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

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.
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https://hal-centralesupelec.archives-ouvertes.fr/hal-02860895
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Submitted on : Monday, June 8, 2020 - 4:32:07 PM
Last modification on : Saturday, October 3, 2020 - 4:16:40 AM

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  • HAL Id : hal-02860895, version 1

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Jean Mercat, Thomas Gilles, Nicole Zoghby, Guillaume Sandou, Dominique Beauvois, et al.. Multi-Head Attention for Joint Multi-Modal Vehicle Motion Forecasting. IEEE International Conference on Robotics and Automation, May 2020, Paris, France. ⟨hal-02860895⟩

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