Building Real-time Awareness of Out-of-distribution in Trajectory Prediction for Autonomous Vehicles

Embodied AI systems operate in a closed perception-action loop. In many safety-critical applications, embodied AI systems must adapt to highly uncertain and dynamically evolving environments. In the context of autonomous vehicles, trustworthy trajectory prediction is therefore a prerequisite for safe interaction with surrounding agents and infrastructure. In practice, however, even well-trained machine learning models may produce unreliable predictions. Such sim-to-real gaps (also known as imperfect training data) may be unavoidable due to the overwhelming complexity of data annotation and environment uncertainties. To ensure safety, it is critical to quickly detect when a model's predictions become unreliable. Leveraging the intuition that in-distribution (ID) scenes exhibit error patterns similar to training data, while out-of-distribution (OOD) scenes do not, we introduce a principled and computationally efficient approach for OOD detection by framing it as a quickest change-point detection problem. We address the challenging settings where the OOD scenes are deceptive, meaning that they are not easily detectable by human intuitions. Our solutions can handle the occurrence of OOD at any time during trajectory prediction inference. Experimental results on multiple real-world datasets demonstrate the effectiveness of our methods.