Which Trajectory Is Safer? Combining Rule and Learning for Safety Evaluation

Title:Which Trajectory Is Safer? Combining Rule and Learning for Safety Evaluation

Abstract:Collision rate remains a dominant metric for evaluating autonomous driving trajectory safety, but its binary nature obscures substantial safety differences among collision-free trajectories. Existing surrogate-safety metrics provide finer-grained assessments, yet often trade off physical interpretability against adaptivity to interaction context. We introduce FutureRisk, a continuous metric that quantifies the latent future risk of a trajectory by how closely and persistently its realized interactions approach unsafe states. At its core, our Context-Adaptive Risk Model (CARM) combines a context-dependent safety-time distribution with an independently supervised critical-event-time distribution. Its safety criterion is bounded around a closed-form physical prior, while a bounded residual provides additional adaptation without abandoning this physical anchor. Experiments on DeepAccident and Bench2Drive show that FutureRisk achieves stronger agreement with native collision labels than existing surrogate-safety measures, discriminates risk among collision-free trajectories, and transfers zero-shot across datasets. Comparisons across four planners further show that FutureRisk reveals safety differences hidden by binary collision outcomes. Together, these results establish FutureRisk as a fine-grained, interpretable, and context-adaptive metric for trajectory safety evaluation. Code is available at this https URL.

Robotics (cs.RO)

arXiv:2610.17806 [cs.RO]

(or arXiv:2610.17806v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.17806 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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