age, gender, license status, and driver injury severity were found to significantly impact the occurrence of WWD fatal crashes. One significant finding is that road profiles like sag curves or hillcrests can increase the likelihood of WWD fatal crashes., Practical Application: The findings of this study contribute to an improved understanding of WWD fatal crashes on divided highways, thereby aiding in the development of strategies for prevention and mitigation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 National Safety Council and Elsevier Ltd. All rights reserved.)

Introduction: Road crashes are still one of the main causes of death around the world. Risky behavior has been proposed as one of the foremost predictors, with the theoretical framework of aberrant behavior emerging as a predominant approach for its examination. Sensation seeking has been pointed out as one of the main personality predictors of aberrant behavior. The current research aimed to investigate the moderated-moderation effect of both risk perception and self-esteem in the relationship between sensation seeking and aberrant behavior., Method: Two studies were conducted. The first study aimed to analyze the psychometric properties of the Spanish version of the Risk Perception Scale (RPS), a 10-item self-report to assess risk perception. A sample composed of 471 Spanish drivers (319 female, M age  = 29.75) completed the RPS. In the second study, a different sample of 236 Spanish drivers (129 female, M age  = 38.49) completed a set of self-reports aiming both to analyze the concurrent and divergent validity of the RPS, and to test the main moderated-moderation hypothesis., Results: With respect to the first study, the confirmatory factor analysis (CFA) supported a 7-item version which fitted in a single reliable factor (α = .74). Regarding the second study, the results supported both the concurrent and divergent validity of the RPS. Likewise, it was verified the moderated-moderation effect in the case of ordinary violations (R 2  = .34), aggressive violations (R 2  = .20), and lapses (R 2  = .12)., Conclusions: The RPS is a useful self-report to assess subjective risk perception in Spanish drivers. Both self-esteem and risk perception affect the relationship between sensation seeking and aberrant driving behavior., Practical Implications: Intervention programs aiming to reduce aberrant driving behavior should be focused on reducing sensation seeking tendencies while simultaneously enhancing both risk perception skills and self-esteem., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd and National Safety Council. All rights reserved.)

Introduction: Previous research has identified the perception of penalties as one of the most important deterrents to road traffic offenses. This study investigated whether the perceived effectiveness and the perceived strictness of penalties for different traffic offenses are associated with prior punishment experience and evaluation perspective (personal - if participants were being punished themselves, vs. general - for punishing all drivers)., Method: A convenience sample of 1,374 Lithuanian drivers participated in the survey (56.3% males; aged 18-77 years). Among them, 801 participants had no penalties for traffic offenses, 333 reported monetary fines in the last year, and 240 reported having their drivers license suspended at least once in their driving career. A scale with 10 specific penalties for traffic offenses was developed to measure the perceived effectiveness and strictness of penalties. Half of the participants evaluated the penalties as if they were personally sanctioned for each traffic offense (personal perspective), while the other half assessed the effectiveness and strictness of the penalties for drivers in general., Results: The results indicated that the perceived effectiveness of penalties was related to the evaluation perspective, being higher for changing one's own behavior than for changing behavior in general. However, the perceived effectiveness of penalties was not related to prior punishment experience. Males and drivers who had experienced their license being suspended reported the highest perceived strictness of the penalties, whereas females and drivers with no prior punishment experience perceived penalties as the least strict., Practical Applications: These findings suggest the need for developing new ways of communicating penalties to different groups of drivers. Nevertheless, this research was exploratory in nature and further research is warranted., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 National Safety Council and Elsevier Ltd. All rights reserved.)

Introduction: This paper presents a comprehensive investigation into the current and emerging solutions, policies, and guidance employed by various agencies to mitigate wrong-way driving (WWD) activities in the United States. The study utilized a two-pronged approach, involving an online survey and follow-up phone interviews with respondents from state transportation agencies, tollway authorities, and law enforcement., Methods: The initial step involved conducting an online survey to gather general insights about the existing strategies and practices used to combat WWD. The survey questionnaire, consisting of 12 questions, covered topics such as mitigation strategies/policies, guidance for selecting countermeasures, and topics/needs for national handbook. The survey was emailed to traffic and safety engineers from all 50 state transportation agencies and 59 tollway authorities across the nation. As the second step, follow-up phone interviews were conducted with respondents identified from the online survey. The interviews delved deeper into specific aspects such as crash/incident data collection methods, identification of crash-prone locations, countermeasure selection and implementation, experience with Intelligent Transportation Systems (ITS) applications, and future initiatives., Results: The findings from the survey and interviews indicated an increasing awareness and adoption of best practices to combat WWD. Various states have implemented new policies and advanced technologies to deter WWD incidents. The insights gathered from the survey and interviews with different agencies are invaluable in shaping safe system approaches and guidelines for the national handbook on WWD solutions., Practical Applications: Overall, this study sheds light on the efforts and progress made by state transportation agencies, tollway authorities, and law enforcement in addressing the critical issue of WWD. By gathering valuable lessons and practices from the various agencies, this research lays the groundwork for developing national guidelines to reduce WWD crashes and incidents on divided highways., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 National Safety Council and Elsevier Ltd. All rights reserved.)

Introduction: Engagement in hand-held phone use while driving among young drivers is a prevalent concern in society, despite countermeasures to deter the behavior. The social norm approach has been effective in reducing negative behaviors in young adults (e.g., binge drinking, drink driving). However, whether this approach can reduce hand-held phone use while driving in this population has not been thoroughly investigated., Method: The qualitative study explored young drivers' attitudes and opinions on social norm messages designed to reduce hand-held phone use while driving. In addition, young drivers' opinions on current campaigns were explored to provide further insight into the effectiveness of these messages. Thirty young drivers were interviewed and shown six social norm messages., Results: The data were analyzed using reflexive thematic analysis, resulting in five themes and one sub-theme: (1) Road safety messages with minimal impact on hand-held phone use while driving; (2) What constitutes an effective road safety message for hand-held phone use while driving; (3) Comparisons between social norm messages and road safety messages; (4) The potential benefits of combined social norms, (4a) Improving and optimizing the message; and (5) "It's kinda just numbers on a screen": Negative views on social norm messages. Results highlight the diverse opinions towards road safety campaigns and the need to increase exposure to these messages. Further, a combined social norm message was perceived as most effective in reducing engagement in hand-held phone use while driving., Conclusions: The current study provides preliminary evidence that the social norm approach may be effective in reducing hand-held phone use among young drivers. Further, this study highlights the need to maximize exposure to phone use while driving campaigns in this high-risk cohort., Practical Applications: Results support the development of a social norm messaging intervention to reduce young drivers hand-held phone use while driving., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Introduction: Driver anger and aggression have been linked to crash involvement and injury outcomes. Improved road safety outcomes may be achieved through understanding the causes of driver anger, and interventions designed to reduce this anger or prevent it from becoming aggression. Scales to measure anger propensities will be an important tool in this work. The measure for angry drivers (MAD; Stephens et al., 2019) is a contemporary scale designed to measure tendencies for anger across three types of driving scenarios: perceived danger from others, travel delays, and hostility or aggression from other drivers., Method: This study aimed to validate MAD using a representative sample of Australian drivers, stratified across age, gender, and location. Participants completed a 10-minute online survey that included MAD, sought demographic information (age, gender, driving purpose, crash history), as well as the frequency of aggressive driving. Multigroup confirmatory factor analyses (MGCFA) assessed how stable the structure of the MAD was across drivers of different ages, gender, purposes for driving and those who do or do not display anger aggressively. MAD was invariant across all groups, showing that all drivers interpreted and responded to MAD in the same way., Results: A comparison of latent means showed anger tendencies were higher for men compared to women, for younger drivers compared to older drivers, and for those who drive mainly for work compared to those who mainly drive for other reasons. When controlling for driver factors, driving anger was associated with increased odds of being aggressive while driving., Practical Applications: Overall, this study demonstrated that MAD is an appropriate scale to measure anger tendencies and can be used to support interventions, and evaluation of interventions, to reduce anger and aggressive driving., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Introduction: Vehicles driving, or being swept, into floodwaters is a leading cause of flood-related death. Establishing safe behaviors among learner drivers may reduce risk throughout their driving lifetime., Methods: An environmental scan of publicly available government issued learner and driver handbooks across the eight Australian jurisdictions was conducted to identify information provided regarding floodwaters. Search terms included 'flood,' 'rain,' 'water,' and 'wet.' A visual audit of flood-related signage was also conducted., Results: Twelve documents, across eight jurisdictions, were analyzed. Four jurisdictions' documents provided no information on flooding. Of the four jurisdictions that provided information, content varied. This included highlighting risks and discouraging entering floodwaters in a vehicle, including penalties associated with travel on closed roads, to advising depth and current checks if crossing a flooded roadway, with recommendations based on vehicle size (preference given to bigger vehicles, i.e., 4wds). Information on flood-related signage was found in one jurisdiction., Discussion: Learner and driver handbooks represent a missed opportunity to provide flood safety information. Currently, information is not provided in all jurisdictions, despite flood-related vehicle drowning deaths of drivers and passengers being a national issue. Where information is presented, it is limited, often lacks practical guidance on how to assess water depth, current, and road base stability, and could better use evidence regarding the psychological factors underpinning, and behavioral prompts for performing, or avoiding, risky driving behavior during floods., Conclusions: The provision and content of information in learner driver and driver handbooks must be improved, particularly within the context of increasing flooding and extreme weather associated with the effects of climate change., Practical Applications: We encourage all jurisdictions to provide practical information that draws on evidence-based risk factors and empirically established psychological factors for behavioral change to help establish safe driver behaviors around floods in the formative years of learning to drive., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Electric vehicles (EVs), which are a great substitute for gasoline-powered vehicles, have the potential to achieve the goal of reducing energy consumption and emissions. However, the energy consumption of an EV is highly dependent on road contexts and driving behavior, especially at urban intersections. This paper proposes a novel ecological (eco) driving strategy (EDS) for EVs based on optimal energy consumption at an urban signalized intersection under moderate and dense traffic conditions. Firstly, we develop an energy consumption model for EVs considering several crucial factors such as road grade, curvature, rolling resistance, friction in bearing, aerodynamics resistance, motor ohmic loss, and regenerative braking. For better energy recovery at varying traffic speeds, we employ a sigmoid function to calculate the regenerative braking efficiency rather than a simple constant or linear function considered by many other studies. Secondly, we formulate an eco-driving optimal control problem subject to state constraints that minimize the energy consumption of EVs by finding a closed-form solution for acceleration/deceleration of vehicles over a time and distance horizon using Pontryagin's minimum principle (PMP). Finally, we evaluate the efficacy of the proposed EDS using microscopic traffic simulations considering real traffic flow behavior at an urban signalized intersection and compare its performance to the (human-based) traditional driving strategy (TDS). The results demonstrate significant performance improvement in energy efficiency and waiting time for various traffic demands while ensuring driving safety and riding comfort. Our proposed strategy has a low computing cost and can be used as an advanced driver-assistance system (ADAS) in real-time., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective: Using brain haemodynamic responses to measure perceived risk from traffic complexity during automated driving., Background: Although well-established during manual driving, the effects of driver risk perception during automated driving remain unknown. The use of fNIRS in this paper for assessing drivers' states posits it could become a novel method for measuring risk perception., Methods: Twenty-three volunteers participated in an empirical driving simulator experiment with automated driving capability. Driving conditions involved suburban and urban scenarios with varying levels of traffic complexity, culminating in an unexpected hazardous event. Perceived risk was measured via fNIRS within the prefrontal cortical haemoglobin oxygenation and from self-reports., Results: Prefrontal cortical haemoglobin oxygenation levels significantly increased, following self-reported perceived risk and traffic complexity, particularly during the hazardous scenario., Conclusion: This paper has demonstrated that fNIRS is a valuable research tool for measuring variations in perceived risk from traffic complexity during highly automated driving. Even though the responsibility over the driving task is delegated to the automated system and dispositional trust is high, drivers perceive moderate risk when traffic complexity builds up gradually, reflected in a corresponding significant increase in blood oxygenation levels, with both subjective (self-reports) and objective (fNIRS) increasing further during the hazardous scenario., Application: Little is known regarding the effects of drivers' risk perception with automated driving. Building upon our experimental findings, future work can use fNIRS to investigate the mental processes for risk assessment and the effects of perceived risk on driving behaviours to promote the safe adoption of automated driving technology.

Over the past decade, the rapid development of artificial intelligence has propelled the transition of autonomous vehicles from laboratories to real-world applications. However, autonomous vehicles are a long way from fully integrating into most people's lives. Previous studies indicate that the word-of-mouth effect is often used by consumers to determine the quality of innovative technologies. Word-of-mouth recommendation can not only increase the income of enterprises by attracting new customers, but also greatly reduce the promotion and publicity expenses of enterprises. Through the word-of-mouth effect, the intention to recommend can contribute to the growth of the autonomous driving market. Therefore, current research explores the mechanisms among the perceived risk of privacy safety, perceived defect, perceived behavioral control, intention to use, and intention to recommend through path analysis. Our findings, based on 433 online questionnaires, indicate that the perceived risk of privacy safety, perceived defects, and perceived behavioral control influence the intention to recommend. Notably, perceived risk of privacy safety and perceived defect directly affects the intention to recommend and also correlates with perceived behavioral control. These findings provide some empirical evidence for the recommendation of autonomous vehicles and the expansion of consumer groups., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

This paper focuses on the cooperative driving challenges of connected and automated vehicles (CAVs) at single-lane roundabouts. First, a geometric path planning method is proposed for CAVs navigating a single-lane roundabout. Based on this method, a vehicle roundabout model is established. Four potential traffic scenarios for CAVs are established, and the optimal arrival times at conflict points are analyzed. By correlating the optimal arrival times at conflict points with the optimal entry times into the roundabout, the multi-vehicle coordination problem in complex intersections is simplified to a speed control issue during entry. Utilizing the principles of optimal control and Pontryagin minimization, two speed optimization strategies are proposed. Finally, MATLAB is employed for simulation analysis. The results indicate that the control strategy proposed in this paper enables the system to clearly identify potential conflicts between vehicles and implement an optimal control strategy, ensuring that vehicles can navigate the roundabout efficiently in terms of time and fuel without collisions. Additionally, the minimum time interval is established at 0.2 seconds to completely prevent vehicle collisions. In this study, the fusion problem involving two vehicles at a single conflict point is further expanded to encompass multiple vehicles at multiple conflict points. Thus, the efficient scheduling of multiple vehicles in single-lane roundabouts is realized., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The paper evaluates the DARS Traffic Plus mobile application within a realistic driving simulator environment to assess its impact on driving safety and user experience, particularly focusing on the Cooperative Intelligent Transport Systems (C-ITS). The study is positioned within the broader context of integrating mobile technology in vehicular environments to enhance road safety by informing drivers about potential hazards in real time. A combination of experimental methods was employed, including a standardised user experience questionnaire (meCUE 2.0), measuring quantitative driving parameters and eye-tracking data within a driving simulator, and post-experiment interviews. The results indicate that the mobile application significantly improved drivers' safety perception, particularly when notifications about hazardous locations were received. Notifications displayed at the top of the mobile screen with auditory cues were deemed most effective. The study concludes that mobile applications like DARS Traffic Plus can play a crucial role in enhancing road safety by effectively communicating hazards to drivers, thereby potentially reducing road accidents and improving overall traffic safety. Screen viewing was kept below the safety threshold, affirming the app's efficacy in delivering crucial information without distraction. These findings support the integration of C-ITS functionalities into mobile applications as a means to augment older vehicle technologies and extend the safety benefits to a broader user base.

Virtual reality (VR) driving simulators are very promising tools for driver assessment since they provide a controlled and adaptable setting for behavior analysis. At the same time, wearable sensor technology provides a well-suited and valuable approach to evaluating the behavior of drivers and their physiological or psychological state. This review paper investigates the potential of wearable sensors in VR driving simulators. Methods: A literature search was performed on four databases (Scopus, Web of Science, Science Direct, and IEEE Xplore) using appropriate search terms to retrieve scientific articles from a period of eleven years, from 2013 to 2023. Results: After removing duplicates and irrelevant papers, 44 studies were selected for analysis. Some important aspects were extracted and presented: the number of publications per year, countries of publication, the source of publications, study aims, characteristics of the participants, and types of wearable sensors. Moreover, an analysis and discussion of different aspects are provided. To improve car simulators that use virtual reality technologies and boost the effectiveness of particular driver training programs, data from the studies included in this systematic review and those scheduled for the upcoming years may be of interest.

The driver in road hypnosis has not only some external characteristics, but also some internal characteristics. External features have obvious manifestations and can be directly observed. Internal features do not have obvious manifestations and cannot be directly observed. They need to be measured with specific instruments. Electroencephalography (EEG), as an internal feature of drivers, is the golden parameter for drivers' life identification. EEG is of great significance for the identification of road hypnosis. An identification method for road hypnosis based on human EEG data is proposed in this paper. EEG data on drivers in road hypnosis can be collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with the PSD (power spectral density) method, and EEG characteristics are extracted. The neural networks EEGNet, RNN, and LSTM are used to train the road hypnosis identification model. It is shown from the results that the model based on EEGNet has the best performance in terms of identification for road hypnosis, with an accuracy of 93.01%. The effectiveness and accuracy of the identification for road hypnosis are improved in this study. The essential characteristics for road hypnosis are also revealed. This is of great significance for improving the safety level of intelligent vehicles and reducing the number of traffic accidents caused by road hypnosis.

Traffic accidents due to fatigue account for a large proportion of road fatalities. Based on simulated driving experiments with drivers recruited from college students, this paper investigates the use of heart rate variability (HRV) features to detect driver fatigue while considering sex differences. Sex-independent and sex-specific differences in HRV features between alert and fatigued states derived from 2 min electrocardiogram (ECG) signals were determined. Then, decision trees were used for driver fatigue detection using the HRV features of either all subjects or those of only males or females. Nineteen, eighteen, and thirteen HRV features were significantly different (Mann-Whitney U test, p < 0.01) between the two mental states for all subjects, males, and females, respectively. The fatigue detection models for all subjects, males, and females achieved classification accuracies of 86.3%, 94.8%, and 92.0%, respectively. In conclusion, sex differences in HRV features between drivers' mental states were found according to both the statistical analysis and classification results. By considering sex differences, precise HRV feature-based driver fatigue detection systems can be developed. Moreover, in contrast to conventional methods using HRV features from 5 min ECG signals, our method uses HRV features from 2 min ECG signals, thus enabling more rapid driver fatigue detection.

With the era of automated driving approaching, designing an effective auditory takeover request (TOR) is critical to ensure automated driving safety. The present study investigated the effects of speech-based (speech and spearcon) and non-speech-based (earcon and auditory icon) TORs on takeover performance and subjective preferences. The potential impact of the non-driving-related task (NDRT) modality on auditory TORs was considered. Thirty-two participants were recruited in the present study and assigned to two groups, with one group performing the visual N-back task and another performing the auditory N-back task during automated driving. They were required to complete four simulated driving blocks corresponding to four auditory TOR types. The earcon TOR was found to be the most suitable for alerting drivers to return to the control loop because of its advantageous takeover time, lane change time, and minimum time to collision. Although participants preferred the speech TOR, it led to relatively poor takeover performance. In addition, the auditory NDRT was found to have a detrimental impact on auditory TORs. When drivers were engaged in the auditory NDRT, the takeover time and lane change time advantages of earcon TORs no longer existed. These findings highlight the importance of considering the influence of auditory NDRTs when designing an auditory takeover interface. The present study also has some practical implications for researchers and designers when designing an auditory takeover system in automated vehicles., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This study investigates the impact of advanced driver-assistance systems on drivers' mental workload. Using a combination of physiological signals including ECG, EMG, EDA, EEG (af4 and fc6 channels from the theta band), and eye diameter data, this study aims to predict and categorize drivers' mental workload into low, adequate, and high levels. Data were collected from five different driving situations with varying cognitive demands. A functional linear regression model was employed for prediction, and the accuracy rate was calculated. Among the 31 tested combinations of physiological variables, 9 combinations achieved the highest accuracy result of 90%. These results highlight the potential benefits of utilizing raw physiological signal data and employing functional data analysis methods to understand and assess driver mental workload. The findings of this study have implications for the design and improvement of driver-assistance systems to optimize safety and performance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Human-level driving is the ultimate goal of autonomous driving. As the top-level decision-making aspect of autonomous driving, behavior decision establishes short-term driving behavior strategies by evaluating road structures, adhering to traffic rules, and analyzing the intentions of other traffic participants. Existing behavior decisions are primarily implemented based on rule-based methods, exhibiting insufficient generalization capabilities when faced with new and unseen driving scenarios. In this paper, we propose a novel behavior decision method that leverages the inherent generalization and commonsense reasoning abilities of visual language models (VLMs) to learn and simulate the behavior decision process in human driving. We constructed a novel instruction-following dataset containing a large number of image-text instructions paired with corresponding driving behavior labels, to support the learning of the Drive Large Language and Vision Assistant (DriveLLaVA) and enhance the transparency and interpretability of the entire decision process. DriveLLaVA is fine-tuned on this dataset using the Low-Rank Adaptation (LoRA) approach, which efficiently optimizes the model parameter count and significantly reduces training costs. We conducted extensive experiments on a large-scale instruction-following dataset, and compared with state-of-the-art methods, DriveLLaVA demonstrated excellent behavior decision performance. DriveLLaVA is capable of handling various complex driving scenarios, showing strong robustness and generalization abilities.

With the rapid development of intelligent connected vehicles, there is an increasing demand for hardware facilities and onboard systems of driver assistance systems. Currently, most vehicles are constrained by the hardware resources of onboard systems, which mainly process single-task and single-sensor data. This poses a significant challenge in achieving complex panoramic driving perception technology. While the panoramic driving perception algorithm YOLOP has achieved outstanding performance in multi-task processing, it suffers from poor adaptability of feature map pooling operations and loss of details during downsampling. To address these issues, this paper proposes a panoramic driving perception fusion algorithm based on multi-task learning. The model training involves the introduction of different loss functions and a series of processing steps for lidar point cloud data. Subsequently, the perception information from lidar and vision sensors is fused to achieve synchronized processing of multi-task and multi-sensor data, thereby effectively improving the performance and reliability of the panoramic driving perception system. To evaluate the performance of the proposed algorithm in multi-task processing, the BDD100K dataset is used. The results demonstrate that, compared to the YOLOP model, the multi-task learning network performs better in lane detection, drivable area detection, and vehicle detection tasks. Specifically, the lane detection accuracy improves by 11.6%, the mean Intersection over Union (mIoU) for drivable area detection increases by 2.1%, and the mean Average Precision at 50% IoU (mAP50) for vehicle detection improves by 3.7%., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: The occupational road-accident risk on public roads and the work conditions for professional driving is still an important issue in occupational health despite lower road-accident rates. This study presents the evolution over time of the work-related constraints for these employees based on the Sumer surveys carried out in 2003, 2010 and 2017., Method: Data from the 2010 and 2017 surveys were restricted to match the scope of the 2003 survey in order to enable prevalence data to be compared in equivalent populations. The main variable of interest was "driving (car, truck, bus, and other vehicles) on public thoroughfares" for work (during the last week of work: yes/no). Work time characteristics, work rhythm, autonomy and scope for initiative, collective work group, standards and evaluations variables were completed by the occupational health physicians. A self-administered questionnaire was also provided to employees and contained the Job Content Questionnaire, which assesses decision latitude, social support and psychological demands, the reward scale of Siegrist questionnaire, the hostile behaviour with inspired questions for Leymann, sick leave and work accidents during the past 12 months and job satisfaction. Finally, prevention in the workplace was also completed by the occupational health physicians., Results: About 25% of employees in France were exposed to work-related driving in 2017, which was stable in comparison with 2003 and 2010. However, the population was older and there were more females, more often from the clerical staff/middle manager category and working in companies with fewer than 10 employees. Employees exposed to work-related driving were also more frequently exposed to sustained work schedules and physical constraints, but less exposed to psychosocial risks., Conclusions: The percentage of employees exposed to occupational road accident risk, i.e., exposure to work-related driving, remained stable at about 25% in 2017 compared with previous surveys. These employees were also more frequently exposed to sustained work schedules and physical constraints, but less exposed to psychosocial risks., Practical Applications: Prevention campaigns on work-related road accident risk should be provided to all employees in all companies since all jobs can be concerned., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Introduction: Almost a third of car accidents involve driving after alcohol consumption. Autonomous vehicles (AVs) may offer accident-prevention benefits, but at current automation levels, drivers must still perform manual driving tasks when automated systems fail. Therefore, understanding how alcohol affects driving in both manual and automated contexts offers insight into the role of future vehicle design in mediating crash risks for alcohol-impaired driving., Method: This study conducted a systematic review on alcohol effects on manual and automated (takeover) driving performance. Fifty-three articles from eight databases were analyzed, with findings structured based on the information processing model, which can be extended to the AV takeover model., Results: The literature indicates that different Blood Alcohol Concentration (BAC) levels affect driving skills essential for traffic safety at various information processing stages, such as delayed reacting time, impaired cognitive abilities, and hindered execution of driving tasks. Additionally, the driver's driving experience, drinking habits, and external driving environment play important roles in influencing driving performance., Conclusions: Future work is needed to examine the effects of alcohol on driving performance, particularly in AVs and takeover situations, and to develop driver monitoring systems., Practical Applications: Findings from this review can inform future experiments, AV technology design, and the development of driver state monitoring systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 National Safety Council and Elsevier Ltd. All rights reserved.)

Introduction: This study investigates the effect among commercial motor vehicle (CMV) drivers of the adoption of fuel-efficient driving techniques (commonly known as eco-driving) on the odds of being involved in safety-related events., Method: For 2,637 long-haul class 8 drivers employed by four carriers in Canada, information on driving style, total distance driven, and safety-related events like collisions, hard-braking, hard-turning, and stability control events were collected for each trip. Three carriers provided driving style-related data from the ISAAC instrument, which provides a score on a 0 to 100 scale that measures the degree to which a driver is using an appropriate amount of engine power according to driving conditions. The fourth carrier provided data on driving style characteristics, including fuel consumption, use of cruise control, and use of top gear. Depending on the carrier, information on speeding, driver age, and years of experience driving a commercial vehicle was also collected. Logit statistical models were developed to estimate the change in odds of a driver experiencing a safety-related event dependent on the measures of driving style., Results: A one-unit increase in the ISAAC score was associated with a 7%, 8%, 8%, and 4% reduction in the odds of having a hard-braking event, hard left-turn event, hard right-turn event, and collision, respectively. For the carrier not employing the ISAAC system, an increase of 10% in the time spent driving in top gear with steady speed near 100 km per hour (km/h) was associated with a substantial 34% decrease in stability control events. In addition, a year increase in the driver's age, as well as a 1% increase in the amount of time spent driving using cruise control, reduced the number of hard-braking events by 9% and 3%, respectively. Conclusion/Practical Applications: The adoption of fuel-efficient driving techniques enhances the safety of CMV drivers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 National Safety Council and Elsevier Ltd. All rights reserved.)

Introduction: Speeding behavior is a major threat to road traffic safety, which can increase crash risks and result in severe injury outcomes. Although several studies have been conducted to analyze speeding crashes and relevant influential factors, the heterogeneity of variables has not been fully explored. Based on the traffic crash data extracted from the Crash Report Sampling System, the study aims to identify the factors that influence speeding driving with the consideration of variable heterogeneity., Method: Quasi-induced exposure technique is adopted to identify the disparities in the propensities of speeding for various driving cohorts. The random parameter logit model with heterogeneity in means is employed to examine the factors impacting speeding behavior., Results: Results indicate that: (a) driving cohorts such as young drivers, male drivers, passenger cars, and pickups appear to have higher propensities of engaging in speeding driving; (b) the propensity of speeding is higher when the driver is drinking, distracted, changing lanes, negotiating a curve, driving in lighted condition, and on curved roads; and (c) the random parameter logit model with heterogeneity in means has better performance as opposed to that without heterogeneity in means., Conclusions: Speeding behavior can be influenced by various factors in terms of driver-vehicle characteristics, physical condition, driving actions, and environmental conditions., Practical Applications: The findings could serve to develop effective countermeasures to reduce speeding behavior and improve traffic safety., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 National Safety Council and Elsevier Ltd. All rights reserved.)

Driver fatigue is a major contributor to road accidents. Therefore, driver assistance systems (DAS) that would monitor drivers' states may contribute to road safety. Such monitoring can potentially be achieved with input from ECG indices (e.g., heart rate). We reviewed the empirical literature on responses of cardiac measures to driver fatigue and on detecting fatigue with cardiac indices and classification algorithms. We used meta-analytical methods to explore the pooled effect sizes of different cardiac indices of fatigue, their heterogeneity, and the consistency of their responses across studies. Our large pool of studies (N = 39) allowed us to stratify the results across on-road and simulator studies. We found that despite the large heterogeneity of the effect sizes between the studies, many indices had significant pooled effect sizes across the studies, and more frequently across the on-road studies. We also found that most indices showed consistent responses across both on-road and simulator studies. Regarding the detection accuracy, we found that even on-road classification could have been as accurate as 70% with only 2-min of data. However, we could only find two on-road studies that employed fatigue classification algorithms. Overall, our findings are encouraging with respect to the prospect of using cardiac measures for detecting driver fatigue. Yet, to fully explore this possibility, there is a need for additional on-road studies that would employ a similar set of cardiac indices and detection algorithms, a unified definition of fatigue, and additional levels of fatigue than the two fatigue vs alert states., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Driving style has been proposed to be a critical factor in automated driving. However, the role of driving style in the process of taking over during automated driving needs further investigation. The main purpose of this study was to investigate the influence of driving style on takeover performance under the influence of warning system factors. In addition, this study also explored whether the impact of driving style on reaction time varies over time and the role of driving style on a comprehensive takeover quality indicator. Two driving simulation experiments with different takeover request (TOR) designs were conducted. In experiment 1, content warning information was provided in the TOR with different warning stage designs; in experiment 2, countdown warning information was provided in the TOR with different warning stage designs. Sixty-four participants (32 for experiment 1 and 32 for experiment 2) were classified into two groups based on their driving style (i.e., aggressive, or defensive) using the Chinese version of the Multidimensional Driving Style Inventory (the brief MDSI-C). The results suggested that drivers' driving style had significant effects on takeover performance, but the effects were influenced by warning system designs. Specifically, defensive participants performed better takeover performance, i.e., shorter reaction time and cautious vehicle control behaviors, than aggressive participants in most warning conditions. The content and countdown warning information and warning stage design affected the roles of driving style on takeover performance: 1) compared to the one-stage warning design, the two-stage warning design significantly shortened the reaction time of the participants with different driving styles, 2) compared to the countdown warning information design, the design of content warning information can shorten the reaction time of aggressive participants and lengthen the reaction time of defensive participants in the two-stage warning conditions, and 3) compared to the content warning information design, countdown warning information can improve the safe takeover performance of defensive participants. This study provides a better understanding of the role of driving style on takeover performance, and driving style should be considered when designing warning systems for autonomous vehicles., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

To investigate the impact of environmental noise on the cognitive abilities of drivers, this study, using in-vehicle voice interaction as an example, conducted laboratory experiments to assess the effects of road traffic noise, entertainment noise, and white noise stimuli on drivers' attention and short-term memory. The noise levels simulated to mimic acoustic conditions during car driving ranged from 35 dB(A) to 65 dB(A). The conclusions drawn were as follows: (1) Noise levels directly influenced subjective annoyance levels, with annoyance linearly increasing as noise levels escalated; (2) Both attention and short-term memory task reaction times of drivers were significantly influenced by noise types. Compared to traffic noise and white noise, drivers' cognitive efficiency was lower under entertainment noise. (3) Performance in complex cognitive tasks was more susceptible to noise levels compared to simple cognitive tasks; (4) Experimentally, it was found that drivers exhibited the highest cognitive efficiency in cognitive tasks when the environmental noise level was 55 dB(A), as opposed to noise levels of 35 dB(A), 45 dB(A), and 65 dB(A)., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Tao Jin reports financial support was provided by National Natural Science Foundation of China. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The road transport system is a complex sociotechnical system that relies on a number of formal and informal rules of the road to ensure safety and resilience. Interactions between vulnerable road users and drivers often includes informal communication channels that are tightly linked to social norms, user expectations and the environmental context. Automated vehicles have a challenge in being able to communicate and respond to these informal rules of the road, therefore additional technologies are required to better support vulnerable road users. This paper presents the informal rules that cyclists and drivers employ within a cyclist overtake manoeuvre, through qualitative data collected from focus groups and interviews with road users. These informal rules are classified into the key elements of resilience (monitor, detect, anticipate, respond and learn) to understand how they guide the resilient interactions between road users. Using a human factors approach, the Perceptual Cycle Model shows how information is communicated between different road users and created by the situational context. This is then used to inform how automation will alter the communication between cyclists and drivers, and what additional feedback mechanisms will be needed to support the systems resilience. Technologies that can support these feedback mechanisms are proposed as avenues for future development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objective: This paper proposes an objective method to measure and identify trust-change directions during takeover transitions (TTs) in conditionally automated vehicles (AVs)., Background: Takeover requests (TORs) will be recurring events in conditionally automated driving that could undermine trust, and then lead to inappropriate reliance on conditionally AVs, such as misuse and disuse., Method: 34 drivers engaged in the non-driving-related task were involved in a sequence of takeover events in a driving simulator. The relationships and effects between drivers' physiological responses, takeover-related factors, and trust-change directions during TTs were explored by the combination of an unsupervised learning algorithm and statistical analyses. Furthermore, different typical machine learning methods were applied to establish recognition models of trust-change directions during TTs based on takeover-related factors and physiological parameters., Result: Combining the change values in the subjective trust rating and monitoring behavior before and after takeover can reliably measure trust-change directions during TTs. The statistical analysis results showed that physiological parameters (i.e., skin conductance and heart rate) during TTs are negatively linked with the trust-change directions. And drivers were more likely to increase trust during TTs when they were in longer TOR lead time, with more takeover frequencies, and dealing with the stationary vehicle scenario. More importantly, the F1-score of the random forest (RF) model is nearly 77.3%., Conclusion: The features investigated and the RF model developed can identify trust-change directions during TTs accurately., Application: Those findings can provide additional support for developing trust monitoring systems to mitigate both drivers' overtrust and undertrust in conditionally AVs.

Trams are experiencing a resurgence with worldwide network expansion driven by the need for sustainable and efficient cities. Trams often operate in shared or mixed-traffic environments, which raise safety concerns, particularly in hazardous situations. This paper adopts an international, mixed-methods approach, conducted through two interconnected studies in Melbourne (Australia) and Birmingham (UK). The first study involved qualitative interviews, while the second was an experimental study involving a virtual reality (VR) simulator and haptic master controller (i.e., speed lever). In tram operations, master controllers play a critical role in ensuring a smooth ride, which directly influences passenger safety and comfort. The objective was to understand how a master control system, enhanced with additional haptic feedback, could improve tram driver braking performance and perceptions in safety-critical scenarios. Interview results indicate that the use of the emergency brake is considered the final or ultimate choice by drivers, and their driving experience is a moderating factor in limiting its application. Combined with the experimental results, this paper highlights how implementing haptic feedback within a master controller can reduce the performance disparity between novice and experienced tram drivers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Commercial bus drivers account for most road traffic crashes and related mortality. The psychosocial working conditions of these drivers have been found precarious. However, road safety initiatives in Ghana still focus on correcting risky driving behaviours, ignoring the conditions under which these drivers operate. Hence, the purpose of this study was to examine whether psychosocial work factors can predict the psychological well-being and risky driving behaviours of long-distance bus drivers in Ghana., Methods: This quantitative cross-sectional survey recruited 7315 long-distance bus drivers that operate from Accra to other parts of Ghana and cities in other West African countries. Hypotheses were tested using Partial Least Squares Structural Equation Modelling (PLS-SEM)., Results: We found that job demands and job resources are direct and significant predictors of psychological well-being and safety incidents among these drivers. Moreover, psychological well-being of the drivers had a significant inverse relationship with their safety incidents. Psychosocial safety climate (PSC) had a negative association with safety incidents, and a positive but non-significant association with psychological well-being. PSC had a negative and significant association with job resources contrary to the notion of the PSC theory., Conclusion: Psychosocial work factors are predictors of psychological well-being and safety incidents of long-distance bus drivers. Owners and managers of bus transport businesses in Ghana, driver unions and station masters need to highly prioritise psychological health and safety of this bus drivers by providing suitable job resources and adopting bottom-up communication that might help the drivers effectively cope with their job demands., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Drivers must actively supervise automation as it can only function in limited conditions. A failure to supervise the system has negative consequences in terms of missed requests to take over control and may cause crashes or jeopardize safety. The objective of this study is to determine the effect of a novel, 3M (Mistakes, Mentoring, and Mastery) training program on drivers' behavior while using level 2 driving automation systems. To achieve this, 36 participants were assigned randomly to three different training programs (3M training, User manual, and Placebo) and drove through scenarios on a fixed-based driving simulator. The results showed that drivers in the 3M training group took back control more effectively when the driving automation system reached its limits compared to drivers who received User manual or Placebo training. Drivers in the 3M training Group also had higher situation awareness and improved trust in automation. The results indicate that an interactive approach to training with regards to vehicle automation can help drivers more safely interact with automation systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Trust in an automated vehicle system (AVs) can impact the experience and safety of drivers and passengers. This work investigates the effects of speech to measure drivers' trust in the AVs. Seventy-five participants were randomly assigned to high-trust (the AVs with 100% correctness, 0 crash, and 4 system messages with visual-auditory TORs) and low-trust group (the AVs with a correctness of 60%, a crash rate of 40%, 2 system messages with visual-only TORs). Voice interaction tasks were used to collect speech information during the driving process. The results revealed that our settings successfully induced trust and distrust states. The corresponding extracted speech feature data of the two trust groups were used for back-propagation neural network training and evaluated for its ability to accurately predict the trust classification. The highest classification accuracy of trust was 90.80%. This study proposes a method for accurately measuring trust in automated vehicles using voice recognition., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)