Development of Driver-Behavior Model Based onWOA-RBM Deep Learning Network
Joint Authors
Wang, Yaya
Liu, Junhui
Jia, Yajuan
Source
Journal of Advanced Transportation
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-29
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Human drivers’ behavior, which is very difficult to model, is a very complicated stochastic system.
To characterize a high-accuracy driver behavior model under different roadway geometries, the paper proposes a new algorithm of driver behavior model based on the whale optimization algorithm-restricted Boltzmann machine (WOA-RBM) method.
This method establishes an objective optimization function first, which contains the training of RBM deep learning network based on the real driver behavior data.
Second, the optimal training parameters of the restricted Boltzmann machine (RBM) can be obtained through the whale optimization algorithm.
Finally, the well-trained model can be used to represent the human drivers’ operation effectively.
The MATLAB simulation results showed that the driver model can achieve an accuracy of 90%.
American Psychological Association (APA)
Liu, Junhui& Jia, Yajuan& Wang, Yaya. 2020. Development of Driver-Behavior Model Based onWOA-RBM Deep Learning Network. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1180729
Modern Language Association (MLA)
Liu, Junhui…[et al.]. Development of Driver-Behavior Model Based onWOA-RBM Deep Learning Network. Journal of Advanced Transportation No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1180729
American Medical Association (AMA)
Liu, Junhui& Jia, Yajuan& Wang, Yaya. Development of Driver-Behavior Model Based onWOA-RBM Deep Learning Network. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1180729
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1180729