## 1 Introduction
The common research issues encountered in this area are:  
* Heterogeneous data--To analyze the behavior of drivers, many types of information are needed, including information on drivers' physical states (drowsy, distracted), vehicles' on-board information (accelerating, braking), and environmental information (road conditions, weather conditions). Processing such data dynamically increases complexity. * Uncertainties--in real-life scenarios, there are many uncertainties in the environment that need to be considered in order to design an efficient assistance module. * The proposed driver behavior analysis and personalized assistance model are designed in such a way as to overcome these issues. The major objective of this research work is to design a novel system for detecting driver behavior and providing intelligent assistance. The sub-objectives are listed below,
* To minimize the computational overhead during driver behavior analysis. * To minimize latency in forwarding alert messages. * To minimize transmission delay in offloading and maximize the resource utilization of fog nod
* To minimize the energy consumption of fog nodesThe main focus of this research work is to provide personalized assistance to drivers and to alert drivers of casualties around them as a step to preventing them from harm. The major contributions of this work are listed below. * Vehicles in the road environment are clustered by edge-assisted road side units (E-RSUs) using multi-criterion-based hierarchical correlation clustering (MCB-HCC) [22] in order to reduce the complexity in handling a large number of nodes. The information about these vehicles is processed in an attention-based residual neural network (AttResNet) [23] to classify the vehicles on the basis of mobility. * The driver behavior of the above classified vehicles is analyzed for several drivers, vehicles, and environmental conditions by implementing a refined asynchronous advantage actor critic (A3C) Algorithm. In this process, the driver's current state is analyzed and classified into several classes such as drowsy, distracted, in an emergency, speeding, bad pedaling, and bad steering. * In case of emergency situations, alert messages are disseminated to surrounding vehicles of the vehicle presenting a risk, which is performed via fog nodes using a jellyfish search optimization (JSO) algorithm [24]. An alert message is also disseminated to nearby optimal fog nodes, and in the case that there are no fog nodes, a virtual fog node is created dynamically by using a constraint-based quantum entropy function. * Personalized assistance is provided to the driver on the basis of the analyzed behavior of the driver. Assistance is provided one or more times by utilizing a multi-attribute utility model. This assistance facilitates in preventing road casualties. All instances of assistance are stored in a cloud server for long-term assessment of driver behavior. The rest of the sections in the manuscript are organized as follows. Section 2 shows the literature survey of the previous works which are relatable to the proposed work. Section 3 explains the problem statement which is identified in the previous works. Section 4 represents the proposed work methodology with corresponding mathematical representations, pseudocode and procedure, which is followed by simulation setup, experimental results and comparative analysis between the proposed and existing works. Finally, Section 6 describes the conclusion and future work of the proposed research.