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

Wirthmueller, Florian and Klimke, Marvin and Schlechtriemen, Julian and Hipp, Jochen and Reichert, Manfred (2021) Predicting the Time Until a Vehicle Changes the Lane Using LSTM-Based Recurrent Neural Networks. IEEE Robotics and Automation Letters, 6 (2). pp. 2357-2364.

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Official URL: https://doi.org/10.1109/LRA.2021.3058930

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.

Item Type:Article
Subjects:DBIS Research > Publications
ID Code:2013
Deposited By: Prof. Dr. Manfred Reichert
BibTex Export:BibTeX
Deposited On:18 May 2021 10:50
Last Modified:18 May 2021 10:50

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