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Multimodal trajectory forecasting based on discrete heat map

arXiv 2106.11467 ↗First posted 22 Jun 2021cs.CVcited by 0

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Abstract (arXiv)

In Argoverse motion forecasting competition, the task is to predict the probabilistic future trajectory distribution for the interested targets in the traffic scene. We use vectorized lane map and 2 s targets' history trajectories as input. Then the model outputs 6 forecasted trajectories with probability for each target.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 3 references that are in the map. First posted on arXiv 22 Jun 2021.

Builds on

3 of 3 references.

References · 3

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2020Learning Lane Graph Representations for Motion Forecastingcited by 382
  3. 2021LaneRCNN: Distributed Representations for Graph-Centric Motion Forecastingcited by 113

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