端到端再进化！用扩散模型和MoE打造会思考的自动驾驶Policy（同济大学）

Core Viewpoint - The article presents a novel end-to-end autonomous driving strategy called Knowledge-Driven Diffusion Policy (KDP), which integrates diffusion models and Mixture of Experts (MoE) to enhance decision-making capabilities in complex driving scenarios [4][72]. Group 1: Challenges in Current Autonomous Driving Approaches - Existing end-to-end methods face challenges such as inadequate handling of multimodal distributions, leading to unsafe or hesitant driving behaviors [2][8]. - Reinforcement learning methods require extensive data and exhibit instability during training, making them difficult to scale in high-safety real-world scenarios [2][8]. - Recent advancements in large models, including visual-language models, show promise in understanding scenes but struggle with inference speed and safety in continuous control scenarios [3][10]. Group 2: Diffusion Models and Their Application - Diffusion models are transforming generative modeling in various fields, offering a robust way to express diverse driving choices while maintaining temporal consistency and training stability [3][12]. - The diffusion policy (DP) treats action generation as a "denoising" process, effectively addressing the diversity and long-term stability issues in driving decisions [3][12]. Group 3: Mixture of Experts (MoE) Framework - MoE technology allows for the activation of a limited number of experts on demand, enhancing computational efficiency and modularity in large models [3][15]. - In autonomous driving, MoE has been applied for multi-task strategies, but existing designs often limit expert reusability and flexibility [3][15]. Group 4: Knowledge-Driven Diffusion Policy (KDP) - KDP combines the strengths of diffusion models and MoE, ensuring diverse and stable trajectory generation while organizing experts into structured "knowledge units" for flexible combination based on different driving scenarios [4][6]. - Experimental results demonstrate KDP's advantages in diversity, stability, and generalization compared to traditional methods [4][6]. Group 5: Experimental Validation - The method was evaluated in a simulation environment with diverse driving scenarios, showing superior performance in safety, generalization, and efficiency compared to existing baseline models [39][49]. - The KDP framework achieved a 100% success rate in simpler scenarios and maintained high performance in more complex environments, indicating its robustness [57][72].