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