# Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model
## 7 Conclusions  
This paper proposed a novel methodology wherein the optimal number of sub-models of a PWARX is identified based on consistent variable selection. By leveraging the idea of consistent variable selection, an optimal model which is replicative and robust to a slight change of driving data can be realized. The PWARX model is well suited to describe both the decision-making and motion control facets in analyzing human driving behavior. The human driving behavior generally changes depending on the surrounding environment, driving time due to fatigue, or driver's mood, by using consistent variable selection with the PWARX in modeling a driver's driving behavior, a basal model that is robust to these changes, which in fact can be said to be the true model of that driver, can be identified.  
The proposed model was applied to the car-following driving task resulting in the identification of three regions/modes/sub-models (dangerous, cruising, and safe). The risk feeling index, KdB \(u_{3}\), and range rate \(u_{2}\) Table 4 were found to play important roles in the decision-making to switch the control laws between the motion facets. By using statistical variable selection, the range \(u_{1}\) and time headway \(u_{6}\) Table 4 were found to be the most influential variables in the motion control of the car-following task. In addition, the similarities in form of identifying the decision-making variables and the influential motion dynamics variables among drivers and the differences between them by looking at the magnitudes of the decision-making variables and the differences in variable selection results could be identified.  
Furthermore, the identified model for the car-following driving task was compared to the conventional microscopic driver model, the Gipps driver model, both in real-road driving data prediction and CARLA-ROS based car-following simulation. The comparison shows that the proposed model shows significantly better prediction performance and is able to mimic the real-road driving behavior better when simulated.  
The content of this paper can be summarised as follows. Section 1 introduced the background, motivation, and the related works of this study. The proposed method of this study was introduced and discussed in Section 2, and Section 3 applied the proposed model to the car-following driving task. Finally, Section 6 discussed some of the applications of the proposed method in this paper.  
**Author Contributions:** Conceptualization, all authors; methodology, J.C.N.; formal analysis, J.C.N.; research, all authors; resources, H.O. and T.S.; data curation, J.C.N.; writing--original draft preparation, J.C.N.; writing--review and editing, all authors; supervision, H.O. and T.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethical Committee of Graduate School of Engineering, Nagoya University with approval number of 18-6.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
**Conflicts of Interest:** The authors declare no conflict of interest.