[论文解读] Leveraging Vehicle Connectivity and Autonomy to Stabilize Flow in Mixed Traffic Conditions: Accounting for Human-driven Vehicle Driver Behavioral Heterogeneity and Perception-reaction Time Delay

Yujie Li, Sikai Chen+4 more|arXiv (Cornell University)|Aug 10, 2020

Traffic control and management参考文献 41被引用 23

一句话总结

本文提出了一种连通自动驾驶汽车（CAV）控制器，通过考虑人类驾驶车辆（HDV）驾驶员行为异质性及感知-反应时间延迟，以稳定混合交通流。该控制器可减少虚拟堵塞的形成并提升安全性，但当对HDV不确定性进行建模时，其稳定性能力显著下降，凸显了在CAV设计中纳入此类动态特性的重要性。

ABSTRACT

The erratic nature of human driving tends to trigger undesired waves that amplify as successive driver reactions propagate from the errant vehicle to vehicles upstream. Known as phantom jams, this phenomenon has been identified in the literature as one of the main causes of traffic congestion. This paper is based on the premise that vehicle automation and connectivity can help mitigate such jams. In the paper, we design a controller for use in a connected and autonomous vehicle (CAV) to stabilize the flow of human-driven vehicles (HDVs) that are upstream of the CAV, and consequently to lower collision risk in the upstream traffic environment. In modeling the HDV dynamics in the mixed traffic stream, we duly consider HDV driver heterogeneity and the time delays associated with their perception reaction time. We can find that the maximum number of HDVs that a CAV can stabilize is lower when human drivers potential time delay and heterogeneity are considered, compared to the scenario where such are not considered. This result suggests that heterogeneity and time delay in HDV behavior impairs the CAVs capability to stabilize traffic. Therefore, in designing CAV controllers for traffic stabilization, it is essential to consider such uncertainty-related conditions. In our demonstration, we also show that the designed controller can significantly improve both the stability of the mixed traffic stream and the safety of both CAVs and HDVs in the stream. The results are useful for real-time calibration of the model parameters that characterize HDV movements in the mixed stream.

研究动机与目标

• 为解决由于驾驶员行为异质性及感知-反应时间延迟导致的人类驾驶车辆（HDVs）对交通流的不稳定影响。
• 设计一种连通自动驾驶车辆（CAV）控制器，以在混合交通条件下稳定上游HDV交通流。
• 量化HDV不确定性对CAV可稳定HDV最大数量的影响。
• 展示控制器在提升混合交通流稳定性与安全性方面的有效性。

提出的方法

• 使用包含驾驶员特定感知-反应时间延迟和行为异质性的跟驰框架对HDV动力学进行建模。
• 基于反馈控制原理设计CAV控制器，以响应上游HDV扰动，调节车距与速度。
• 采用线性化稳定性分析，评估HDV不确定性对系统稳定性裕度的影响。
• 通过模拟不同异质性和时间延迟水平下的混合交通场景，评估控制器性能。
• 利用实时数据校准模型参数，以提高对HDV行为的表征准确性。
• 通过不同不确定性条件下的对比仿真验证控制器的有效性。

实验结果

研究问题

• RQ1 驾驶员行为异质性如何影响CAV稳定上游交通流的能力？
• RQ2 HDV中的感知-反应时间延迟对CAV可稳定车辆最大数量有何影响？
• RQ3 将HDV不确定性纳入模型后，如何改变基于CAV的交通稳定控制器的性能？
• RQ4 所提出的控制器在多大程度上可提升混合交通流的稳定性和安全性？
• RQ5 哪些关键模型参数需要实时校准，以真实反映HDV动力学？

主要发现

• 与不考虑此类不确定性的理想化场景相比，当考虑感知-反应时间延迟和行为异质性时，CAV可稳定HDV的最大数量显著降低。
• 将HDV不确定性纳入模型会降低CAV的稳定能力，表明现实世界中驾驶员的变异性限制了控制策略的有效性。
• 所提出的控制器在混合交通环境中成功提升了交通流稳定性并降低了碰撞风险。
• 即使在高程度的HDV异质性和时间延迟下，控制器仍能维持稳定性，尽管其容量有所降低。
• 实时校准HDV模型参数对于在动态交通条件下实现准确表征与有效控制至关重要。