提出扩散桥策略，实现自动驾驶闭环轨迹规划的高效安全生成。
BridgeDrive: Diffusion Bridge Policy for Closed-Loop Trajectory Planning in Autonomous Driving
- 用扩散桥机制将粗略路径直接优化为上下文感知的精细轨迹
- 在Bench2Drive上成功率达94.8%，比PDM-Lite提升7.72%
- 兼容实时求解器，适合需要快速响应的自动驾驶系统
基于扩散的规划器在自动驾驶中展现出捕捉多模态驾驶行为的强大潜力。核心挑战在于如何在闭环设置下有效引导模型，使自车动作影响未来状态时仍能保持安全与响应性。现有方法依赖典型专家驾驶行为（即锚点）来引导扩散规划器，但采用截断的扩散过程，导致前向与反向过程不对称，违背了扩散模型的基本原理。为此，我们提出BridgeDrive，一种新型锚点引导的扩散桥策略，用于闭环轨迹规划。该方法将规划建模为扩散桥，直接将粗略锚点轨迹转化为精细化、上下文感知的规划，确保前后过程理论一致。BridgeDrive兼容高效的常微分方程求解器，支持实时部署。在Bench2Drive闭环评估基准上，性能达到领先水平，相比PDM-Lite和LEAD数据集分别提升成功率7.72%和2.45%。项目页面：https://github.com/shuliu-ethz/BridgeDrive。
原文摘要 · Abstract (English)
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.
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