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

• Published in: IEEE transactions on intelligent transportation systems Vol. 21; no. 1; pp. 433 - 443
• Main Authors: Zhou, Mofan, Yu, Yang, Qu, Xiaobo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automation; Car following; deep deterministic policy gradient; Driver behavior; Driving; intersection; Intersections; Learning; machine learning; Neural network; Optimization; Oscillators; Real-time systems; Reinforcement learning; Traffic information; traffic light; traffic oscillation; Traffic signals; Training; Trajectory; Vehicles

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 advan...