Enhancing Lane Change Safety and Efficiency in Autonomous Driving Through Improved Reinforcement Learning for Highway Decision-Making

Abstract

Autonomous driving (AD) significantly reduces road accidents, providing safer transportation while optimizing traffic flow for greater efficiency and smoothness. However, ensuring safe decision-making in dynamic and complex highway environments, especially during lane-changing maneuvers, remains a challenge. Reinforcement Learning (RL) has become a promising method for developing decision-making systems in AD, particularly Deep Reinforcement Learning (DRL). In this study, we focus on highway lane-change behaviors and propose a novel DRL algorithm, called Huber-regularized Reward-threshold Adaptive Double Deep Q-Network (HRA-DDQN). First, a reward function optimally balances speed, safety, and the necessity of lane changes, ensuring efficient and safe maneuvering in highway scenarios. Second, the dynamic target network update strategy triggered by reward difference is introduced into HRA-DDQN, which enhances the model’s adaptability to varying traffic conditions. Finally, a hybrid loss function, combining Huber loss with L2 regularization, is implemented in HRA-DDQN to improve robustness against outliers and mitigate overfitting. Simulation results demonstrate that the proposed decision framework significantly enhances both driving efficiency and safety, outperforming other methods by yielding higher rewards, lower collision rates, and more stable lane-changing decisions.