## 2 Related Work  
In this section, the literature survey regarding the detection and classification of driver behavior in IoCV is presented. The research gap in designing an effective driver behavior analysis is analyzed. Using driving behavior detection to reduce the occurrence of traffic accidents was proposed in [21]. Driving behavior analysis is conducted based on the serial feature network (SF-NET) and smart phone inertial sensor, which consider the continuity of driving events and utilize multitime data to analyze the driving status. Data are collected from GPS data, acceleration, and gyroscope sensors in smartphones. The input vector not only consists of the current sensor information, but also the relevant information for the adjacent time. In the serial feature network (SF-NET), 10 different behaviors of drivers are identified, which uses multitime and multilevel features information. The SF-NET model must be simplified to reduce the overhead in driver behavior analysis because it is difficult to analyze the multiple features in a single model. Using vehicle motion prediction for driver behavior monitoring and design was proposed in [22]. Attention span is incorporated in this paper for deriving the relative importance of nearby vehicles with respect to the future moving direction. The weighted sum model is added for fusing the related motion patterns to the surrounding vehicles. By incorporating the historical information of vehicle motion, the future trajectories of vehicles are computed. The vehicle motion is different depending on the number of intersections, vehicle densities, and traffic signals. The consideration of historical information does not reflect higher accuracy in driver behavior detection. The prediction of driving distraction based on a hybrid deep learning model was proposed in [23]. Visual analysis of the driver is monitored continuously by a camera, for which CNN is presented. A hybrid CNN is presented for behavior detection. For driver behavior extraction, three different deep learning models are used, including ResNet50, Inception V3, and XCeption, and then the extracted features are applied in the hybrid CNN. In this final softmax layer, anomalies in driver behavior are assessed to manage safe driving habits. The set of driver behaviors classified by the hybrid CNN are safe driving, right-handed phone use, left-handed texting, left-handed phone use, operating the radio, drinking, glancing behind, hair and makeup, and talking to the passengers. However, this paper only considers visual images when the camera is placed in a different location, and it is not effective in handling driver behavior continuously. Image analysis-based driver status monitoring using lightweight deep learning algorithms was proposed in [24]. In the first step, the driver's facial behavior is analyzed to analyze the time series information for driver status behavior analysis. In the second step, the driver's face is continuously tracked and PydMobileNet is used for facial analysis-based driver behavior detection. However, visual analysis can predict driver behavior using facial emotions and actions, although vehicle wheel movement and lane change deviation must be computed to manage the risk values of a particular vehicle. Otherwise, the ITS system is affected by roadside loss, and accidents to pedestrians and nearby vehicles, that are caused by the abnormal driver. An approach for the risk assessment of drivers in near risk conditions was proposed in [25]. The data about vehicle crashes and driver behavior was observed to guarantee safety, which avoids emergency events in ITS. A real-world dataset was used, which collected driver, vehicle, and road information for driver behavior modeling. A modified version of rough set theory was used--namely, variable precision rough set--which makes decisions based on real-world information. The mutual information entropy function was used to find the significant road attributes and to determine the risk values.