Reinforcement Learning Approach for Highway Lane-Changing: PPO-Based Strategy Design

This paper presents a novel approach to highway lane-changing using reinforcement learning, specifically employing the Proximal Policy Optimization (PPO) algorithm. The proposed strategy aims to enhance the safety, efficiency, and smoothness of lane changes in high-speed driving environments. By modeling the lane-changing task as a Markov Decision Process (MDP), the PPO-based agent learns an optimal policy through continuous interaction with a simulated highway environment. Extensive experiments demonstrate that the trained policy effectively balances the trade-off between aggressive and conservative maneuvers, adapting dynamically to surrounding traffic conditions. The results indicate that the PPO-driven strategy outperforms traditional rule-based methods in terms of maneuver success rate, travel time, and passenger comfort. This work contributes to the advancement of autonomous driving technologies by providing a robust and adaptive lane-changing solution leveraging state-of-the-art reinforcement learning techniques.