S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction in Autonomous Driving. (arXiv:2206.10902v1 [cs.CV])

To safely and rationally participate in dense and heterogeneous traffic,
autonomous vehicles require to sufficiently analyze the motion patterns of
surrounding traffic-agents and accurately predict their future trajectories.
This is challenging because the trajectories of traffic-agents are not only
influenced by the traffic-agents themselves but also by spatial interaction
with each other. Previous methods usually rely on the sequential step-by-step
processing of Long Short-Term Memory networks (LSTMs) and merely extract the
interactions between spatial neighbors for single type traffic-agents. We …

arxiv autonomous autonomous driving cv driving networks prediction temporal transformer