Predicting The Time Until A Vehicle Changes The Lane Using Lstmbased Recurrent Neural Networks

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

**arXiv ID:** 2102.01431
**Authors:** Florian Wirthmüller, Marvin Klimke, Julian Schlechtriemen, Jochen Hipp, Manfred Reichert
**Published:** 2021-02-02T11:04:22Z
**Abstract:**
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.

## Skill Description

This skill is generated from the arXiv paper: Predicting the Time Until a Vehicle Changes the Lane Using LSTM-based Recurrent Neural Networks (2102.01431).

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## References

- [arXiv:2102.01431](http://arxiv.org/abs/2102.01431v2)