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Conference papers
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.
Cited literature [27 references]
https://hal-centralesupelec.archives-ouvertes.fr/hal-02860895
Contributor : Guillaume Sandou Connect in order to contact the contributor
Submitted on : Monday, June 8, 2020 - 4:32:07 PM
Last modification on : Tuesday, July 20, 2021 - 3:06:27 AM
Contributor : Guillaume Sandou Connect in order to contact the contributor
Submitted on : Monday, June 8, 2020 - 4:32:07 PM
Last modification on : Tuesday, July 20, 2021 - 3:06:27 AM
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