School of Transportation, Southeast University, China.
Department of Civil and Environmental Engineering, University of Massachusetts Lowell, 1 University Ave, Lowell, MA 01854, United States.
Accid Anal Prev. 2021 Jul;157:106157. doi: 10.1016/j.aap.2021.106157. Epub 2021 May 8.
Surrogate Safety Measures (SSM) are important for safety performance evaluation, since crashes are rare events and historical crash data does not capture near crashes that are also critical for improving safety. This paper focuses on SSM and their applications, particularly in Connected and Automated Vehicles (CAV) safety modeling. It aims to provide a comprehensive and systematic review of significant SSM studies, identify limitations and opportunities for future SSM and CAV research, and assist researchers and practitioners with choosing the most appropriate SSM for safety studies. The behaviors of CAV can be very different from those of Human-Driven Vehicles (HDV). Even among CAV with different automation/connectivity levels, their behaviors are likely to differ. Also, the behaviors of HDV can change in response to the existence of CAV in mixed autonomy traffic. Simulation by far is the most viable solution to model CAV safety. However, it is questionable whether conventional SSM can be applied to modeling CAV safety based on simulation results due to the lack of sophisticated simulation tools that can accurately model CAV behaviors and SSM that can take CAV's powerful sensing and path prediction and planning capabilities into crash risk modeling, although some researchers suggested that proper simulation model calibration can be helpful to address these issues. A number of critical questions related to SSM for CAV safety research are also identified and discussed, including SSM for CAV trajectory optimization, SSM for individual vehicles and vehicle platoon, and CAV as a new data source for developing SSM.
替代安全措施（SSM）对于安全性能评估很重要，因为事故是罕见事件，历史事故数据无法捕捉到对提高安全性也很关键的近事故。本文重点介绍了 SSM 及其应用，特别是在联网和自动驾驶车辆（CAV）安全建模中。本文旨在对重要的 SSM 研究进行全面系统的回顾，确定未来 SSM 和 CAV 研究的局限性和机遇，并帮助研究人员和从业者选择最适合安全研究的 SSM。CAV 的行为可能与人类驾驶车辆（HDV）有很大不同。即使是具有不同自动化/连接水平的 CAV，它们的行为也可能不同。此外，HDV 的行为可能会随着混合自治交通中 CAV 的存在而发生变化。到目前为止，模拟是建模 CAV 安全的最可行的解决方案。然而，由于缺乏能够准确模拟 CAV 行为的复杂模拟工具，以及能够将 CAV 强大的感知和路径预测和规划能力纳入事故风险建模的 SSM，因此基于模拟结果，传统的 SSM 是否可以应用于建模 CAV 安全是值得怀疑的，尽管一些研究人员建议适当的模拟模型校准可能有助于解决这些问题。还确定并讨论了与 CAV 安全研究相关的一些关键 SSM 问题，包括 CAV 轨迹优化的 SSM、个体车辆和车辆编队的 SSM 以及 CAV 作为开发 SSM 的新数据源。
