## 4 Proposed Work
### Cluster-Based Vehicle Motion Detection
Instances of assistance are stored in a cloud server for training purposes. Figure 5:0 Number of Vehicles vs. Safety Scores. Figure 5:1 Number of Emergency Events vs. Dissemination Efficiency. ### Limitation and Discussion

This sub-section describes the performance discussion and limitation of the proposed DBA-PA method. Figures 5-15 illustrates the efficient performance of the proposed DBA-PA method in terms of various performance metrics. Motion prediction error (4.8%) is reduced by efficient clustering using MCB-HCC. Low number of alerts (10) is achieved by performing analysis of driver behavior using A3C Algorithm. A high number of risk maneuvers (24) is attained by efficient personalized assistance. Vehicle motion detection accuracy (95.2%) is increased by efficient clustering by MCB-HCC and vehicle classification using AttResNet by considering multiple criteria such as velocity, location, direction and speed. Increase of driver behavior detection accuracy (95.7%) by classification of driver's current state using A3C Algorithm. Improving the assistance accuracy (95.7%) by providing assistance using multi-attribute utility model. This also increases the safety score (0.75). Latency based on number of vehicles (13 s) and fog nodes (4 s) are reduced by performing generation of assistance and analysis of driver behavior in the fog layer. False alarm rate (6%) is reduced due to accurate detection of driver behavior. Alert message dissemination efficiency (95.7%) is increased by performing efficient dissemination of messages using JSO algorithm. However, this proposed DBA-PA method has addressed security issues during efficient communication between V2V and V2X. Table 4 describes the time complexity analysis for the proposed jelly fish search optimization algorithm and other algorithms which were implemented in the previous works. ## 6 Conclusions and Future Work

In this paper, road safety in the VANET environment is improved in terms of minimizing latency, maximizing availability of resource, reducing computation overhead, and reducing delay during transmission by using the DBA-PA model. Initially, in Layer 1, E-RSUs implement MCB-HCC to form clusters of vehicles which initially computes the correlation distance between the vehicles and form as clusters to reduce the computational overhead in processing driver behavior. Then, the E-RSUs execute AttResNet to predict the motion of vehicles in each cluster by using input features such as vehicular features, environmental features, and physiological features. This information is passed to Layer 2 to analyze driver behavior. The fog node in Layer 2 analyzes driver behavior analysis by using the A3C Algorithm based on several physiological features of drivers, vehicular features, and environmental features of the surrounding environment. Driver behavior is classified into several states for which assistance is generated. If the current state of the driver is found to indicate an emergency, then an alert message is disseminated by the JSO Algorithm, where dissemination is performed for surrounding vehicles within the cluster and in neighboring regions. If a neighboring region has no fog coverage, a virtual fog node is deployed by using a constraint-based quantum entropy function. Personalized assistance is provided to the driver by using the multi-attribute utility model, in which the location characteristics of the vehicle are utilized, and assistance is provided for one or more detected driver states. From the above processes, the proposed work achieves safety ITS environment. The proposed DBA-PA model was experimented on by integrating OMNET++ and SUMO simulation tools; SUMO acts as a traffic simulator, and OMNET++