# Lane Change Intention Prediction of two distinct Populations using a Transformer

arXiv: https://arxiv.org/abs/2509.06529 · HTML: https://mlsanity.com/paper/2509.06529/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 8 Sep 2025.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Predicting the Time Until a Vehicle Changes the Lane Using LSTM-based Recurrent Neural Networks](https://mlsanity.com/paper/2102.01431/) (2021)
- [Prediction of Lane Change Intentions of Human Drivers using an LSTM, a CNN and a Transformer](https://mlsanity.com/paper/2507.08365/) (2025)

## Abstract (arXiv)

In complex traffic scenarios, intention prediction of surrounding vehicles can improve the strategy of automated driving functions. Existing work on intention prediction is often trained on datasets of single regions or countries. In this article, a transformer network for lane change intention prediction was trained to predict whether target vehicles perform a left lane change, right lane change or keep their lane. Features inputs used were vehicle positions and distances, each for longitudinal and lateral direction. Before being inputted, these features were converted to Frenet coordinates. Two different datasets from leveLXData collected on highways were used: one from German highways and one from Hong Kong highways. Through cross-dataset evaluation, we show that the accuracy values drop to 71.94%, compared to 85.44% when doing dataset-specific training. When training on both datasets, accuracy levels up to 86.84% were achieved.