## 4 Proposed Work
### Cluster-Based Vehicle Motion Detection
The potentiality of the proposed research work in improving road safety and reducing road accidents is analyzed in an elaborative manner. ### Simulation Setup

The proposed DBA-PA model was experimented on with an integration of simulation tools, that is, a simulation of urban mobility (SUMO) version 0.19.0 which is a traffic simulation based on microscopic, all the information such as number of the lane, position etc.., are simulated using SUMO [46] and of an objective modular network (OMNET++)

Figure 4: Overall Flow of Alert Message Dissemination. version 4.6, in which the prior tool is used as a traffic simulator and the latter tool is used as a network simulator. The SUMO tool is an open-source environment in which any one can implement and run their own algorithms, there are several outputs are generated for every execution of simulation. The main reason of taking SUMO tool is, it extends simulation model to consumption of fuel, and emission of noise model which supports SUMO for dense traffic scenarios. The OMNET++ also an open-source simulation time used for modeling of network which exploits discrete C++ simulator. The OMNET is reusable simulation tool and also construct network in hierarchical form that also allows simulations based on embeddings, the embeddings improve the memory management capability. The experiment was carried out by constructing a VANET network with one cloud node, four fog nodes, four E-RSU nodes, and one hundred vehicles. Table 3 presents the simulation parameters used in the experiment. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Description** \\ \hline \hline \multicolumn{2}{c}{Network parameters} \\ \hline Area of simulation & 2450 * 2450 m \\ Simulation time & 400 s \\ Number of cloud nodes & 1 \\ Number of fog nodes & 4 \\ Number of E-RSUs & 4 \\ Number of vehicles & 100 \\ Mobility model & Random way point \\ Range of transmission & 210–260 m \\ Type of traffic & Traffic control interface model \\ Total number of packets & 8000 (approx.) \\ Size of packet & 512 bytes \\ Transport protocol & TCP \\ Rate of transmission & 250 Mbps \\ \hline \hline \multicolumn{2}{c}{Algorithm Parameters} \\ \hline \multicolumn{2}{c}{MCB-HCC} \\ \hline \(j\) & 20 \\ \(\lambda\) & 20 \\ \(\gamma\) & 0.85 \\ \(\Delta\) & 0.05 \\ \hline \multicolumn{2}{c}{AttResNet} \\ \hline \(\mathbb{N}\) & 0.04 \\ Optimizer & Adam \\ \(\beta_{1}\) & 0.91 \\ \(\beta_{2}\) & 0.993 \\ \(\varepsilon\) & \(10^{-8}\) \\ Learning rate & 0.0001 \\ Drop rate & 0.2 \\ Batch size & 32 \\ \hline \multicolumn{2}{c}{A3C} \\ \hline Discount factor & 0.97 \\ Optimizer & Stochastic gradient descent \\ Buffer size & 5 \(\times\) 10\({}^{4}\) \\ Learning rate & 1 \(\times\) 10\({}^{-4}\) \\ Batch size & 32 \\ Soft update factor & 1 \(\times\) 10\({}^{-3}\) \\ \hline \multicolumn{2}{c}{JSO} \\ \hline \(n_{\texttt{\tiny D}}\) & 100 \\ \(max_{iter}\) & 10,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation Parameters. ### Comparative Analysis

In this sub-section, the proposed DBA-PA model is evaluated through an extensive comparison of the proposed model with other existing approaches in terms of motion prediction error, number of alerts, number of risk maneuvers, vehicle-motion-detection accuracy, driver-behavior-detection accuracy, assistance accuracy, latency, false alarm rate, safety score, and efficiency of alert message dissemination. #### 5.2.1 Impact of Motion Prediction Error

The motion prediction error refers to the error rate that emerges in predicting the motion of moving vehicles. Figure 5 is a comparison of the motion prediction error of the proposed DBA-PA model and DMPC [2] and TIP [35]. The motion prediction error increased as the number of moving vehicles increased. The motion prediction accuracy of the proposed model was high due to the implementation of MCB-HCC, in which the vehicles are clustered by the E-RSUs, which reduces the complexity in computation of the motion prediction process carried out by the AttResNet.