## 3 Problem Statement  
For ITS safety provision, driver behavior analysis is essential to provide continuous assistance and route planning for drivers. The specific problem statements that were encountered in the detection and classification of driver behavior are as follows. The machine learning models that were used for the accurate detection and classification of driver behavior for minimizing road casualties were proposed in [33; 34]. The driver behavior was described by the acceleration and speed of the vehicle [33]. The environment was modelled using dilated residual networks (DRN) that split the video image into a number of patches for the driver's attention. For stress analysis, extreme gradient boosting (XGBoost) predicted the stress values of the driver into three aspects: high, low, and medium. In [34], various drivers' behavior signals such as gravity, acceleration, throttle, speed, and revolutions per minute (rpm) were used to categorize drivers as normal, aggressive, distracted, drowsy, and drunk driving. A 2D convolutional neural network (CNN) was used to classify driver behavior based on vehicle moving trajectories. For this, the temporal dependencies of driving signals were converted into spatial dependencies. The major problems that are faced in these approaches are:  
1. The visual result of the environment (scenes by images) was considered and processed in DRN, but the weather information was not considered, which causes low accuracy in monitoring the driver's behavior;
2. System errors in modelling driver's stress become very high due to the usage of the XGBoost algorithm, and training time is long compared to the Catboost and light GBM algorithms;
3. Environmental and health status was not considered in this paper, which causes a higher risk of accidents and high economic damage. Lane change deviation is also taken into account for modelling the behavior of drivers;
4. This paper does not discuss the alert messages for the personalized monitoring of drivers during driving. Hence, driver behavior modelling must be investigated using other significant parameters;
5. CNN does not effectively handle all of the image features that are converted from signals, and behavior modelling takes a long amount of time to generate a prediction. This increases the chance of accidents and also does not ensure traffic safety through early alert message transmission. In [35; 36], driver monitoring was utilized to provide personalized assistance. This system was developed for the internet of vehicles (IoV), in which on-board image parameters and wearable sensor parameters were collected to compute the deviation degree of vehicles, as well as the head motion of drivers. Based on the observed parameters, an abnormality level of driver behavior was computed. If the abnormality was high, alert messages were forwarded to the neighboring vehicles and the pedestrians that travelled at the roadside. Frequent alert message transmission avoids accidents and economic damage, and also ensures traffic safety. The cloud environment keeps track of drivers' reports and allows the monitoring of drivers' behavior [36]. The problems in these approaches are presented as follows:  
* Vehicle motion, which measures lane change deviation using image processing techniques, is not a focus. Hence, it does not suit all types of drivers. High acceleration, continuous speeding, frequent lane changes, and large brakes must be considered to monitor the driver's behavior;
* Dangerous drivers' behavior is not classified, including drunk, distracted, and fatigued. Hence, the warning messages are not optimal in this case (i.e., decisions must be properly made and sent to the pedestrians and other roadside vehicles);
* Cloud-based driver behavior monitoring increases latency. It is not effective to monitor a large number of drivers' behaviors to provide real-time warning message updates;* The health (physiological) parameters of the driver are not considered, which are important in emergency situations.