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

Jiao, Yujun; Wang, Xuesong; Hurwitz, David; Hu, Gengdan; Xu, Xiaoyan; Zhao, Xudong

doi:10.1016/j.aap.2023.107065

Cited by 4 publications

(3 citation statements)

References 35 publications

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“…In this paper, we innovatively defined three typical abnormal lane-changing behaviors: “Cutting off Behavior”, “Tailgating Behavior”, and “Cutting in Behavior”. While previous studies on abnormal driving behaviors have made significant progress, particularly in the use of driving simulators and naturalistic driving approaches, they have primarily focused on driver-operated behaviors, such as those induced by drivers’ psychology and style, for instance, “Talking on a cell phone while driving”, “Sounding the horn to express anger”, and the like [ 60 ]. Such behaviors are challenging for roadside cameras to capture, making them less applicable to road safety supervision.…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, from a traffic regulation standpoint, this study focuses on lane-changing behavior, identifying three typical abnormal lane-changing behaviors through the detection model rather than merely distinguishing between normal and abnormal driving behavior. Furthermore, this research extends beyond significant violations or lapses, such as disregarding the speed limit [ 60 ], and starts from driving performance, identifying abnormal lane-changing behavior at the level of driving trajectories. This provides a new avenue for traffic authorities to detect and regulate dangerous driving behavior through roadside devices.…”

Section: Resultsmentioning

confidence: 99%

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

Xu,

Liu,

Yao

et al. 2023

Sensors

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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

Xu,

Liu,

Yao

et al. 2023

Sensors

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show abstract

“…Questionnaire (DSQ) [67]. Among the various forms and contents of questionnaires, the Manchester Driver Behavior Questionnaire constructed by Reason et al [68] is one of the more commonly used standardized questionnaires, many scholars have conducted studies based on this questionnaire for the driver population in their own countries [69][70][71].…”

Section: Driver Questionnaires and Analytical Methodsmentioning

confidence: 99%

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

Xiang,

Zhang,

Pan

et al. 2024

PLoS ONE

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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.

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Calculating Driving Behaviour Score Based on Driving Background

Nadimi,

Sheikh Hosseini Lori,

Arun

et al. 2024

Int J Civ Eng

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

Cited by 4 publications

References 35 publications

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

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

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

Calculating Driving Behaviour Score Based on Driving Background

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