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.

中文翻译：

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基于强化学习的鸟瞰自动车辆控制避免交叉路口

本文提出了一种用于联网自动驾驶汽车 (CAV) 的创新鸟瞰控制框架。最近测试的自动驾驶汽车基于配备在车身上的传感系统，这要求自动驾驶策略稳健并适应各种环境不确定性。受车辆到基础设施技术的启发，自动驾驶技术也可以通过道路基础设施与车辆之间的通信来实现，其中传感器主要安装在道路上的较高位置，可以从鸟瞰中收集交通信息. 为此，我们开发了一种基于融合的 Q 学习方法，为多车道道路上的 CAV 生成最佳鸟瞰控制策略。通过我们的控制策略，CAV可以在复杂的交通环境下智能驾驶，与领先车辆互动并同时穿越交通。一系列案例研究表明，我们的 CAV 控制策略是字符串稳定的，可以避免各种场景下的碰撞。