TSGN: Temporal Scene Graph Neural Networks with Projected Vectorized
  Representation for Multi-Agent Motion Prediction
Yunong Wu Thomas Gilles Bogdan Stanciulescu Fabien Moutarde
Predicting future motions of nearby agents is essential for an autonomous
vehicle to take safe and effective actions. In this paper, we propose TSGN, a
framework using Temporal Scene Graph Neural Networks with projected vectorized
representations for multi-agent trajectory prediction. Projected vectorized
representation models the traffic scene as a graph which is constructed by a
set of vectors. These vectors represent agents, road network, and their spatial
relative relationships. All relative features under this representation are
both translationand rotation-invariant. Based on this representation, TSGN
captures the spatial-temporal features across agents, road network,
interactions among them, and temporal dependencies of temporal traffic scenes.
TSGN can predict multimodal future trajectories for all agents simultaneously,
plausibly, and accurately. Meanwhile, we propose a Hierarchical Lane
Transformer for capturing interactions between agents and road network, which
filters the surrounding road network and only keeps the most probable lane
segments which could have an impact on the future behavior of the target agent.
Without sacrificing the prediction performance, this greatly reduces the
computational burden. Experiments show TSGN achieves state-of-the-art
performance on the Argoverse motion forecasting benchmar.