Revision of the driver behavior questionnaire for Chinese drivers' aberrant driving behaviors using naturalistic driving data

Cyclist safety in the digital age: A review of advanced warning technologies

Improving the safety of cyclists, who are considered vulnerable road users, is essential. Implementing a warning system that alerts cyclists to nearby hazards is an effective method to improve their safety. Nevertheless, the literature needs a comprehensive and detailed review of cyclist warning systems. This paper presents a systematic review of existing studies about warning technologies and systems developed to improve cyclist safety, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (n = 78). Based on the review, it was found that the most used cyclist warning technologies were cameras with artificial intelligence (AI), ultrasonic sensors, lidar, and radar. Furthermore, there were some insights from the studies that conducted cycling experiments with the use of a warning system: (1) most participating cyclists responded positively to the warning technologies/systems, (2) most cyclists preferred auditory signals over visual or haptic signals as warning modalities, (3) warning technologies/systems influenced some cyclists' behaviour during critical situations, such as early braking, faster reactions, increased focus, maintaining a safer distance, and temporarily reducing speed, and (4) only two studies tried to explore how warning systems affect crash risk. Future research should focus on developing warning systems that are compact, lightweight, and affordable. More studies are also needed to demonstrate the impact of the cyclist warning system on cyclists' crash risk. This review identifies gaps in the current literature and offers a solid foundation for future studies to enhance cyclist safety.

Driving simulator study of text messaging and phone conversations: Effects of messages' valence, drivers' values and self-reported driving behaviors

This study investigated the impact of reading emotionally-laden text messages and engaging in handheld phone conversations on driving behavior in a simulated environment. Additionally, the study explored how driving behavior correlates with individual differences in basic human values and self-reported risky driving patterns (e.g., violations, errors, and lapses). A within-subject design was employed, where all participants read both a negative and a positive text message and answered a phone call while driving in the simulator. Results showed that average driving speed remained unchanged after receiving text messages and during phone calls but increased after the call ended. However, the standard deviation of speed varied across different conditions. Regarding centerline deviation, participants maintained a consistent distance from the centerline during baseline driving and after reading text messages, but moved closer to the centerline during phone conversations. Self-reported values of power, security, and universalism were significantly correlated with objective driving measures: power was associated with riskier driving behaviors, whereas security and universalism were linked to safer driving patterns. Moreover, self-reported driving errors and lapses were positively correlated with increased driving speed during and after phone calls.

Validating self-reported driving behaviours as determinants of real-world driving speeds

Self-reported driver behaviour has long been a tool used by road safety researchers to classify drivers and to evaluate the impact of interventions yet the relationship with real-world driving is challenging to validate due to the need for extensive, detailed observations of normal driving. This study examines this association by applying the large UDRIVE naturalistic driving study data involving 96 car drivers, comprising 131,462 trips and 1,459,110 km travelled over a duration of 32,096 hours, to compare individual questions and composite indicators based on the Driver Behaviour Questionnaire with real world driving. Self-reported speed behaviour was compared to the measured values under urban and highway conditions. Generalised Linear Mixed Models were developed to examine the relationships between the observed speed behaviours with DBQ errors and violations scores in conjunction with traffic and environmental factors. Drivers' self-reported data on speed selection seldom aligned with their real-world behaviour and there were no meaningful differences between many of the response categories. The DBQ violations and errors scales showed a highly significant correlation with driving speed indicators however they had a low explanatory power compared to other traffic situational and driving factors. Overall, the study highlights the need to validate self-reported driving data against the accuracy and relevance to real-world driving.

Analysis of coping capacities and cognitive biases of novice drivers-A questionnaire-based study

Coping capacity is a key aspect of driver-vehicle interaction when drivers observe and make decisions, and is of great importance for drivers. However, different drivers have different self-cognition and assess their driving abilities differently, especially for novice drivers. Based on questionnaire data, this study has investigated the coping capacities of drivers in both static environments and dynamic environments. With the ANOVA analysis method and the structural equation model (SEM), this study has verified the effects of gender and driving factors (driving years, driving frequency, driving time) on drivers' coping capacities based on drivers' self-assessment scores and mutual assessment scores. Drivers' self-assessment scores show significant effects of all factors on drivers' coping capacities, and drivers' mutual assessment scores show significant effects of all factors, excluding driving time, on drivers' coping capacities. Also, it has been found that all drivers in the driving year group have cognitive biases. It seems that first-year drivers are always overconfident with their driving skills, while drivers with a driving experience of more than three years usually score driving skills of themselves and other drivers most conservatively. With increased exposure to various traffic conditions, experienced drivers are more aware of their limitations in dealing with complex traffic situations, while novice drivers do not know their lack of capability to properly respond to any unexpected situation they could encounter.

Integrating Surrounding Vehicle Information for Vehicle Trajectory Representation and Abnormal Lane-Change Behavior Detection

The detection of abnormal lane-changing behavior in road vehicles has applications in traffic management and law enforcement. The primary approach to achieving this detection involves utilizing sensor data to characterize vehicle trajectories, extract distinctive parameters, and establish a detection model. Abnormal lane-changing behaviors can lead to unsafe interactions with surrounding vehicles, thereby increasing traffic risks. Therefore, solely focusing on individual vehicle perspectives and neglecting the influence of surrounding vehicles in abnormal lane-changing behavior detection has limitations. To address this, this study proposes a framework for abnormal lane-changing behavior detection. Initially, the study introduces a novel approach for representing vehicle trajectories that integrates information from surrounding vehicles. This facilitates the extraction of feature parameters considering the interactions between vehicles and distinguishing between different phases of lane-changing. The Light Gradient Boosting Machine (LGBM) algorithm is then employed to construct an abnormal lane-changing behavior detection model. The results indicate that this framework exhibits high detection accuracy, with the integration of surrounding vehicle information making a significant contribution to the detection outcomes.