Multi-Head Attention for Multi-Modal Joint 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.

Learning Lane Graph Representations for Motion Forecasting

“…[9] uses the distance along the centerlines and offset from the centerlines as input to their nearest neighbours regression and LSTM [20] models. [34,1] use 1D CNN and LSTM to encode lane features. In contrast, our model constructs a lane graph from vectorized map data, and extracts multi-scale topology features using the proposed LaneGCN.…”