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. We release our code at: https://github.com/Manojbhat09/Trajformer
@article{arxiv.2011.14910,
title = {Trajformer: Trajectory Prediction with Local Self-Attentive Contexts for Autonomous Driving},
author = {Manoj Bhat and Jonathan Francis and Jean Oh},
journal= {arXiv preprint arXiv:2011.14910},
year = {2020}
}
Comments
Accepted: Machine Learning for Autonomous Driving @ NeurIPS 2020