Worst-Case Hidden-Vehicle Trajectory Search in Spatiotemporal Occlusion Regions
Researchers propose HC-MTS, a bilevel minimax search that finds worst-case hidden-vehicle trajectories under occlusion, cutting retained hidden-seed counts ~18-22% on Waymo scenarios.
HC-MTS constructs finite hidden-state modes certified by backward witnesses satisfying multi-frame visibility, occupancy, semantic-map, and kinematic constraints, then solves a bilevel minimax problem where an inner oracle maximizes the ego driving score and an outer search minimizes it via legal hidden-vehicle trajectories. Across eight Waymo Open Motion Dataset scenarios, raising the visibility-memory horizon K from 1 to 20 reduces mean retained hidden-seed counts by 18.12% for vehicles, 21.67% for pedestrians, and 18.45% overall. The method identifies six avoidable counterexamples, while no legal collision-producing attacker was found in the remaining two scenes within the finite search budget.
- HC-MTS certifies hidden-state modes via backward witnesses using visibility, occupancy, semantic-map, and kinematic constraints.
- Solves a bilevel minimax: inner oracle maximizes ego driving score, outer search minimizes it with legal hidden trajectories.
- Extending visibility-memory horizon K from 1 to 20 cuts hidden-seed counts by 18.12-21.67% across eight Waymo scenarios.
- Identifies six avoidable counterexamples; no legal collision-producing attacker found in two remaining scenes within budget.
From the original article
1 min read at arxiv.orgOcclusion 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.
By Ruichen Tan · arxiv.org
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