Context-Aware Timewise VAEs for Real-Time Vehicle Trajectory Prediction

Abstract

Real-time, accurate prediction of human steering behaviors has wide applications, from developing intelligent traffic systems to deploying autonomous driving systems in both real and simulated worlds. In this paper, we present ContextVAE, a context-aware approach for multi-modal vehicle trajectory prediction. Built upon the backbone architecture of a timewise variational autoencoder, ContextVAE employs a dual attention mechanism for observation encoding that accounts for the environmental context information and the dynamic agents' states in a unified way. By utilizing features extracted from semantic maps during agent state encoding, our approach takes into account both the social features exhibited by agents on the scene and the physical environment constraints to generate map-compliant and socially-aware trajectories. We perform extensive testing on the nuScenes prediction challenge [1], Lyft Level 5 dataset [2] and Waymo Open Motion Dataset [3] to show the effectiveness of our approach and its state-of-the-art performance. In all tested datasets, ContextVAE models are fast to train and provide highquality multi-modal predictions in real-time.