Implicit Latent Variable Model for Scene-Consistent Motion Forecasting

Left: a localization error makes the SDV follow a wrong route when using an HD map, driving into traffic. Right: mapless driving can interpret the scene from sensors and achieve a safe plan that follows a high-level command.


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.

In European Conference on Computer Vision 2020