{"ID":2864096,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23589","arxiv_id":"2509.23589","title":"BridgeDrive: Diffusion Bridge Policy for Closed-Loop Trajectory Planning in Autonomous Driving","abstract":"Diffusion-based planners have shown strong potential for autonomous driving by capturing multi-modal driving behaviors. A key challenge is how to effectively guide these models for safe and reactive planning in closed-loop settings, where the ego vehicle's actions influence future states. Recent work leverages typical expert driving behaviors (i.e., anchors) to guide diffusion planners but relies on a truncated diffusion schedule that introduces an asymmetry between the forward and denoising processes, diverging from the core principles of diffusion models. To address this, we introduce BridgeDrive, a novel anchor-guided diffusion bridge policy for closed-loop trajectory planning. Our approach formulates planning as a diffusion bridge that directly transforms coarse anchor trajectories into refined, context-aware plans, ensuring theoretical consistency between the forward and reverse processes. BridgeDrive is compatible with efficient ODE solvers, enabling real-time deployment. We achieve state-of-the-art performance on the Bench2Drive closed-loop evaluation benchmark, improving the success rate by 7.72% and 2.45% over prior arts with PDM-Lite and LEAD datasets, respectively. Project page: https://github.com/shuliu-ethz/BridgeDrive.","short_abstract":"Diffusion-based planners have shown strong potential for autonomous driving by capturing multi-modal driving behaviors. A key challenge is how to effectively guide these models for safe and reactive planning in closed-loop settings, where the ego vehicle's actions influence future states. Recent work leverages typical...","url_abs":"https://arxiv.org/abs/2509.23589","url_pdf":"https://arxiv.org/pdf/2509.23589v4","authors":"[\"Shu Liu\",\"Wenlin Chen\",\"Weihao Li\",\"Zheng Wang\",\"Lijin Yang\",\"Jianing Huang\",\"Yipin Zhang\",\"Zhongzhan Huang\",\"Ze Cheng\",\"Hao Yang\"]","published":"2025-09-28T02:47:12Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.CV\",\"cs.LG\"]","methods":"[\"Diffusion Model\"]","has_code":false,"code_links":[{"ID":609100,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2864096,"paper_url":"https://arxiv.org/abs/2509.23589","paper_title":"BridgeDrive: Diffusion Bridge Policy for Closed-Loop Trajectory Planning in Autonomous Driving","repo_url":"https://github.com/shuliu-ethz/BridgeDrive","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}