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].

Core Insights - The article discusses the MoSE (Skill-by-Skill Mixture-of-Expert) framework, which enhances the reasoning capabilities of small-scale visual language models (VLMs) in autonomous driving tasks by simulating human learning processes [2][10][46]. Group 1: MoSE Framework Overview - MoSE is inspired by human drivers' learning processes, allowing for skill-based, step-by-step learning in driving tasks [2][10]. - The framework employs a skill-centric routing mechanism that enables the model to identify and learn specific driving skills required for various scenarios [12][14]. - MoSE achieves state-of-the-art performance in extreme driving scenarios while significantly reducing the number of activated parameters by at least 62.5% compared to existing methods [10][35]. Group 2: Technical Implementation - The model integrates a hierarchical skill dataset and pre-trains routers to encourage step-by-step reasoning, aligning with human-like multi-step planning [2][8]. - MoSE utilizes a sparse mixture of experts (MoE) configuration, where only a portion of the model's parameters are activated during inference, enhancing computational efficiency [7][21]. - The framework has been tested on the CODA dataset, which focuses on multi-modal extreme driving situations, demonstrating superior performance compared to larger models [26][32]. Group 3: Experimental Results - In experiments, MoSE outperformed several state-of-the-art models with over 80 billion parameters while using less than 30 billion parameters [35]. - The results indicate that MoSE maintains robust performance even with a limited amount of training data, confirming its efficiency in utilizing available resources [42][44]. - The model's performance improves steadily with increased data size, showcasing its scalability and adaptability to various datasets and tasks [40][46]. Group 4: Future Directions - The article suggests that further research is needed to explore MoSE's applicability in trajectory estimation tasks and its integration with closed-loop evaluations in simulation environments [48]. - The potential for MoSE to be adapted for diverse downstream tasks and pre-trained models is highlighted, indicating a promising direction for future developments in autonomous driving technology [48].