Worst-Case Hidden-Vehicle Trajectory Search in Spatiotemporal Occlusion Regions

Abstract:Occlusion creates fundamental uncertainty in autonomous driving. Existing methods often propagate frame-wise hypotheses or optimize ego behavior against prescribed hidden-agent predictions, leaving the worst history-consistent interaction unexplored. We introduce History-Conditioned Minimax Trajectory Search (HC-MTS), which combines temporal occlusion reasoning with response-aware search. First, HC-MTS constructs finite hidden-state modes, each certified by a backward witness satisfying multi-frame visibility, occupancy, semantic-map support, and class-specific kinematic constraints. It then solves a bilevel minimax problem: an inner finite oracle maximizes the ego driving score over destination attainment and ride comfort, while the outer search selects the legal hidden-vehicle trajectory that minimizes this best-response value. Across eight Waymo Open Motion Dataset scenarios, increasing the visibility-memory horizon from K=1 to K=20 reduces the mean per-scenario vehicle, pedestrian, and total retained hidden-seed counts by 18.12%, 21.67%, and 18.45%, respectively. HC-MTS identifies six avoidable counterexamples, while no legal collision-producing attacker is found in the remaining two scenes within the finite search budget.

16 pages, 3 figures. Accepted at the ECCV 2026 Workshop on Safe and Defensive Autonomous Driving (SDAD) as an Oral Presentation; Best Paper Award. Workshop website: this https URL

Robotics (cs.RO); Cryptography and Security (cs.CR)

arXiv:2609.20480 [cs.RO]

(or arXiv:2609.20480v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.20480 arXiv-issued DOI via DataCite (pending registration)