Adaptive Multi-Mode Out-of-Distribution Detection for Trajectory Prediction in Autonomous Vehicles

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.