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arXiv · 2102.01431

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

Florian Wirthmüller·Marvin Klimke·Julian Schlechtriemen·Jochen Hipp·Manfred Reichert
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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.

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Florian Wirthmüller, Marvin Klimke, Julian Schlechtriemen, Jochen Hipp, Manfred Reichert. 2021-02-02. Predicting the Time Until a Vehicle Changes the Lane Using LSTM-based Recurrent Neural Networks. https://doi.org/10.1109/lra.2021.3058930

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