用专家路由增强扩散模型，让自动驾驶更智能、可解释。
A Knowledge-Driven Diffusion Policy for End-to-End Autonomous Driving Based on Expert Routing
- 引入稀疏专家路由机制，动态激活专用模块。
- 在多个场景中成功率更高，碰撞风险降低，控制更平滑。
- 适合需要可解释性和模块化设计的自动驾驶研究者。
端到端自动驾驶仍受限于多样场景下生成自适应、鲁棒且可解释决策的难度。现有方法常导致驾驶行为同质化，缺乏长时序一致性，或需任务特定工程，限制泛化能力。本文提出KDP，一种基于知识驱动的扩散策略，将生成式扩散建模与稀疏专家混合路由机制结合。扩散组件生成时间上连贯的动作序列，专家路由机制根据上下文激活专门且可复用的专家，实现模块化知识组合。在代表性驾驶场景中的大量实验表明，KDP相比主流范式在成功率、碰撞风险和控制平滑性方面均表现更优。消融实验验证了稀疏专家激活和Transformer骨干网络的有效性，激活分析揭示了专家的结构化专业化与跨场景复用特性。这些结果确立了扩散与专家路由结合为可扩展、可解释的知识驱动端到端自动驾驶新范式。
原文摘要 · Abstract (English)
End-to-end autonomous driving remains constrained by the difficulty of producing adaptive, robust, and interpretable decision-making across diverse scenarios. Existing methods often collapse diverse driving behaviors, lack long-horizon consistency, or require task-specific engineering that limits generalization. This paper presents KDP, a knowledge-driven diffusion policy that integrates generative diffusion modeling with a sparse mixture-of-experts routing mechanism. The diffusion component generates temporally coherent action sequences, while the expert routing mechanism activates specialized and reusable experts according to context, enabling modular knowledge composition. Extensive experiments across representative driving scenarios demonstrate that KDP achieves consistently higher success rates, reduced collision risk, and smoother control compared to prevailing paradigms. Ablation studies highlight the effectiveness of sparse expert activation and the Transformer backbone, and activation analyses reveal structured specialization and cross-scenario reuse of experts. These results establish diffusion with expert routing as a scalable and interpretable paradigm for knowledge-driven end-to-end autonomous driving.
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