通过安全引导提升端到端自动驾驶的可靠性。
DriveSafer: End-to-End Autonomous Driving with Safety Guidance
- 在训练和推理阶段引入安全约束，引导生成式规划器避险。
- 在NAVSIM上将致命失误减少48%，可行驶区域合规性提升超65%。
- 适合关注自动驾驶安全性的研究者与工程团队。
近年来，端到端（E2E）自动驾驶模型能力持续提升，在更具挑战性的基准测试中表现优异。然而，当前生成式E2E规划器在安全关键场景中仍存在大量灾难性失败。我们发现，这些失败多源于违反物理约束与安全要求，导致不安全行为。为此，本文提出DriveSafer——一种面向E2E规划器的故障感知安全框架，通过训练时的安全约束与推理时的安全引导，主动规避危险行为。相比最先进模型DiffusionDrive，DriveSafer在NAVSIM基准上将致命失败（PDMS=0）数量减少48%，可行驶区域合规性失败降低超过65%。
原文摘要 · Abstract (English)
End-to-End (E2E) autonomous driving models have shown growing capability in recent years, with performance improving on increasingly challenging benchmarks. However, modern generative E2E planners still suffer from a substantial number of catastrophic failures in safety-critical scenarios. We find that many such failures arise from violations of physical constraints and safety requirements, leading to unsafe behavior. Motivated by this finding, in this paper, we focus on improving safety outcomes in generative end-to-end driving with a targeted reduction of catastrophic planning failures, instead of enhancing average planning quality. Towards this end, we propose DriveSafer, a failure-aware safety framework for end-to-end planners. DriveSafer explicitly steers generative planners towards safe behaviors leveraging both training-time safety constraints and inference-time safety guidance. Compared to the state-of-the-art DiffusionDrive model, on the NAVSIM benchmark, DriveSafer reduces the number of catastrophic failures (PDMS=0) by 48%, with over 65% reduction in drivable-area compliance failures.
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