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.

Video

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}
}