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International Journal of Intelligent Transportation Systems Research
Erschienen in:

23.03.2019

A New Lane-Changing Model with Consideration of Driving Style

verfasst von: Guoqing Ren, Yong Zhang, Hao Liu, Ke Zhang, Yongli Hu

Erschienen in: International Journal of Intelligent Transportation Systems Research | Ausgabe 3/2019

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Abstract

The lane-changing model is a hot spot in the field of traffic research, and there are already a lot of free lane-changing model established mathematical statistical methods or machine learning algorithm. However, these models don’t consider the driver’s driving style to the free lane-changing, and the accuracy of these models is low. This paper considers the driver’s driving style and proposes a new free lane-changing model based on machine learning. The new model splits the sample data into three driving styles: cautious, stable and radical. This paper selects the most effective multilayer perceptron model by comparing different machine learning methods based on the NGSIM trajectory data. In the analysis of the final accuracy of this paper, it can be seen that the new model has a great improvement in accuracy.
Vorheriger Artikel Traffic Density Recognition Based on Image Global Texture Feature
Nächster Artikel Stability Analysis of Following Vehicles on a Highway for Safety of Automated Transportation Systems

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Metadaten
Titel
A New Lane-Changing Model with Consideration of Driving Style
verfasst von
Guoqing Ren
Yong Zhang
Hao Liu
Ke Zhang
Yongli Hu
Publikationsdatum
23.03.2019
Verlag
Springer US
Erschienen in
International Journal of Intelligent Transportation Systems Research / Ausgabe 3/2019
Print ISSN: 1348-8503
Elektronische ISSN: 1868-8659
DOI
https://doi.org/10.1007/s13177-019-00180-7

Weitere Artikel der Ausgabe 3/2019

CPS-Based Reliability Enhancement Mechanism for Vehicular Emergency Warning System

Traffic Density Recognition Based on Image Global Texture Feature

Driver Behavior Response to Information Presentation Based on the Emergency Evacuation Procedure of the Great East Japan Earthquake

Real-Time Driver Drowsiness Detection Using Wavelet Transform and Ensemble Logistic Regression

Stability Analysis of Following Vehicles on a Highway for Safety of Automated Transportation Systems

Multi-view Low Rank Representation for Multi-Source Traffic Data Completion

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