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

• URL: http://arxiv.org/abs/2102.01431v2
• Date: Wed, 3 Feb 2021 09:07:53 GMT
• Title: Predicting the Time Until a Vehicle Changes the Lane Using LSTM-based Recurrent Neural Networks
• Authors: Florian Wirthm\"uller, Marvin Klimke, Julian Schlechtriemen, Jochen Hipp and Manfred Reichert
• Abstract summary: This paper deals with the development of a system that accurately predicts the time to the next lane change of surrounding vehicles on highways. An 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.
• Score: 0.5399800035598186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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