Using Reinforcement Learning to Handle the Unintended Lateral Attack in the Intelligent Connected Vehicle Environment

Abstract

It is widely accepted that an unintended lateral attack is inevitable in the intelligent connected vehicle environment. This paper explores the feasibility of a reinforcement learning method named PPO (Proximal Policy Optimization) to handle the unintended lateral attack and keep the vehicle stay in the ego lane. Based on the China highway design guide, the discrete speed variants of 120 km/h, 100 km/h, and 80 km/h were selected, along with different curvatures ranging from 250 m to 1200 m in every 50 m as combinations of speed-curvature test. The tests were implemented in the Open.ai CarRacing-v0 simulation environment with an external racing wheel attached to simulate the unintended lateral attack. The simulation results show that the PPO can handle the unintended lateral attack on the standard-designed highway in China. The results can be applied to the intelligent connected vehicle to be mass-produced in the future.