arXiv cs.AI· Zhenjun Qiu, Jianing Huang, Dongang Liu, Baiyu Du, Yixun Niu, Hao Yang, Xinyu Huang, Chuan Hu, Shu Liu·· 9 天前AI 评分22
BridgeGuard：面向扩散模型自动驾驶的显式安全漂移约束方法
BridgeGuard: Explicit Safety Drift for Diffusion-based Autonomous Driving
AI 导读
BridgeGuard 是一种安全约束扩散规划方法，在去噪过程中逐步强化约束项，将中间轨迹推向场景相关的安全域，修正仅在低维曲线空间进行以保持几何连贯。其 DistanceFieldNet 模块从鸟瞰图特征预测时变距离场，通过在专家轨迹之外采样查询进行值与空间梯度监督，学习安全与不安全区域。
正文 · 原文
Abstract:Diffusion-based driving planners capture diverse behaviors but can generate unsafe trajectories under distribution shift. We propose BridgeGuard, a safety-constrained diffusion planning method that progressively strengthens a constraint term during denoising to drive intermediate trajectories toward a scene-dependent safety domain. Corrections operate in a low-dimensional curve space, promoting geometric coherence. A learned module, DistanceFieldNet, predicts a time-dependent distance field from bird's-eye-view features. Value and spatial-gradient supervision at queries sampled beyond expert trajectories teaches this field about both safe and unsafe regions. The learned field supplies the constraint term through safety injection while the pretrained perception backbone and planner remain frozen. We further establish sufficient conditions for terminal safety in an idealized continuous-time bridge. On Bench2Drive, BridgeGuard improves driving score/success rate from 87.99/74.99% to 90.88/76.36% for BridgeDrive and from 80.79/58.18% to 90.46/74.09% for $\text{DiffusionDrive}^{\text{geo}}$, demonstrating cross-model generalization.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.11483 [cs.AI] |
| (or arXiv:2610.11483v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11483 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jianing Huang [view email]
[v1]
Thu, 8 Oct 2026 08:26:52 UTC (4,275 KB)
来源：arXiv cs.AI · arxiv.org