Autonomous driving at intersections is one of the most complicated and accident-prone traffic scenarios, especially with mixed traffic participants such as vehicles, bicycles and pedestrians. The driving policy should make safe decisions to handle the dynamic traffic conditions and meet the requirements of on-board computation. However, most of the current researches focuses on simplified intersections considering only the surrounding vehicles and idealized traffic lights. This paper improves the integrated decision and control framework and develops a learning-based algorithm to deal with complex intersections with mixed traffic flows, which can not only take account of realistic characteristics of traffic lights, but also learn a safe policy under different safety constraints. We first consider different velocity models for green and red lights in the training process and use a finite state machine to handle different modes of light transformation. Then we design different types of distance constraints for vehicles, traffic lights, pedestrians, bicycles respectively and formulize the constrained optimal control problems (OCPs) to be optimized. Finally, reinforcement learning (RL) with value and policy networks is adopted to solve the series of OCPs. In order to verify the safety and efficiency of the proposed method, we design a multi-lane intersection with the existence of large-scale mixed traffic participants and set practical traffic light phases. The simulation results indicate that the trained decision and control policy can well balance safety and tracking performance. Compared with model predictive control (MPC), the computational time is three orders of magnitude lower.
翻译:在十字路口自主驾驶是最复杂和容易发生事故的交通情况之一,特别是车辆、自行车和行人等混合交通参与者的交通情况。驾驶政策应作出安全决定,处理动态交通条件,满足机载计算的要求。然而,目前大多数研究的重点是简化交叉点,只考虑周围车辆和理想交通灯光;本文件改进综合决定和控制框架,并发展基于学习的算法,以处理混合交通流动的复杂交叉点,这种模式不仅能够考虑到交通灯的现实特点,而且还学习不同安全限制下的安全政策。我们首先考虑在培训过程中采用不同的绿色和红灯速度模型,并使用固定状态机器处理不同的灯光转换模式。然后我们分别为车辆、交通灯、行人、自行车设计不同种类的距离限制,并优化限制的最佳控制问题。最后,采用具有价值和政策网络的强化学习模式(RL),以解决一系列的交通灯泡。为了核实拟议方法的安全和效率,我们首先考虑采用不同的绿色和红灯模式,然后使用固定的机器处理不同的灯光转换模式。我们设计了不同的距离限制,然后分别对车辆、交通、行车、行车、行驶、行驶、行驶、路路路路段进行混合分析的结果。通过一个大比例的周期和路路路路路路路路路路段的模拟,以显示和路段和路段路段的模拟,可以以稳定。