Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
This addresses the need for safer and more comfortable motion planning in autonomous vehicles, representing a strong specific gain rather than a foundational advancement.
The paper tackles the problem of learning scene-consistent motion forecasts for autonomous vehicles in complex urban traffic by proposing an implicit latent variable model that characterizes the joint distribution over future trajectories, achieving state-of-the-art results in motion forecasting and interaction understanding.
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.