Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai, PR China.
Traffic Inj Prev. 2023;24(7):652-661. doi: 10.1080/15389588.2023.2235454. Epub 2023 Jul 24.
Road testing can accelerate the development and validation of autonomous vehicles (AVs). AV road testing can come with high safety risks, particularly in a complex road traffic environment, due to the immaturity of AV technology. A priori safety risk assessments of the road traffic environment before AV road testing are of great importance, allow the quantifying of risk levels in different road scenarios, and provide guidelines for AV road testing in low to high-risk environments.
This study proposes a framework, namely Safety Risk Assessment for AV road testing (SRAAV), based on the probability and severity of five categories of potential AV accidents. Four groups of influencing factors are considered comprehensively in assessing AV safety risk, and their impacts are quantified using impact coefficients derived from a Bayesian network and empirical AV road testing data. The safety risk is assessed on a road section level, based on which an overall risk level is defined for a corridor and a region. Afterwards, the quantified safety risk is classified into four levels according to expert experience and knowledge, through a questionnaire survey.
Applications of the proposed SRAAV framework are conducted for urban roads in Shanghai, and expressways in Shanghai and Gothenburg. The assessment results are validated using disengagement data from AV road testing. The results show that the SRAAV framework and its models could estimate the safety risk levels of road traffic environments for AV road testing in a sound way and have the flexibility for further extensions to be made.
The framework and assessment results can provide technical support for determining where and when to grant permission for public roads to be used for AV road testing, and how to choose public roads from a low to a high risk level, guaranteeing the safety of AV public road testing.
道路测试可以加速自动驾驶汽车（AV）的开发和验证。由于 AV 技术的不成熟，AV 道路测试可能会带来很高的安全风险，尤其是在复杂的道路交通环境中。在进行 AV 道路测试之前，对道路交通环境进行先验安全风险评估非常重要，这可以量化不同道路场景中的风险水平，并为 AV 在低风险到高风险环境中的道路测试提供指导。
本研究提出了一个框架，即自动驾驶汽车道路测试安全风险评估（SRAAV），该框架基于五类潜在 AV 事故的发生概率和严重程度。在评估 AV 安全风险时，综合考虑了四组影响因素，并使用贝叶斯网络和经验性 AV 道路测试数据得出的影响系数对其进行量化。基于路段水平评估安全风险，并根据走廊和区域的整体风险水平进行定义。之后，根据专家经验和知识，通过问卷调查将量化后的安全风险分为四个等级。
将所提出的 SRAAV 框架应用于上海的城市道路以及上海和哥德堡的高速公路。使用来自 AV 道路测试的脱离数据验证评估结果。结果表明，SRAAV 框架及其模型可以合理地估计 AV 道路测试道路交通环境的安全风险水平，并且具有进一步扩展的灵活性。
该框架和评估结果可为确定何时何地允许公共道路用于 AV 道路测试以及如何从低风险道路选择到高风险道路提供技术支持，从而保证 AV 公共道路测试的安全。
