Multi-Head Attention for Multi-Modal Joint Vehicle Motion Forecasting.

May 31, 2020·
Jean Mercat
,
Nicole El Zoghby
,
Guillaume Sandou
,
Dominique Beauvois
,
Guillermo Pita-Gil
· 0 min read
Representation of a road scene with Gaussian mixture forecasts.
Abstract
This paper presents a novel vehicle motion forecasting method based on multi-head attention. It produces joint forecasts for all vehicles on a road scene as sequences of multi-modal probability density functions of their positions. Its architecture uses multi-head attention to account for complete interactions between all vehicles, and long short-term memory layers for encoding and forecasting. It relies solely on vehicle position tracks, does not need maneuver definitions, and does not represent the scene with a spatial grid. This allows it to be more versatile than similar model while combining many forecasting capabilities, namely joint forecast with interactions, uncertainty estimation, and multi-modality. The resulting prediction likelihood outperforms state-of-the-art models on the same dataset.
Publication
In International Conference on Robotics and Automation