Hu, Fengqing (The Hong Kong Polytechnic University), Wu, Jingda (The Hong Kong Polytechnic University), Huang, Chao (The Hong Kong Polytechnic University)

Event-Triggered Control for Automated Vehicles Based on Safe Reinforcement Learning

Scheduled for presentation during the Invited Session "Cooperative Driving Technology for Connected Automated Vehicles" (WeAT10), Wednesday, September 25, 2024, 10:30−10:50, Salon 18

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

A novel event-triggered control framework is proposed in this paper to realize safe reinforcement learning (SRL) for autonomous vehicle (AV) control. Safety is guaranteed by designing an additional safe controller to correct the unsafe actions proposed by the deep reinforcement learning (DRL) agent. Event-triggered control barrier functions (CBFs) are used to impose safety constraints on the actions in a discrete manner. Based on twin delayed deep deterministic policy gradient (TD3), an event-triggered safe TD3 (ET-STD3) is presented for safe AV control. Experiments are conducted to test the proposed ET-STD3 in a simulated car-following scenario and compare it to RL-based and model-based baselines. Results show that ET-STD3 achieves better control and safety performance and comparable triggering times compared to the event-triggered baselines.