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

• URL: http://arxiv.org/abs/2409.17277v2
• Date: Wed, 23 Apr 2025 16:07:20 GMT
• Title: Building Real-time Awareness of Out-of-distribution in Trajectory Prediction for Autonomous Vehicles
• Authors: Tongfe Guo, Taposh Banerjee, Rui Liu, Lili Su,
• Abstract summary: Even well-trained machine learning models may produce unreliable predictions due to discrepancies between training data and real-world conditions encountered during inference.<n>We introduce a principled, real-time approach for OOD detection by framing it as a change-point detection problem.<n>Our lightweight solutions can handle the occurrence of OOD at any time during trajectory prediction inference.
• Score: 9.009998323918621
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate trajectory prediction is essential for the safe operation of autonomous vehicles in real-world environments. Even well-trained machine learning models may produce unreliable predictions due to discrepancies between training data and real-world conditions encountered during inference. In particular, the training dataset tends to overrepresent common scenes (e.g., straight lanes) while underrepresenting less frequent ones (e.g., traffic circles). In addition, it often overlooks unpredictable real-world events such as sudden braking or falling objects. To ensure safety, it is critical to detect in real-time 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, real-time approach for OOD detection by framing it as a 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 lightweight solutions can handle the occurrence of OOD at any time during trajectory prediction inference. Experimental results on multiple real-world datasets using a benchmark trajectory prediction model demonstrate the effectiveness of our methods.

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