Safety-Aware Event-Triggered Intervention for Motion Planning and Decision Making in Diffusion-Based Autonomous Driving

Engineering notes

On NAVSIM navtest, global planner metrics remain nearly unchanged across sparse trigger policies, but the semantic trigger achieves better triggered-subset final score, TTC, and progress than matched-random and TTC-based triggers.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Diffusion-based trajectory planners achieve strong nominal performance in autonomous driving, but sparse safety intervention remains difficult to evaluate and realize effectively. This study addresses this problem by proposing a safety-aware event-triggered intervention framework on top of a fixed DiffusionDriveV2 planner. The method uses candidate-level risk signals and an auxiliary semantic risk trigger to decide when intervention should be activated, and realizes the intervention through conservative re-selection and mild action-space augmentation. To evaluate sparse interventions beyond global validation metrics, we further construct normal, conservative, and actual trajectories and introduce triggered-subset counterfactual evaluation. On NAVSIM navtest, global planner metrics remain nearly unchanged across sparse trigger policies, but the semantic trigger achieves better triggered-subset final score, TTC, and progress than matched-random and TTC-based triggers. Qualitative cases show that behaviorally distinct safety responses mainly arise from action-space augmentation rather than candidate reranking alone. These results show that the proposed framework can diagnose and partially alleviate the gap between risk recognition and action realization, while revealing that stronger semantic-conditioned action generation is needed to fully overcome the trigger-to-action bottleneck.

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