# Trajformer: Trajectory Prediction with Local Self-Attentive Contexts for Autonomous Driving

arXiv: https://arxiv.org/abs/2011.14910 · HTML: https://mlsanity.com/paper/2011.14910/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Scene Transformer: A unified architecture for predicting multiple agent trajectories](https://mlsanity.com/paper/2106.08417/) (2021)
- [Facial Expression Recognition with Visual Transformers and Attentional Selective Fusion](https://mlsanity.com/paper/2103.16854/) (2021)
- [JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Driving](https://mlsanity.com/paper/2212.08710/) (2022)
- [Distribution-aware Goal Prediction and Conformant Model-based Planning for Safe Autonomous Driving](https://mlsanity.com/paper/2212.08729/) (2022)
- [Towards Safe Autonomy in Hybrid Traffic: Detecting Unpredictable Abnormal Behaviors of Human Drivers via Information Sharing](https://mlsanity.com/paper/2309.16716/) (2023)
- [CATP: Context-Aware Trajectory Prediction with Competition Symbiosis](https://mlsanity.com/paper/2407.07328/) (2024)

## Abstract (arXiv)

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