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

TL;DR

ContextVAE is a novel, real-time, multi-modal vehicle trajectory prediction model that uses a dual attention mechanism to incorporate environmental and social context, achieving state-of-the-art results on multiple datasets.

Contribution

The paper introduces ContextVAE, a context-aware timewise variational autoencoder with dual attention for improved multi-modal vehicle trajectory prediction.

Findings

Achieves state-of-the-art performance on nuScenes, Lyft, and Waymo datasets.

Provides high-quality, real-time multi-modal predictions.

Models are fast to train and map-compliant.

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 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.