Evidential Uncertainty Estimation for Multi-Modal Trajectory Prediction

• URL: http://arxiv.org/abs/2503.05274v1
• Date: Fri, 07 Mar 2025 09:46:21 GMT
• Title: Evidential Uncertainty Estimation for Multi-Modal Trajectory Prediction
• Authors: Sajad Marvi, Christoph Rist, Julian Schmidt, Julian Jordan, Abhinav Valada,
• Abstract summary: We propose a novel multi-modal trajectory prediction approach based on evidential deep learning.<n>Our approach estimates both positional and mode probability uncertainty in real time.<n>We perform extensive evaluations of our method on the Argoverse 1 and Argoverse 2 datasets.
• Score: 10.832351645863536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate trajectory prediction is crucial for autonomous driving, yet uncertainty in agent behavior and perception noise makes it inherently challenging. While multi-modal trajectory prediction models generate multiple plausible future paths with associated probabilities, effectively quantifying uncertainty remains an open problem. In this work, we propose a novel multi-modal trajectory prediction approach based on evidential deep learning that estimates both positional and mode probability uncertainty in real time. Our approach leverages a Normal Inverse Gamma distribution for positional uncertainty and a Dirichlet distribution for mode uncertainty. Unlike sampling-based methods, it infers both types of uncertainty in a single forward pass, significantly improving efficiency. Additionally, we experimented with uncertainty-driven importance sampling to improve training efficiency by prioritizing underrepresented high-uncertainty samples over redundant ones. We perform extensive evaluations of our method on the Argoverse 1 and Argoverse 2 datasets, demonstrating that it provides reliable uncertainty estimates while maintaining high trajectory prediction accuracy.

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