  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.
