TRAJECTORY PREDICTION USING EQUIVARIANT CON-TINUOUS CONVOLUTION

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 rotationallyequivariant 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. Our code and data can be found at

1. INTRODUCTION

Trajectory prediction is one of the core tasks in AI, from the movement of basketball players to fluid particles to car traffic (Sanchez-Gonzalez et al., 2020; Gao et al., 2020; Shah & Romijnders, 2016) . A common abstraction underlying these tasks is the movement of many interacting agents, analogous to a many-particle system. Therefore, understanding the states of these particles, their dynamics, and hidden interactions is critical to accurate and robust trajectory forecasting. 



Figure 1: Car trajectories in two scenes. Though the entire scenes are not related by a rotation, the circled areas are. ECCO exploits this symmetry to improve generalization and sample efficiency.Even for purely physical systems such as in particle physics, the complex interactions among a large number of particles makes this a difficult problem. For vehicle or pedestrian trajectories, this challenge is further compounded with latent factors such as human psychology. Given these difficulties, current approaches require large amounts of training data and many model parameters. State-of-the-art methods in this domain such as Gao et al. (2020) are based on graph neural networks. They do not exploit the physical properties of system and often make predictions which are not self-consistent or physically meaningful. Furthermore, they predict a single agent trajectory at a time instead of multiple agents simultaneously. * Equal Contribution

availability

https://github.com/Rose

