# Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model
## 6 Discussion  
The useful applications of the proposed model are summarized as follows:  
* The method presented in this study which is a combination of underlying dynamics identification and statistical model selection is well suited for understanding complex human driving behavior. The methodology proposed here was able to identify the variables responsible for the decision-making of the drivers in the car-following driving task. These variables as discussed in Section 4.1 are KdB (\(u_{3}\)) and range rate (\(u_{2}\)). In other words, the values of these variables decide how the motion dynamics are controlled by the driver or what variables to use for the vehicle's motion control. In addition, the variables range (\(u_{1}\)) and time headway (\(u_{6}\)) were identified using variable selection as the most influential variables in expressing the motion dynamics of the vehicle-following tasks for all the drivers. Furthermore, the model was able to identify the similarities (i.e., the decision-making variables and the influential motion dynamics variables) among the drivers, as well as the differences (i.e., the magnitudes of the decision-making variables and the differences in variable selection results) between drivers. Therefore, the direct application of this study is in designing controllers that can reflect drivers' preferences and characteristics for automated driving. For example, looking at Table 4, to design the control dynamics of the cruising region (mode 3) for driver B, the variable jerk (\(u_{4}\)) must be paid attention  
Figure 11: Profiles from the car-following simulation: (**a**) speed and range profiles from the actual real-road driving data; (**b**) speed and range profiles from the car-following simulation based on the proposed model; (**c**) Speed and range profiles from the car-following simulation based on [PERSON] driver model.  
to, whereas it is not necessary to consider this for driver A. These preferences in form of variable differences and the magnitudes of the parameters can be used for a personalized approach towards designing automated systems and ADAS. In addition, this method can be applied in order to gain knowledge about how the automation system/ADAS cooperate considering the human driver. Furthermore, by introducing flexibility in form of variable selection (i.e., using only the most influential variables) in the model, computational cost can be reduced, thereby facilitating online applications.
* This study presented a novel method for deciding the optimal number of behavioral segments (modes or clusters) from data based on consistent variable selection, thereby making it suitable for application in clustering or segmenting problems, particularly, where structural consistency of the identified clusters is of interest.
* Compared to the [PERSON] driver model, the proposed methodology was better both in prediction performance and in the car-following simulation, thereby, making it very suitable as a microscopic driver model in traffic flow applications, especially where expressing the actual driver behavior or personalized behavior generation is of importance.
* In the design of advanced driving assistance systems (ADAS) for car-following driving task, by using the work presented here, the driving situation (i.e., the mode) such as dangerous region, safe region, or cruising region could be identified either by using the specific explanatory variable's value or by looking at the values of the decision variables (i.e., KdB and range rate). The assistance system can be tuned appropriately based on the identified driving situation, hence enabling a better and more reliable system that is able to complement the human driver efficiently.