Implicit Latent Variable Model for Scene-Consistent Motion Forecasting

TL;DR

This paper introduces an implicit latent variable model using graph neural networks to generate scene-consistent motion forecasts for autonomous vehicles, improving safety and interaction understanding in urban traffic.

Contribution

It presents a novel scene modeling approach with a joint distribution over trajectories, achieving state-of-the-art motion forecasting results.

Findings

State-of-the-art motion forecasting accuracy.

Trajectories are consistent across traffic participants.

Improved safety and comfort in autonomous vehicle planning.

Abstract

In order to plan a safe maneuver an autonomous vehicle must accurately perceive its environment, and understand the interactions among traffic participants. In this paper, we aim to learn scene-consistent motion forecasts of complex urban traffic directly from sensor data. In particular, we propose to characterize the joint distribution over future trajectories via an implicit latent variable model. We model the scene as an interaction graph and employ powerful graph neural networks to learn a distributed latent representation of the scene. Coupled with a deterministic decoder, we obtain trajectory samples that are consistent across traffic participants, achieving state-of-the-art results in motion forecasting and interaction understanding. Last but not least, we demonstrate that our motion forecasts result in safer and more comfortable motion planning.