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首页> 外文期刊>Computer-Aided Civil and Infrastructure Engineering >Reinforcement learning-based bird-view automated vehicle control to avoid crossing traffic
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Reinforcement learning-based bird-view automated vehicle control to avoid crossing traffic

机译：基于强化学习的鸟类自动化车辆控制，以避免交通交通

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摘要

This paper presents an innovative bird-view control framework for connected automated vehicles (CAV). Most recently tested automated vehicles are based on sensing systems equipped on the car body, which require the self-driving policy to be robust and adaptive to various environmental uncertainties. Inspired by the vehicle to infrastructure technologies, the self-driving technology can also be achieved through the communication between road infrastructure and the vehicle, where sensors are mainly installed on the road in a high position, which can collect traffic information from a bird-view. To this end, we developed a fusion-based Q-learning method to yield an optimal bird-view control policy for a CAV on a multi-lane road. With our control policy, the CAV can drive smartly under complicated traffic environment, interacting with leading vehicles and crossing traffic simultaneously. A series of case studies show our CAV control policy is string stable and can avoid collisions under various scenarios.
机译：本文提出了一种用于连接自动车辆（CAV）的创新鸟瞰控制框架。最近测试的自动化车辆是基于装备在车身上的传感系统，这需要自动驾驶政策稳健和适应各种环境不确定性。受到车辆到基础设施技术的启发，通过道路基础设施和车辆之间的通信，传感器主要安装在高位的路上，可以实现自动驾驶技术，其中可以从鸟瞰图中收集交通信息。为此，我们开发了一种基于融合的Q学习方法，在多车道道路上产生一个最佳的鸟瞰控制政策。通过我们的控制政策，CAV可以在复杂的交通环境下巧妙地驱动，与主要车辆同时交流并同时交流。一系列案例研究表明我们的CAV控制政策是串稳定的，可以避免在各种场景下碰撞。

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    Wang Yipei; Hou Shuaikun; Wang Xin;

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    Univ Wisconsin Dept Math Madison WI 53706 USA;

    Univ Wisconsin Dept Ind & Syst Engn Mech Engn Bldg 1513 Univ Ave Madison WI 53706 USA;

    Univ Wisconsin Dept Ind & Syst Engn Mech Engn Bldg 1513 Univ Ave Madison WI 53706 USA;

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