  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 observation encoding employs a dual attention mechanism
that accounts for the environmental context 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, Lyft Level 5 dataset and Waymo
Open Motion Dataset 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 high-quality multi-modal predictions in real-time.
Our code is available at: https://github.com/xupei0610/ContextVAE.
