School of Transportation, Southeast University, China.
School of Transportation, Southeast University, China.
Accid Anal Prev. 2023 Apr;183:106975. doi: 10.1016/j.aap.2023.106975. Epub 2023 Jan 23.
The concepts of Connected and Automated Vehicles (CAV) and vehicle platooning have generated high expectations regarding the safety performance of future transportation systems. Existing CAV longitudinal control research primarily focuses on efficiency and control stability, by considering different inter-vehicle spacing policies. In very few cases, safety was also considered as a constraint, but not in the main control objectives. Theoretically, stability can only guarantee that CAV platoons eventually achieve an equilibrium state but is unable to promise safety along the process of achieving equilibrium. It is important to note that CAV does not mean absolutely safe, and its longitudinal or platoon control safety performance depends on how the control algorithms are designed, how accurately it can detect and predict its lead vehicle's (could be a human-driven vehicle) next move, and other practical factors such as control and communication delays. To optimize CAV platoon safety, this study integrates surrogate safety measures (SSM) and model predictive control (MPC) into CAV longitudinal control for trajectory optimization. SSM has been widely adopted for modeling the safety consequences of various vehicle control strategies and identifying near-crash events from either simulated or field-captured traffic data. This study directly incorporates three typical SSM into the longitudinal control objectives of CAV and constructs a state-space MPC algorithm to model how these SSM vary as a result of CAV dynamics. Numerical examples are provided to show the performance of these SSM-based optimal CAV longitudinal control methods under traffic flow perturbations. To further confirm the necessity of explicitly considering SSM in CAV longitudinal control and its effectiveness in reducing rear-end collision risk, the proposed methods are compared with three classical longitudinal control models that do not consider SSM based on microscopic traffic simulation. It is noted that all SSM-based optimal control methods perform better than others as manifested by some key risk indicators, demonstrating the importance of explicitly considering SSM and safety in CAV longitudinal control.
车联网和车辆编队的概念对未来交通系统的安全性能寄予了很高的期望。现有的车联网纵向控制研究主要侧重于效率和控制稳定性，考虑了不同的车间距策略。在极少数情况下，安全也被视为一个约束条件，但不是主要的控制目标。从理论上讲，稳定性只能保证车联网车队最终达到平衡状态，但不能保证在达到平衡的过程中的安全性。需要注意的是，车联网并不意味着绝对安全，其纵向或编队控制安全性能取决于控制算法的设计方式，它能够多准确地检测和预测前车（可能是人为驾驶的车辆）的下一步动作，以及控制和通信延迟等其他实际因素。为了优化车联网车队的安全性，本研究将替代安全措施（SSM）和模型预测控制（MPC）集成到车联网纵向控制中，以进行轨迹优化。SSM 已广泛用于模拟各种车辆控制策略的安全后果，并从模拟或现场捕获的交通数据中识别近碰撞事件。本研究直接将三种典型的 SSM 纳入车联网的纵向控制目标，并构建状态空间 MPC 算法，以模拟这些 SSM 如何因车联网动力学而变化。数值示例用于展示这些基于 SSM 的最优车联网纵向控制方法在交通流扰动下的性能。为了进一步确认在车联网纵向控制中明确考虑 SSM 的必要性及其在降低追尾碰撞风险方面的有效性，将所提出的方法与不考虑 SSM 的三种基于微观交通模拟的经典纵向控制模型进行了比较。需要注意的是，所有基于 SSM 的最优控制方法都表现出优于其他方法的性能，这体现在一些关键风险指标上，这表明在车联网纵向控制中明确考虑 SSM 和安全性的重要性。
