# Attention-based Lane Change Prediction

arXiv: https://arxiv.org/abs/1903.01246 · HTML: https://mlsanity.com/paper/1903.01246/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior](https://mlsanity.com/paper/2211.01294/) (2022)
- [Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-based Convolutional Neural Networks](https://mlsanity.com/paper/2109.10742/) (2021)
- [Design Insights and Comparative Evaluation of a Hardware-Based Cooperative Perception Architecture for Lane Change Prediction](https://mlsanity.com/paper/2509.20218/) (2025)
- [Monocular Vision-based Prediction of Cut-in Maneuvers with LSTM Networks](https://mlsanity.com/paper/2203.10707/) (2022)
- [Overtake Detection in Trucks Using CAN Bus Signals: A Comparative Study of Machine Learning Methods](https://mlsanity.com/paper/2507.00593/) (2025)

## Abstract (arXiv)

Lane change prediction of surrounding vehicles is a key building block of path planning. The focus has been on increasing the accuracy of prediction by posing it purely as a function estimation problem at the cost of model understandability. However, the efficacy of any lane change prediction model can be improved when both corner and failure cases are humanly understandable. We propose an attention-based recurrent model to tackle both understandability and prediction quality. We also propose metrics which reflect the discomfort felt by the driver. We show encouraging results on a publicly available dataset and proprietary fleet data.