RobustTP uses a non-linear motion model plus instance segmentation to create noisy trajectories, then an LSTM-CNN to predict 5-second future positions of heterogeneous agents in dense traffic, claiming up to 18% ADE and 35.5% FDE gains over prior methods.
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RobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputs
RobustTP uses a non-linear motion model plus instance segmentation to create noisy trajectories, then an LSTM-CNN to predict 5-second future positions of heterogeneous agents in dense traffic, claiming up to 18% ADE and 35.5% FDE gains over prior methods.