Worst-Case

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Period ending 2026-09-21

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Period ending 2026-09-14

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33 papers

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Worst-Case Hidden-Vehicle Trajectory Search in Spatiotemporal Occlusion Regions
Sep 17, 2026Ruichen Tan, Zengxiang Lei, Satish UkkusuriAutonomous Driving Trajectory Optimization
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Kernel Methods for Refined Prophet Inequalities
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Policy Gradient for Continuous-Time Robust Markov Decision Processes
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A Practical Upper Bound on Selection Bias Effects in Medical Prediction Models
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Improved Distribution Estimation in $\ell_\infty$
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On Language Generation in the Limit with Bounded Memory
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Instance-Adaptive Online Multicalibration
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Lyapunov-Certified Direct Switching Theory for Q-Learning
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The Cost of Relaxation: Evaluating the Error in Convex Neural Network Verification
Apr 20, 2026Merkouris Papamichail, Konstantinos Varsos, Giorgos Flouris +1Neural Network Verification Convex Sets
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Apr 19, 2026Davin Choo, Paul W. Goldberg, Nicholas TehLotteries Betting
Algorithms for Deciding the Safety of States in Fully Observable Non-deterministic Problems: Technical Report
Mar 16, 2026Johannes Schmalz, Chaahat JainSafety Constraints Worst-Case
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Sep 8, 2025William Xu, Chenyu Zhang, Yihan Wang +5Data Poisoning Poisoning
Random Walk Learning and the Pac-Man Attack
Aug 1, 2025Xingran Chen, Parimal Parag, Rohit Bhagat +2Worst-Case Adversarial Training
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Sep 17, 2026 · cs.ROJ/K move · Enter open · S save

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

Ruichen Tan, Zengxiang Lei, Satish Ukkusuri

Lyles School of Civil and Construction Engineering, Purdue University, West Lafayette, IN 47907, USA

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.

Autonomous Driving Trajectory Optimization Trajectory Prediction Worst-Case

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