提出动态专家混合网络，让自动驾驶持续学习新路况。
Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network
- 用多专家模型按任务特点动态激活，逐步优化结构。
- 在复杂城市道路中比行为克隆提升7.8%性能。
- 适合需要长期适应真实交通变化的自动驾驶系统。
基于学习的自动驾驶需持续整合复杂交通中的多样化知识，但现有方法在自适应能力上存在明显局限。为填补这一空白，本文提出一种动态渐进优化框架，通过融合强化学习与监督学习实现数据聚合，支持对动态环境变化的持续适应。在此基础上，设计了混合渐进专家（Mixture of Progressive Experts, MoPE）网络，根据任务特征选择性激活多个专家模型，并逐步优化网络架构以适应新任务。仿真结果表明，MoPE模型在复杂城市道路环境中相比行为克隆方法性能提升最高达7.8%。
原文摘要 · Abstract (English)
Learning-based autonomous driving requires continuous integration of diverse knowledge in complex traffic , yet existing methods exhibit significant limitations in adaptive capabilities. Addressing this gap demands autonomous driving systems that enable continual adaptation through dynamic adjustments to evolving environmental interactions. 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 7.8% performance improvement in intricate urban road environments.
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