Influence of the feedback links of connected and automated vehicle on rear-end collision risks with vehicle-to-vehicle communication

Objective: Connected and automated vehicles (CAV) can monitor multiple vehicles ahead via vehicle-to-vehicle (V2V) communication. Although feedback information from more vehicles ahead may be more helpful for anticipations, it also makes control more complex and increases the probability of data packet loss. Then it needs an appropriate number of CAV feedback links, and the maximum number may be not suitable. Therefore, this article focuses on the influence of CAV feedback links on rear-end collision risks. Methods: To deal with this, stability analysis of a CAV car-following model was conducted to obtain the designs of CAV feedback gains for maintaining stable CAV flow. Simulation experiments were performed to describe a traffic accident on freeway, using car-following models of manually driven vehicles (MDVs) and CAV under different CAV penetration rates. Four scenarios are considered in simulation experiments; that is, the CAV monitors 1, 2, 3, and 4 preceding vehicles, respectively. Based on the simulation experiments, surrogate safety indicators, time-exposed time-to-collision (TET), and time-integrated time-to-collision (TIT) are used to evaluate risks of rear-end collisions. Results: Results indicated that CAV helped to decrease the collision risks, especially the more serious collision risks with smaller threshold values of time-to-collision (TTC). In addition, the reductions in collision risks are more obvious when CAV feedback changes from one link to 2 links. In addition, reducing amplitudes are not significant if the CAV feedback is extended from 2 links to 3 or 4 links. Conclusions: Two links of CAV feedback are appropriate when control complexity is a priority, whereas 3 links is the better choice when reductions in collisions are a priority. The findings of this study provide helpful reference for CAV control and design before larger-scale implementation in real vehicles.研究目标：网联自动驾驶车辆（Connected and automated vehicles, CAV）可通过车对车（vehicle-to-vehicle, V2V）通信监测前方多台车辆。尽管前方更多车辆的反馈信息对行车预判更有助益，但同时也会使控制流程更为复杂，提升数据包丢失概率，因此需要选取合适数量的CAV反馈链路，一味采用最大链路数量并不适配实际场景。故此，本文聚焦于CAV反馈链路对追尾碰撞风险的影响。 研究方法：针对该问题，本文首先开展CAV跟驰模型的稳定性分析，以推导得到维持CAV车流稳定的反馈增益设计方案。随后设置仿真实验，基于不同CAV渗透率场景下的人工驾驶车辆（manually driven vehicles, MDVs）与CAV跟驰模型，模拟高速公路交通事故场景。仿真实验共设置4种工况：CAV分别监测1、2、3、4台前方车辆。基于仿真结果，采用两项替代安全指标——暴露碰撞时间（time-exposed time-to-collision, TET）与积分碰撞时间（time-integrated time-to-collision, TIT），对追尾碰撞风险进行评估。 研究结果：结果表明，CAV可有效降低碰撞风险，尤其在碰撞时间（time-to-collision, TTC）阈值更小的场景中，更严重的碰撞风险的降幅更为显著。此外，当CAV反馈链路从1条增至2条时，碰撞风险的降低效果最为明显；而当反馈链路从2条拓展至3条或4条时，风险降幅并不显著。 研究结论：若优先考量控制复杂度，2条CAV反馈链路为最优选择；若优先追求碰撞风险的降低效果，则3条链路更为合适。本研究结论可为CAV的控制策略与系统设计提供有益参考，为其后续在实车中开展大规模落地应用奠定基础。