# Lane Attention: Predicting Vehicles' Moving Trajectories by Learning Their Attention over Lanes

arXiv: https://arxiv.org/abs/1909.13377 · HTML: https://mlsanity.com/paper/1909.13377/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2019.

## Built on by (most cited first, 9 of 9)

- [Multimodal Motion Prediction with Stacked Transformers](https://mlsanity.com/paper/2103.11624/) (2021)
- [LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents](https://mlsanity.com/paper/2104.00249/) (2021)
- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction](https://mlsanity.com/paper/2203.01880/) (2022)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [Diverse Multiple Trajectory Prediction Using a Two-stage Prediction Network Trained with Lane Loss](https://mlsanity.com/paper/2206.08641/) (2022)
- [TAFormer: A Unified Target-Aware Transformer for Video and Motion Joint Prediction in Aerial Scenes](https://mlsanity.com/paper/2403.18238/) (2024)
- [A LiDAR Assisted Control Module with High Precision in Parking Scenarios for Autonomous Driving Vehicle](https://mlsanity.com/paper/2105.00398/) (2021)
- [Jointly Learning Agent and Lane Information for Multimodal Trajectory Prediction](https://mlsanity.com/paper/2111.13350/) (2021)

## Abstract (arXiv)

Accurately forecasting the future movements of surrounding vehicles is essential for safe and efficient operations of autonomous driving cars. This task is difficult because a vehicle's moving trajectory is greatly determined by its driver's intention, which is often hard to estimate. By leveraging attention mechanisms along with long short-term memory (LSTM) networks, this work learns the relation between a driver's intention and the vehicle's changing positions relative to road infrastructures, and uses it to guide the prediction. Different from other state-of-the-art solutions, our work treats the on-road lanes as non-Euclidean structures, unfolds the vehicle's moving history to form a spatio-temporal graph, and uses methods from Graph Neural Networks to solve the problem. Not only is our approach a pioneering attempt in using non-Euclidean methods to process static environmental features around a predicted object, our model also outperforms other state-of-the-art models in several metrics. The practicability and interpretability analysis of the model shows great potential for large-scale deployment in various autonomous driving systems in addition to our own.