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arXiv · 1910.03650

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

Jean Mercat·Thomas Gilles·Nicole El Zoghby·Guillaume Sandou·Dominique Beauvois·Guillermo Pita Gil
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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 any 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.

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Jean Mercat, Thomas Gilles, Nicole El Zoghby, Guillaume Sandou, Dominique Beauvois, Guillermo Pita Gil. 2019-10-08. Multi-Head Attention for Multi-Modal Joint Vehicle Motion Forecasting. https://arxiv.org/abs/1910.03650

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