Multi-Head Attention for Multi-Modal Joint Vehicle Motion Forecasting
Jean Mercat, Thomas Gilles, Nicole El Zoghby, Guillaume Sandou, Dominique Beauvois, Guillermo Pita Gil
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 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 rasterize the scene as 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.
BibTeX
@inproceedings{icra2020_multiheadattenti,
title = {Multi-Head Attention for Multi-Modal Joint Vehicle Motion Forecasting},
author = {Jean Mercat and Thomas Gilles and Nicole El Zoghby and Guillaume Sandou and Dominique Beauvois and Guillermo Pita Gil},
booktitle = {ICRA 2020},
year = {2020}
}