提出可证明保证的轨迹预测异常检测方法，有效识别自动驾驶中的罕见场景。
Latent Dynamics-Aware OOD Monitoring for Trajectory Prediction with Provable Guarantees
- 基于隐马尔可夫模型建模正常误差演化，实现对分布外变化的敏感捕捉。
- 在三个真实数据集上，检测延迟显著降低，且对极端分布和未知变化鲁棒。
- 无需先验知识即可提供误报率与延迟的理论保证，适合高安全需求场景。
在安全关键的网络物理系统中，轨迹预测支撑后续规划与控制。深度学习模型在验证数据上表现良好，但在环境不确定或罕见交通行为导致的分布外（OOD）场景下可靠性下降。此类失效常无声无息：预测仍具空间合理性，准确率却骤降，报告的不确定性却不升高。检测困难源于交通状态与交互模式持续演变，而自动驾驶的安全性要求对检测延迟与误报率有形式化保障。受[4]启发，我们将OOD监测重构为快速变点检测（QCD），一个具有成熟理论基础的统计框架。我们发现，分布内（ID）数据上的预测误差演化可被隐马尔可夫模型（HMM）良好刻画。基于此，我们将近期累积最大均值差异方法拓展至本场景。该方法无需关于变化后分布的详细先验，但仍可提供可证明的检测延迟与误报率保证。在三个真实驾驶数据集上，该方法显著降低检测延迟，同时对重尾分布和未知变化条件保持鲁棒。
原文摘要 · Abstract (English)
In safety-critical Cyber-Physical Systems (CPS), trajectory prediction guides downstream planning and control. Deep learning models forecast well on validation data, but their reliability drops in out-of-distribution (OOD) scenarios driven by environmental uncertainty or rare traffic behaviors [1, 2]. Such failures are often silent: forecasts stay spatially plausible while accuracy collapses, and reported uncertainty does not rise [3]. Detection is hard because traffic conditions and interaction patterns keep evolving, yet the safety-critical nature of autonomous driving (AD) demands formal guarantees on detection delay and false-alarm rate. Following [4], we reframe OOD monitoring as quickest changepoint detection (QCD), a principled statistical framework with well-established theory. We find that the evolution of prediction errors on in-distribution (ID) data is well modeled by a Hidden Markov Model (HMM). Building on this, we extend a recent cumulative Maximum Mean Discrepancy approach to our setting. The method needs no detailed prior knowledge of the post-change distribution, yet admits provable delay and false-alarm guarantees. On three real-world driving datasets, it reduces detection delay while staying robust to heavy-tailed distributions and unknown post-change conditions.
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