Sustainable Adaptation for Autonomous Driving with the Mixture of Progressive Experts Networ

Learning-based autonomous driving methods require continuous acquisition of domain knowledge to adapt to diverse driving scenarios. However, due to the inherent challenges of long-tailed data distribution, current approaches still face limitations in complex and dynamic driving environments, particularly when encountering new scenarios and data. This underscores the necessity for enhanced continual learning capabilities to improve system adaptability. To address these challenges, the paper introduces a dynamic progressive optimization framework that facilitates adaptation to variations in dynamic environments, achieved by integrating reinforcement learning and supervised learning for data aggregation. Building on this framework, we propose the Mixture of Progressive Experts (MoPE) network. The proposed method selectively activates multiple expert models based on the distinct characteristics of each task and progressively refines the network architecture to facilitate adaptation to new tasks. Simulation results show that the MoPE model outperforms behavior cloning methods, achieving up to a 7.3% performance improvement in intricate urban road environments.