Lightweight Strategies for Decision-Making of Autonomous Vehicles in Lane Change Scenarios Based on Deep Reinforcement Learning

High-performance vision-based decision-making networks are often limited by hardware capabilities in practical applications. To address this challenge, this study proposes lightweight optimization strategies for decision-making models from the aspects of parameter size, training memory usage, and inference speed. Specifically, an innovative solution is proposed to achieve lightweight parameters. The Video Swin Transformer is employed to simultaneously extract temporal and spatial features, with the network trained using a Prioritized Replay Deep Q-Network (PRDQN) that incorporates risk assessment. To further reduce training memory usage, the Q-target network in PRDQN is removed, and the mellowmax operator is integrated to enhance the training process, resulting in the PRDeepMellow Swin Transformer. After analyzing the inference speed problems encountered by the algorithm in practical applications, the vanilla self-attention is replaced by a linear self-attention based on double softmax, namely Double Softmax Linear Video Swin Transformer (DSLVS Transformer) which improves the inference speed for long sequences. The proposed methods were evaluated across three high-speed lane change scenarios (a static scenario, a dynamic scenario, and a randomly changing scenario). Experimental results demonstrate that the proposed methods can still maintain excellent decision performance after the corresponding lightweight optimizations.