Problem
Research questions and friction points this paper is trying to address.
Occlusion
Autonomous Driving
Uncertainty
Worst-Case Interaction
Worst-Case Hidden-Vehicle Trajectory Search in Spatiotemporal Occlusion Regions
🤖 AI Summary
本文提出了一种历史条件最小最大轨迹搜索方法，通过结合时间遮挡推理与响应感知搜索来解决自动驾驶中因遮挡导致的不确定性问题。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
History-Conditioned Minimax Trajectory Search (HC-MTS)
temporal occlusion reasoning
response-aware search
bilevel minimax problem
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