Development of an Efficient Driving Strategy for Connected and Automated Vehicles at Signalized Intersections: A Reinforcement Learning Approach

TLDR

Connected and Automated Vehicles share instant traffic information, enabling driving behaviours that are more responsible, accurate, and efficient than those based solely on driver observation. The study proposes a reinforcement‑learning‑based car‑following model for CAVs to achieve appropriate driving behaviour that improves travel efficiency, fuel consumption, and safety at signalized intersections in real time. The model employs reinforcement learning to learn a controller that adapts to varying traffic demands and signal cycle durations. The learned controller performs well under different traffic conditions and signal timings, demonstrating the potential of reinforcement learning for efficient, safe, and fuel‑saving driving at signalized intersections.

Abstract

The concept of Connected and Automated Vehicles (CAVs) enables instant traffic information to be shared among vehicle networks. With this newly proposed concept, a vehicle's driving behaviour will no longer be solely based on the driver's limited and incomplete observation. By taking advantages of the shared information, driving behaviours of CAVs can be improved greatly to a more responsible, accurate and efficient level. This study proposed a reinforcement-learning-based car following model for CAVs in order to obtain an appropriate driving behaviour to improve travel efficiency, fuel consumption and safety at signalized intersections in real-time. The result shows that by specifying an effective reward function, a controller can be learned and works well under different traffic demands as well as traffic light cycles with different durations. This study reveals a great potential of emerging reinforcement learning technologies in transport research and applications.