UST: Unifying Spatio-Temporal Context for Trajectory Prediction in Autonomous Driving

TL;DR

This paper introduces a unified spatio-temporal modeling approach for trajectory prediction in autonomous driving, effectively capturing interactions and behaviors to outperform existing methods.

Contribution

It presents a simple, unified module that treats space and time equally, automatically partitions spatio-temporal data, and surpasses state-of-the-art methods in trajectory prediction.

Findings

Outperforms previous state-of-the-art methods on ApolloScape and Argoverse datasets.

The proposed module is simple, easy to implement, and effective.

The approach effectively models complex interactions in autonomous driving scenarios.

Abstract

Trajectory prediction has always been a challenging problem for autonomous driving, since it needs to infer the latent intention from the behaviors and interactions from traffic participants. This problem is intrinsically hard, because each participant may behave differently under different environments and interactions. This key is to effectively model the interlaced influence from both spatial context and temporal context. Existing work usually encodes these two types of context separately, which would lead to inferior modeling of the scenarios. In this paper, we first propose a unified approach to treat time and space dimensions equally for modeling spatio-temporal context. The proposed module is simple and easy to implement within several lines of codes. In contrast to existing methods which heavily rely on recurrent neural network for temporal context and hand-crafted structure for spatial context, our method could automatically partition the spatio-temporal space to adapt the data. Lastly, we test our proposed framework on two recently proposed trajectory prediction dataset ApolloScape and Argoverse. We show that the proposed method substantially outperforms the previous state-of-the-art methods while maintaining its simplicity. These encouraging results further validate the superiority of our approach.