マルチモーダル軌道予測のためのエビデンシャル不確かさ推定
Evidential Uncertainty Estimation for Multi-Modal Trajectory Prediction
自動運転の軌道予測において、位置とモード確率の不確かさを1回の順伝播でリアルタイムに推定するエビデンシャル深層学習手法を提案し、Argoverseデータセットで精度と信頼性を検証した。
著者: Sajad Marvi, Christoph Rist, Julian Schmidt, Julian Jordan, Abhinav Valada
分類: cs.RO, cs.AI
原文アブストラクト
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.
