Trajectory Prediction using Equivariant Continuous Convolution

• URL: http://arxiv.org/abs/2010.11344v2
• Date: Wed, 17 Mar 2021 22:07:18 GMT
• Title: Trajectory Prediction using Equivariant Continuous Convolution
• Authors: Robin Walters, Jinxi Li, Rose Yu
• Abstract summary: Trajectory prediction is a critical part of many AI applications, for example, the safe operation of autonomous vehicles. We propose a novel model, Equivariant Continous COnvolution (ECCO) for improved trajectory prediction. On both vehicle and pedestrian trajectory datasets, ECCO attains competitive accuracy with significantly fewer parameters.
• Score: 21.29323790182332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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