  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
