# TENET: Transformer Encoding Network for Effective Temporal Flow on Motion Prediction

arXiv: https://arxiv.org/abs/2207.00170 · HTML: https://mlsanity.com/paper/2207.00170/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 30 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [On Layer Normalization in the Transformer Architecture](https://mlsanity.com/paper/2002.04745/) (2020)
- [Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles](https://mlsanity.com/paper/1606.07839/) (2016)

## Built on by (most cited first, 14 of 14)

- [Motion Transformer with Global Intention Localization and Local Movement Refinement](https://mlsanity.com/paper/2209.13508/) (2022)
- [MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying](https://mlsanity.com/paper/2306.17770/) (2023)
- [GoRela: Go Relative for Viewpoint-Invariant Motion Forecasting](https://mlsanity.com/paper/2211.02545/) (2022)
- [GANet: Goal Area Network for Motion Forecasting](https://mlsanity.com/paper/2209.09723/) (2022)
- [ProphNet: Efficient Agent-Centric Motion Forecasting with Anchor-Informed Proposals](https://mlsanity.com/paper/2303.12071/) (2023)
- [Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding](https://mlsanity.com/paper/2310.12970/) (2023)
- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [GMMFormer v2: An Uncertainty-aware Framework for Partially Relevant Video Retrieval](https://mlsanity.com/paper/2405.13824/) (2024)
- [Motion Forecasting in Continuous Driving](https://mlsanity.com/paper/2410.06007/) (2024)
- [PBP: Path-based Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2309.03750/) (2023)
- [Hierarchical Light Transformer Ensembles for Multimodal Trajectory Forecasting](https://mlsanity.com/paper/2403.17678/) (2024)
- [FFINet: Future Feedback Interaction Network for Motion Forecasting](https://mlsanity.com/paper/2311.04512/) (2023)
- [Improving Motion Forecasting for Autonomous Driving with the Cycle Consistency Loss](https://mlsanity.com/paper/2211.00149/) (2022)
- [Enhancing Trajectory Prediction through Self-Supervised Waypoint Noise Prediction](https://mlsanity.com/paper/2312.09466/) (2023)

## Abstract (arXiv)

This technical report presents an effective method for motion prediction in autonomous driving. We develop a Transformer-based method for input encoding and trajectory prediction. Besides, we propose the Temporal Flow Header to enhance the trajectory encoding. In the end, an efficient K-means ensemble method is used. Using our Transformer network and ensemble method, we win the first place of Argoverse 2 Motion Forecasting Challenge with the state-of-the-art brier-minFDE score of 1.90.