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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 12/2019

29.01.2019 | Original Article

A fast decision making method for mandatory lane change using kernel extreme learning machine

verfasst von: Senlin Cheng, Yang Xu, Ruixue Zong, Chuanhai Wang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 12/2019

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Abstract

Lane change maneuver on the highway is a complicated process. A quick and accurate decision for the maneuver is very important for a safe driving. This paper proposes a K-ELM (kernel extreme learning machine) based decision making method for mandatory lane changes. In this method, multiple driving variables that are essential for an accurate lane change are extracted and used as the inputs of an established K-ELM network to generate the right lane-changing decision. The K-ELM network is trained using a tenfold cross-validating approach with the vehicle trajectory data from the NGSIM (next generation simulation) data set on U.S. Highway 101 and Interstate 80. Simulation results demonstrate that the proposed method can generate the lane-changing decision with a 92.86% accuracy for merge events and a 94.36% accuracy for non-merge events. Compared with both the ELM and the SVM method, the proposed method is more accurate and faster in decision making.
Vorheriger Artikel A novel decomposition-ensemble approach to crude oil price forecasting with evolution clustering and combined model
Nächster Artikel Sensitive time series prediction using extreme learning machine

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Metadaten
Titel
A fast decision making method for mandatory lane change using kernel extreme learning machine
verfasst von
Senlin Cheng
Yang Xu
Ruixue Zong
Chuanhai Wang
Publikationsdatum
29.01.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 12/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-019-00923-8

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