Cooperative coordination at unsignalized road intersections, which aims to improve the driving safety and traffic throughput for connected and automated vehicles, has attracted increasing interests in recent years. However, most existing investigations either suffer from computational complexity or cannot harness the full potential of the road infrastructure. To this end, we first present a dedicated intersection coordination framework, where the involved vehicles hand over their control authorities and follow instructions from a centralized coordinator. Then a unified cooperative trajectory optimization problem will be formulated to maximize the traffic throughput while ensuring the driving safety and long-term stability of the coordination system. To address the key computational challenges in the real-world deployment, we reformulate this non-convex sequential decision problem into a model-free Markov Decision Process (MDP) and tackle it by devising a Twin Delayed Deep Deterministic Policy Gradient (TD3)-based strategy in the deep reinforcement learning (DRL) framework. Simulation and practical experiments show that the proposed strategy could achieve near-optimal performance in sub-static coordination scenarios and significantly improve the traffic throughput in the realistic continuous traffic flow. The most remarkable advantage is that our strategy could reduce the time complexity of computation to milliseconds, and is shown scalable when the road lanes increase.

翻译：无信号灯路口的协同协调旨在提升联网自动驾驶车辆的驾驶安全与通行效率，近年来受到越来越多关注。然而，现有研究大多面临计算复杂度过高的问题，或未能充分利用道路基础设施的潜力。为此，本文首先提出一种专用路口协调框架，其中涉及车辆移交控制权限并遵循集中式协调器的指令。随后，我们构建统一的协同轨迹优化问题，以在确保驾驶安全与协调系统长期稳定性的前提下最大化通行效率。为解决实际部署中的关键计算挑战，我们将这一非凸序贯决策问题重构为无模型马尔可夫决策过程（MDP），并基于深度强化学习（DRL）框架设计了一种采用双延迟深度确定性策略梯度（TD3）的求解策略。仿真与实验结果表明，该策略在准静态协调场景下可达到接近最优的性能，并在真实连续交通流中显著提升通行效率。其最显著的优势在于能将计算时间复杂度降至毫秒级，且随道路车道数增加仍具备良好的可扩展性。