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NARX Network Based Driver Behavior Analysis and Prediction Using Time-series Modeling

Ling Wu1, Haoxue Liu2, Tong Zhu2, Yueqi Hu3

1 School of Vehicle Engineering, Xi'an Aeronautical University, Xi’an, Shaanxi, China
2 Key Laboratory for Automotive Transportation Safety Enhancement Technology of the Ministry of Communication, Chang’an University, Xi’an, Shaanxi, China
3 School of Automobiles, Chang’an University, Xi’an, Shaanxi, China

* Corresponding Author: Ling Wu,

Intelligent Automation & Soft Computing 2018, 24(3), 633-642.


The objective of the current study was to examine how experienced and inexperienced driver behaviour changed (including heart rate and longitudinal speeds) when approaching and exiting highway tunnels. Simultaneously, the NARX neural network was used to predict real-time speed with the heart rate regarded as the input variable. The results indicated that familiarity with the experimental route did decrease drivers’ mental stress but resulted in higher speed. The proposed NARX model could predict synchronous speed with high accuracy. These results of the present study concern how to establish the automated driver model in the simulation environment.


Cite This Article

L. Wu, . H. Liu, . T. Zhu and . Y. Hu, "Narx network based driver behavior analysis and prediction using time-series modeling," Intelligent Automation & Soft Computing, vol. 24, no.3, pp. 633–642, 2018.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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