Latent Dynamics-Aware OOD Monitoring for Trajectory Prediction with Provable Guarantees

• URL: http://arxiv.org/abs/2603.14603v1
• Date: Sun, 15 Mar 2026 20:56:57 GMT
• Title: Latent Dynamics-Aware OOD Monitoring for Trajectory Prediction with Provable Guarantees
• Abstract summary: In safety-critical Cyber-Physical Systems, accurate trajectory prediction provides vital guidance for downstream planning and control.<n> detecting such OOD events is challenging due to evolving traffic conditions and changing interaction patterns.<n>We extend the cumulative Maximum Discrepancy approach to enable detection without requiring explicit knowledge of the post-change distribution.<n>Experiments on three real-world driving datasets demonstrate reduced detection delay and robustness to heavy-tailed errors and unknown post-change conditions.
• Score: 8.920589816043298
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In safety-critical Cyber-Physical Systems (CPS), accurate trajectory prediction provides vital guidance for downstream planning and control, yet although deep learning models achieve high-fidelity forecasts on validation data, their reliability degrades under out-of-distribution (OOD) scenarios caused by environmental uncertainty or rare traffic behaviors in real-world deployment; detecting such OOD events is challenging due to evolving traffic conditions and changing interaction patterns, while safety-critical applications demand formal guarantees on detection delay and false-alarm rates, motivating us-following recent work [1]-to formulate OOD monitoring for trajectory prediction as a quickest changepoint detection (QCD) problem that offers a principled statistical framework with established theory; we further observe that the real-world evolution of prediction errors under in-distribution (ID) conditions can be effectively modeled by a Hidden Markov Model (HMM), and by leveraging this structure we extend the cumulative Maximum Mean Discrepancy approach to enable detection without requiring explicit knowledge of the post-change distribution while still admitting provable guarantees on delay and false alarms, with experiments on three real-world driving datasets demonstrating reduced detection delay and robustness to heavy-tailed errors and unknown post-change conditions.

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