AnchDrive: Bootstrapping Diffusion Policies with Hybrid Trajectory Anchors for End-to-End Driving

• URL: http://arxiv.org/abs/2509.20253v2
• Date: Fri, 26 Sep 2025 11:00:50 GMT
• Title: AnchDrive: Bootstrapping Diffusion Policies with Hybrid Trajectory Anchors for End-to-End Driving
• Authors: Jinhao Chai, Anqing Jiang, Hao Jiang, Shiyi Mu, Zichong Gu, Hao Sun, Shugong Xu,
• Abstract summary: AnchDrive is a framework for end-to-end driving.<n>It bootstraps a diffusion policy to mitigate the high computational cost of traditional generative models.<n>Experiments on the NAVSIM benchmark confirm that AnchDrive sets a new state-of-the-art.
• Score: 19.724857120152944
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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