同変連続畳み込みによる軌道予測
Trajectory Prediction using Equivariant Continuous Convolution
流体力学の対称性の知見を取り入れ、回転同変な連続畳み込みで車両や歩行者の軌道を予測するECCOを提案し、少ないパラメータで高精度かつ物理的に整合した予測を実現した。
著者: Robin Walters, Jinxi Li, Rose Yu
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
Trajectory prediction is a critical part of many AI applications, for example, the safe operation of autonomous vehicles. However, current methods are prone to making inconsistent and physically unrealistic predictions. We leverage insights from fluid dynamics to overcome this limitation by considering internal symmetry in real-world trajectories. We propose a novel model, Equivariant Continous COnvolution (ECCO) for improved trajectory prediction. ECCO uses rotationally-equivariant continuous convolutions to embed the symmetries of the system. On both vehicle and pedestrian trajectory datasets, ECCO attains competitive accuracy with significantly fewer parameters. It is also more sample efficient, generalizing automatically from few data points in any orientation. Lastly, ECCO improves generalization with equivariance, resulting in more physically consistent predictions. Our method provides a fresh perspective towards increasing trust and transparency in deep learning models.
