Volume 42 Issue 3
Jun.  2024
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WANG Yiyun, YU Rongjie. An Analysis of Safety Influencing Factors for Longitudinal Interaction Between Vehicles in Human-machine Mixed Traffic Driving Conditions[J]. Journal of Transport Information and Safety, 2024, 42(3): 11-19. doi: 10.3963/j.jssn.1674-4861.2024.03.002
Citation: WANG Yiyun, YU Rongjie. An Analysis of Safety Influencing Factors for Longitudinal Interaction Between Vehicles in Human-machine Mixed Traffic Driving Conditions[J]. Journal of Transport Information and Safety, 2024, 42(3): 11-19. doi: 10.3963/j.jssn.1674-4861.2024.03.002
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An Analysis of Safety Influencing Factors for Longitudinal Interaction Between Vehicles in Human-machine Mixed Traffic Driving Conditions

doi: 10.3963/j.jssn.1674-4861.2024.03.002

  • Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 208104, China

  • Received Date: 2023-08-16
    Available Online: 2024-10-21
    Abstract
  • Autonomous vehicles are gradually introduced to the existing traffic environment, leading to a mixed flow of both autonomous vehicles and human-driven vehicles. Studies show that the crash rate per-kilometer for autonomous vehicles is 9.1, which is more than twice that of human-driven vehicles (4.1). The ratio of the rear-end crash pattern between autonomous vehicles and human-driven vehicles is 57.5%, which exceeds 27.9% of among human-driven vehicles. Therefore, there is an urgent need to investigate the safety mechanisms of longitudinal interactions of autonomous vehicles and human-driven vehicles. Existing studies typically employ driving simulation experiments to analyze the longitudinal interaction and safety between human-driven and autonomous vehicles in virtual environments. However, the differences between simulated environments and real-world road scenarios make it challenging to accurately capture the interaction behavior between vehicles in mixed human-autonomous traffic flows. In this study, public road-testing dataset of autonomous vehicles are utilized to extract longitudinal interacting scenarios, and the influencing factors and the impact mechanisms of longitudinal interaction behavior and safety are investigated. Specifically, scenarios of human-driven vehicles following the other human-driven vehicle, and following an autonomous vehicle are studied, Structural equation model is applied to construct a chained relationship among driving behavior of leading vehicle, type of leading vehicle (whether it is an autonomous vehicle or not), speed level of vehicles on the roadway, and the safety surrogate measure. The modelling results revealed the type of leading vehicle is identified as an influencing factor in longitudinal interaction safety. When other variables remain constant, the safety of interactions between human drivers and autonomous vehicles as leading vehicles decreased compared to interactions with other human-driven vehicles as leading vehicles.

     

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