原文：Worst-Case Hidden-Vehicle Trajectory Search in Spatiotemporal Occlusion Regions
作者：Ruichen Tan, Zengxiang Lei, Satish Ukkusuri
来源：arXiv cs.RO（机器人）
正文
Computer Science > Robotics
arXiv:2609.20480v1 (cs)
[Submitted on 17 Sep 2026]
Title:Worst-Case Hidden-Vehicle Trajectory Search in Spatiotemporal Occlusion Regions
Authors:Ruichen Tan, Zengxiang Lei, Satish Ukkusuri
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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.
Comments:
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
Subjects:
Robotics (cs.RO); Cryptography and Security (cs.CR)
Cite as:
arXiv:2609.20480 [cs.RO]
(or
arXiv:2609.20480v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.20480
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arXiv-issued DOI via DataCite (pending registration)
主题
机器人 · 安全
由「前沿雷达」于 2026-09-17 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。
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