Learning Cooperative Trajectories at Intersections in Mixed Traffic

Abstract

AbstractIntersections are a significant bottleneck in traffic and have been a topic of much research. Optimization approaches incorporating traffic models are often limited by the intractable complexity resulting from the combinatorial explosion associated with increasing numbers of vehicles. Learning cooperative maneuver policies with deep neural networks from traffic data is a promising approach to address this issue. This chapter presents two approaches for managing traffic at intersections using deep reinforcement learning. The first approach learns an adaptive traffic signal controller, serving as a trajectory planner for all vehicles at the intersection. For smaller intersections with less traffic and fewer lanes, traffic signs are preferred over traffic lights due to their lower cost and higher efficiency. The second approach uses a centralized control unit to optimize efficiency and equity by ordering automated vehicles to yield to vehicles on conflicting routes with lower priorities. Self-driving cars have the potential to improve traffic flow in mixed environments with human-driven vehicles. The chapter evaluates the approaches using a traffic simulator with simulated and real-world traffic data. The approaches achieve state-of-the-art performance in terms of traffic efficiency and equity compared to non-learning and other learning-based methods. The chapter concludes with a discussion of possible future work on learning cooperative trajectories in mixed traffic.