DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets

• URL: http://arxiv.org/abs/2108.09640v1
• Date: Sun, 22 Aug 2021 05:27:35 GMT
• Title: DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets
• Authors: Junru Gu, Chen Sun, Hang Zhao
• Abstract summary: We propose an anchor-free and end-to-end trajectory prediction model, named DenseTNT, that directly outputs a set of trajectories from dense goal candidates. DenseTNT achieves state-of-the-art performance, ranking 1st on the Argoverse motion forecasting benchmark and being the 1st place winner of the 2021 Open Motion Prediction Challenge.
• Score: 29.239524389784606
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the stochasticity of human behaviors, predicting the future trajectories of road agents is challenging for autonomous driving. Recently, goal-based multi-trajectory prediction methods are proved to be effective, where they first score over-sampled goal candidates and then select a final set from them. However, these methods usually involve goal predictions based on sparse pre-defined anchors and heuristic goal selection algorithms. In this work, we propose an anchor-free and end-to-end trajectory prediction model, named DenseTNT, that directly outputs a set of trajectories from dense goal candidates. In addition, we introduce an offline optimization-based technique to provide multi-future pseudo-labels for our final online model. Experiments show that DenseTNT achieves state-of-the-art performance, ranking 1st on the Argoverse motion forecasting benchmark and being the 1st place winner of the 2021 Waymo Open Dataset Motion Prediction Challenge.

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