针对自动驾驶轨迹预测的多模式异常检测方法
Adaptive Multi-Mode Out-of-Distribution Detection for Trajectory Prediction in Autonomous Vehicles
- 基于误差模式感知的CUSUM算法，动态识别多类型错误模式
- 在真实数据集上实现检测延迟降低与误报率减少
- 适用于复杂开放道路场景下的轨迹预测可靠性监控
可信的轨迹预测是保障自动驾驶安全的基础，但部署模型不可避免会遇到分布外（OOD）场景。以往的自动驾驶OOD检测主要针对感知层面，而规划器依赖的是预测的未来轨迹而非原始场景，因此错误的预测可能绕过帧级检测并影响控制决策。为此，我们提出在轨迹预测层面进行OOD检测。对真实世界基准的数据分析表明，预测误差具有多模态特性，表现出低误差和高误差两种模式，且随开放道路环境变化而演化。基于此，我们提出模式感知的CUSUM方法，显式建模多种误差模式，同时保持经典CUSUM的高效性与通用性。通过动态识别当前活跃的误差模式并自适应调整检测阈值，该方法可在多样化条件下实现稳健监测。在大规模轨迹预测基准上的实验显示，该方法显著降低了检测延迟和误报率。
原文摘要 · Abstract (English)
Trustworthy trajectory prediction grounds autonomous vehicle (AV) safety, yet deployed models inevitably face out-of-distribution (OOD) scenes. Prior AV OOD detection targets perception, but planners act on predicted futures rather than raw scenes, so erroneous forecasts can slip past frame-level checks and corrupt control. We therefore tackle OOD detection at the trajectory-prediction level. Our analysis of real-world benchmarks reveals that prediction errors are often multi-modal, exhibiting distinct low- and high-error modes that evolve with open-world driving context. Observing this, we propose Mode-Aware CUSUM, which explicitly models multiple error modes while retaining the efficiency and general compatibility of classical CUSUM. By dynamically identifying the active error mode and adapting detection thresholds, our method enables robust monitoring across heterogeneous conditions. Experiments on large-scale trajectory benchmarks demonstrate consistent reductions in detection delay and false alarms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。