## 3 Problem Statement  
Driver behavior analysis and vehicle motion prediction are the major aspects of road safety provisioning. Many research works have been focused on road safety and accident prevention for Internet of connected vehicle (IoCV) environments. However, the existing work focus on either behavior analysis or motion prediction for accident detection. In particular, driver behavior is analyzed only by detecting vehicle parameters and road  
information. Recommendations to the driver based on their behavior are not provided accurately, and there are more delays. Additionally, the following research problems in driver behavior analysis are encountered. * High false-alarm rate: Due to limited consideration of parameters from the driver and surroundings, the prediction of accidents is not accurate and leads to high traffic congestion in both urban and rural areas and re-routing of nearby vehicles. For accurate driver-behavior detection, driver health parameters (pulse rate, EEG rate, respiration rate, and heart rate), vehicle-motion-detection parameters (speed, longitudinal acceleration, yaw angle rate, gyroscope, and magnetometer), and surrounding factors (traffic density, accidents, weather conditions, and pedestrians) are required. Lack of these important factors affects accuracy. * Lack of optimal assistance for drivers: Recommendations are provided to the driver only on the basis of vehicle motion detection, which is not sufficient enough to assist drivers. The state of driving is not analyzed to give recommendations to drivers. To overcome this, driver states such as drowsiness, distraction, and emergency should be detected. * High latency: In the existing works, latency is observed in two fields. Computing tasks in a cloud environment increases time consumption and thereby increases the probability of accidents occurring. Even when computations are carried out in an edge server, the higher number of vehicles in a real-time scenario makes it time consuming. A clustering method for grouping vehicles on the basis of behavior was proposed in [40]. Integrating a deep learning algorithm with clusters, the authors proposed a location prediction algorithm to localize vehicles in the future. Roads are considered as small portions to easily locate vehicles; hence, long-term predictability is possible. Stacked auto-encoding is used to predict the location of vehicles, which are mapped to the road. The result of a simulation showed that the proposed method is accurate in prediction. It can help vehicles select better routes and reduce traffic congestion. The major problems faced in this research work were:  
* Clustering can be performed effectively, however, if the number of samples increases, scaling issues will occur. The stacked auto-encoder may work perfectly with a training set but fail miserably when samples run out. It is more sensitive to input errors different from those in the training set, which could cause massive errors in an auto encoder, and it might have to be retrained from scratch. It focuses only on the quantity of information rather than the quality. Research solutions: The efficiency of the clustering can be improved by using multi-criterion-based hierarchical correlation clustering (MCB-HCC). AttResNet is used to focus on the quality of the information and to avoid retraining the network. The authors in [41] proposed an offloading method for carrying out the computations of connected vehicles at an ultra-low latency by deploying multi-access edge computing servers along with road side units (RSU's). The selection of MEC servers by fast moving vehicles is carried out by a deep Q learning algorithm, which will solve the frequent handover problems. The centralized management of the vehicular network is taken care of by a software-defined network. The result of a simulation showed that the proposed method has better performance and adaptation than the older offloading methods. The issues faced in this paper were as follows.