用混合轨迹锚点加速扩散模型，实现高效高质端到端自动驾驶规划
AnchDrive: Bootstrapping Diffusion Policies with Hybrid Trajectory Anchors for End-to-End Driving
- 以静态驾驶先验和动态感知轨迹为锚点，启动扩散模型生成
- 在NAVSIM上达到新SOTA，生成多样且高质量的驾驶轨迹
- 适合追求高效、可泛化的自动驾驶系统研发者
端到端多模态规划已成为自动驾驶领域的变革性范式，有效应对行为多模态与长尾场景泛化难题。本文提出AnchDrive框架，通过混合轨迹锚点引导扩散策略，显著降低传统生成模型的高计算开销。不同于从纯噪声开始去噪，AnchDrive以丰富的混合轨迹锚点作为初始规划：包括通用驾驶先验的静态词汇库，以及由处理密集与稀疏感知特征的Transformer实时解码的动态上下文感知轨迹。扩散模型则学习预测轨迹偏移分布，对这些锚点进行精细化修正。该锚点引导的自举设计实现了高效、多样化且高质量的轨迹生成。在NAVSIM基准测试中，AnchDrive取得新SOTA性能，展现出强泛化能力。
原文摘要 · Abstract (English)
End-to-end multi-modal planning has become a transformative paradigm in autonomous driving, effectively addressing behavioral multi-modality and the generalization challenge in long-tail scenarios. We propose AnchDrive, a framework for end-to-end driving that effectively bootstraps a diffusion policy to mitigate the high computational cost of traditional generative models. Rather than denoising from pure noise, AnchDrive initializes its planner with a rich set of hybrid trajectory anchors. These anchors are derived from two complementary sources: a static vocabulary of general driving priors and a set of dynamic, context-aware trajectories. The dynamic trajectories are decoded in real-time by a Transformer that processes dense and sparse perceptual features. The diffusion model then learns to refine these anchors by predicting a distribution of trajectory offsets, enabling fine-grained refinement. This anchor-based bootstrapping design allows for efficient generation of diverse, high-quality trajectories. Experiments on the NAVSIM benchmark confirm that AnchDrive sets a new state-of-the-art and shows strong generalizability
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。