Automated driving in urban traffic requires extensive information from the surroundings. The most promisring approach to facilitate automated driving in mixed traffic is platooning of connected and automated vehicles (CAV). In this research, we investigate a human-leading strategy (HL) by which CAVs drive in platoons with the CAV leading the platoon driven by a human. We thoroughly formulate the problem of managing CAV platoons by the HL strategy, systematically model the platoon dynamics and the traffic system, as well as propose two approaches to implement this strategy. By conducting experiments in a simulation framework that combines the traffic and the communication network, the implementation of the HL strategy is evaluated with the consideration of travel time, automated driving experience, and communication reliability. The simulation results revealed that the HL strategy makes it feasible for CAVs to drive in automated mode in an urban mixed traffic network, while its performance relies on the CAV penetration rate and communication reliability. In addition, the results suggest that the performance of the HL strategy can be significantly improved by approaches that allow uninterrupted platooning and result in stable platoon dynamics.

中文翻译：

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混合交通中的协同驾驶：基础设施辅助方法


城市交通中的自动驾驶需要来自周围环境的大量信息。在混合交通中促进自动驾驶最有前景的方法是联网自动驾驶车辆（CAV）的编队行驶。在这项研究中，我们研究了一种人类主导策略 (HL)，通过该策略，CAV 成排行驶，而 CAV 则由人类驾驶来领导该排。我们通过 HL 策略彻底阐述了 CAV 排的管理问题，系统地对排动态和交通系统进行了建模，并提出了两种实施该策略的方法。通过在交通和通信网络相结合的仿真框架中进行实验，综合考虑出行时间、自动驾驶体验和通信可靠性来评估HL策略的实施情况。仿真结果表明，HL策略使得CAV在城市混合交通网络中以自动模式行驶是可行的，而其性能取决于CAV的渗透率和通信可靠性。此外，结果表明，通过允许不间断排行并产生稳定排动态的方法可以显着提高 HL 策略的性能。