# Predicting the Time Until a Vehicle Changes the Lane Using LSTM-based Recurrent Neural Networks

arXiv: https://arxiv.org/abs/2102.01431 · HTML: https://mlsanity.com/paper/2102.01431/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 2 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [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)
- [An LSTM Network for Highway Trajectory Prediction](https://mlsanity.com/paper/1801.07962/) (2018)
- [Teaching Vehicles to Anticipate: A Systematic Study on Probabilistic Behavior Prediction Using Large Data Sets](https://mlsanity.com/paper/1910.07772/) (2019)
- [Towards Incorporating Contextual Knowledge into the Prediction of Driving Behavior](https://mlsanity.com/paper/2006.08470/) (2020)
- [A Fleet Learning Architecture for Enhanced Behavior Predictions during Challenging External Conditions](https://mlsanity.com/paper/2009.11221/) (2020)

## Built on by (most cited first, 3 of 3)

- [Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-based Convolutional Neural Networks](https://mlsanity.com/paper/2109.10742/) (2021)
- [Predicting highway lane-changing maneuvers: A benchmark analysis of machine and ensemble learning algorithms](https://mlsanity.com/paper/2204.10807/) (2022)
- [Lane Change Intention Prediction of two distinct Populations using a Transformer](https://mlsanity.com/paper/2509.06529/) (2025)

## Abstract (arXiv)

To plan safe and comfortable trajectories for automated vehicles on highways, accurate predictions of traffic situations are needed. So far, a lot of research effort has been spent on detecting lane change maneuvers rather than on estimating the point in time a lane change actually happens. In practice, however, this temporal information might be even more useful. This paper deals with the development of a system that accurately predicts the time to the next lane change of surrounding vehicles on highways using long short-term memory-based recurrent neural networks. An extensive evaluation based on a large real-world data set shows that our approach is able to make reliable predictions, even in the most challenging situations, with a root mean squared error around 0.7 seconds. Already 3.5 seconds prior to lane changes the predictions become highly accurate, showing a median error of less than 0.25 seconds. In summary, this article forms a fundamental step towards downstreamed highly accurate position predictions.