Crash comparison of autonomous and conventional vehicles using pre-crash scenario typology

Liu, Qian; Wang, Xuesong; Wu, Xiangbin; Glaser, Yi; He, Linjia

doi:10.1016/j.aap.2021.106281

Cited by 43 publications

(20 citation statements)

References 27 publications

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“…Therefore, popularizing AV knowledge for conventional drivers and improving their vigilance in mixed traffic may help reduce crash severity. While studying car-following in mixed traffic, the interaction between human drivers and AVs should be paid more attention [ 81 ].…”

Section: Discussionmentioning

confidence: 99%

Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach

Ren

Chen

et al. 2022

IJERPH

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Influencing factors on crash severity involved with autonomous vehicles (AVs) have been paid increasing attention. However, there is a lack of comparative analyses of those factors between AVs and human-driven vehicles. To fill this research gap, the study aims to explore the divergent effects of factors on crash severity under autonomous and conventional (i.e., human-driven) driving modes. This study obtained 180 publicly available autonomous vehicle crash data, and 39 explanatory variables were extracted from three categories, including environment, roads, and vehicles. Then, a hierarchical Bayesian approach was applied to analyze the impacting factors on crash severity (i.e., injury or no injury) under both driving modes with considering unobserved heterogeneities. The results showed that some influencing factors affected both driving modes, but their degrees were different. For example, daily visitors’ flowrate had a greater impact on the crash severity under the conventional driving mode. More influencing factors only had significant impacts on one of the driving modes. For example, in the autonomous driving mode, mixed land use increased the severity of crashes, while daytime had the opposite effects. This study could contribute to specifying more appropriate policies to reduce the crash severity of both autonomous and human-driven vehicles especially in mixed traffic conditions.

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Section: Discussionmentioning

confidence: 99%

Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach

Ren

Chen

et al. 2022

IJERPH

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“…There were three police reportable accidents (denoted as level C1) in California while driving 2,208,199 km, giving an accident rate of 1,36 police reportable incident pr. million km (Liu et al 2021;Xu et al 2019). This is 1/3 of reportable accidents of human-driven passenger vehicles in the same area, and could be argued as an improvement in safety.…”

Section: Ground Transportcar and Transport Systemsmentioning

confidence: 92%

A Conceptual Control System Description of Cooperative and Automated Driving in Mixed Urban Traffic With Meaningful Human Control for Design and Evaluation

Calvert

Mecacci

2020

IEEE Open J. Intell. Transp. Syst.

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The introduction of automated vehicles means that some or all operational control over these vehicles is diverted away from a human driver to a technological system. The concept of Meaningful Human Control (MHC) was derived to address control issues over automated systems, allowing a system to explicitly consider human intentions and reasons. Applying MHC to technological systems, such as automated driving is a real challenge, and the main focus of this paper. An approach with mathematical elaboration has been developed that offers a first quantifiable operationalisation of MHC for the traffic domain and for use with automated vehicles. A major contribution lies in the taxonomification of control for MHC in the broader traffic environment, including consideration of the driver, the vehicle, the traffic environment, considering behaviour, moral standards and societal values, which are considered in a case study. The demonstration case shows the validity of the developed approach for an automated vehicle overtaking a cyclist on an urban street. This paper is one of the first to operationalise MHC to such a level of detail and opens the door to further development of the concept for technological implementation.

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“…It was found that CAV driving mode was significant influencing factor for collision type and severity level of CAV involved crashes. Liu et al (2021) compared the AV crashes and conventional vehicle crashes and found the two groups differed in the proportion of crashes. An in-depth investigation revealed that perception-reaction time, inaccurate identification and insufficient path planning were significant causes of AV crashes.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered probit model analysis

Yuan

Wang

et al. 2022

JICV

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Purpose This study aims to investigate the safety and liability of autonomous vehicles (AVs), and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs. Design/methodology/approach The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations. The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs, respectively, as well as accommodating the heterogeneity issue simultaneously. Findings The findings show that day, location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability. Originality/value The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability.

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Crash comparison of autonomous and conventional vehicles using pre-crash scenario typology

Cited by 43 publications

References 27 publications

Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach

Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach

A Conceptual Control System Description of Cooperative and Automated Driving in Mixed Urban Traffic With Meaningful Human Control for Design and Evaluation

Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered probit model analysis

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