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Trajectory Prediction with Local Self-Attentive Contexts for Autonomous Driving (NeurIPS 2020)

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Trajformer

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Official implementation (PyTorch) of the paper:
Trajformer: Trajectory Prediction with Local Self-Attentive Contexts for Autonomous Driving, 2020 [arXiv] [Accepted to ML4AD NeurIPS 2020]

Effective feature-extraction is critical to models’ contextual understanding, particularly for applications to robotics and autonomous driving, such as multimodal trajectory prediction. However, state-of-the-art generative methods face limitations in representing the scene context, leading to predictions of inadmissible futures. We alleviate these limitations through the use of self-attention, which enables better control over representing the agent’s social context; we propose a local feature-extraction pipeline that produces more salient information downstream, with improved parameter efficiency. We show improvements on standard metrics (minADE, minFDE, DAO, DAC) over various baselines on the Argoverse dataset.

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Updates:

  • After hearing about the feedback for the delay in the code-base publications, we are addressing some concerns.

The root codebase (GPLv2) has been committed to the repository, the encoder will be added into utils next with approved licence.

  • We are updating the code to include transformer encoders

If you find this work useful, please consider citing:

@article{trajformer2020,
  title={Trajformer: Trajectory Prediction with Local Self-Attentive Contexts for Autonomous Driving},
  author={Bhat, Manoj, Francis, Jonathan, Oh, Jean},
  journal={arXiv preprint arXiv:2011.14910},
  year={2020}
}