Wang Jinghua, Lu Guangquan, Long Wenmin, Zhang Zhao, Liu Miaomiao, Xia Yong
School of Transportation Science and Engineering, Beihang University, Beijing 100191, China.
School of Transportation Science and Engineering, Beihang University, Beijing 100191, China; Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beihang University, Beijing 100191, China; Key Laboratory of Intelligent Transportation Technology and System, Ministry of Education, Beihang University, Beijing 100191, China.
Accid Anal Prev. 2025 Mar;211:107879. doi: 10.1016/j.aap.2024.107879. Epub 2024 Dec 12.
Driving behavior is crucial in shaping traffic dynamics and serves as the foundation for safe and efficient autonomous driving. Despite the widespread interest in driving behavior modeling, existing models often focus on specific behaviors and cannot describe all types of vehicle movements, while vehicle status and driving scenarios are dynamic and infinite. That means comprehending and modeling generalized driving behavior mechanisms is essential. Risk Homeostasis Theory (RHT) emerges as a compelling conceptual framework to explain human risk behaviors comprehensively. The critical problem in modeling behavior using RHT is quantifying the subject risk precepted by humans. RHT has been applied in car-following behavior modeling based on the one-dimensional risk indicator Safety Margin (SM), simplifying the specific behavior along its direction. While the generalized perceived risk indicator on the two-dimensional surface still lacks. Considering the collision avoidance capacity from the driver's perspective, this paper proposes the two-dimensional safety margin (TSM) to describe the driver's risk perception in generalized driving scenarios with two-dimensional movements. Results demonstrate that TSM could accurately describe car-following behavior compared to existing risk indicators, with a 9.1 % correlation improvement and the reasonably calibrated response time (1.07 s). And TSM could effectively capture the discrepant risk perceptions of different drivers involved in the same conflict, underscoring the alignment of TSM with drivers' subjective risk perceptions. Besides, TSM reflects the risk homeostasis of driving behaviors, as both typical scenarios have the normally distributed and concentrated target levels. Further, TSM also achieves a generalized, scenario-independent risk quantification with a mean target level of 0.85. As a good representation of driver's risk perception in two-dimensional scenarios, TSM serves as a crucial basis in areas such as driving behavior modeling, and decision-making and testing of autonomous driving.
驾驶行为对于塑造交通动态至关重要，是安全高效自动驾驶的基础。尽管对驾驶行为建模有着广泛的兴趣，但现有模型往往侧重于特定行为，无法描述所有类型的车辆运动，而车辆状态和驾驶场景是动态且无限的。这意味着理解和建模广义驾驶行为机制至关重要。风险稳态理论（RHT）作为一个引人注目的概念框架出现，用于全面解释人类风险行为。使用RHT对行为进行建模的关键问题是量化人类感知到的主体风险。RHT已基于一维风险指标安全裕度（SM）应用于跟车行为建模，简化了沿其方向的特定行为。而二维表面上的广义感知风险指标仍然缺乏。从驾驶员的角度考虑避撞能力，本文提出二维安全裕度（TSM）来描述在二维运动的广义驾驶场景中驾驶员的风险感知。结果表明，与现有风险指标相比，TSM能够准确描述跟车行为，相关性提高了9.1%，响应时间校准合理（1.07秒）。并且TSM能够有效捕捉参与同一冲突的不同驾驶员的差异风险感知，强调了TSM与驾驶员主观风险感知的一致性。此外，TSM反映了驾驶行为的风险稳态，因为两种典型场景都具有正态分布且集中的目标水平。进一步地，TSM还实现了广义的、与场景无关的风险量化，平均目标水平为0.85。作为二维场景中驾驶员风险感知的良好表示，TSM在驾驶行为建模、自动驾驶决策和测试等领域起着关键作用。
