# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 1 Introduction  
The increasing number of vehicles in the world raises the issue of traffic and road safety. Enhancing road safety and reducing accident numbers is now a high priority for all governments. The lack of concentration of drivers on the road or driver distraction are the major causes of these accidents. Despite the fact that the driving task requires continuous attention to be paid to the road and the surrounding vehicles, drivers are usually distracted by other activities such as answering a phone call, looking at a billboard, chatting, eating, talking with passengers and radio calibration. In addition, distraction may be the result of a stressful situation that induces an abnormal driving behavior. Stressful situations are now very common and are due to impaired driving, traffic jam, excessive workload, personal problems, etc. Distraction situations due to stress can be reflected in driver behavior, which can be represented by an aggressive driving, a lane departure or hard, repetitive braking. Moreover, the mental and medical states of a driver impact their driving behavior. If a driver is sick or very tired, this may lead to drowsy driving.  
It is estimated that in the USA, 14% of the total number of traffic crashes reported by the police in 2018 was due to a distracted driver, while the number of fatal crashes due to distracted drivers amounted to 8% [1]. It is also important to note that of a total of 2841 reported fatalities with a distracted driver in 2018 by the police in the USA, 61% involved the driver's loss of concentration, 21% involved passengers, 14% involved pedestrians and 3% involved cyclists [1]. This directly shows that distracted drivers pose a major hazard to themselves as well as people in their vicinity. In order to avoid such incidents, efforts have been made to ensure drivers' constant awareness utilizing different approaches and prediction techniques [2]. As a matter of fact, car manufacturers have already implemented different detection techniques that monitor and predict driver behavior in the hope of increasing total vehicle safety [2]. Such systems tend to be costly and encumbering due to the multitude of variables and needed sensors, as well as potential ethical and privacy-related aspects. The magnitude of processed information is introducing new constraintsregarding the system time response and computational power consumption put in place by manufacturers or regulations [3]. Hence, finding the best processing method needed to process all of this information is a major issue.  
In this paper, we investigate an efficient classification method based on the machine learning approach that can be utilized for driver behavior and distraction detection. Therefore, different classification methods are applied on a set of data recorded from real driving tests and compared together in order to define the most efficient among them. The following section presents a summary of the driver distraction factors and detection and classification methods. Section 3 presents the data description and analysis. The proposed classification methods are presented in Section 4. Then, the simulation results and comparison are detailed and discussed in Section 5. Finally, a conclusion and future works are proposed to enhance road safety.