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.