## 7 Using Vehicles to Augment MEC Capabilities
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Methodology** & **Computing Place** & **Focused Connection** \\ \hline
[68] & Safety management in tele-operated driving & MEC server & V2I \\
[135] & Detecting and solving road hazards & Vehicle, MEC server, & V2I \\  & & cloud server & V2V-V2I \\
[137] & Detecting car-to-pedestrian hazards & User cellphone & V2V-V2I \\
[138] & Detecting vulnerable road users, avoiding car accidents & MEC server, cloud server & V2X \\
[139] & Crash anticipation-assisted steering system & MEC server & V2I \\
[144] & Detecting car-to-pedestrian hazards & MEC server, vehicles, user & V2X \\  & & cellphone & \\
[146] & Assigning processing resource to collision & MEC & V2I \\  & detection system & Vehicle, MEC server & V2V-V2I \\
[147] & Platooning, cooperative adaptive cruise control & MEC server & V2V-V2I \\
[148] & Platooning, optimizing task offloading and & MEC server & V2V-V2I \\  & resource allocation & MEC server & V2V-V2I \\
[149] & Platooning, conserving fuel, and increasing & MEC server & V2I \\  & traffic effectiveness & Vehicle, MEC server, & \\
[150] & Platooning, short delay, and high reliability & cloud server & V2V-V2I \\
[151] & Platooning resource allocation & MEC server & V2I \\
[152] & Platooning, cooperative adapted driving & Vehicle, MEC server, & \\  & & cloud server & V2V-V2I \\
[154] & Platooning, cooperative adaptive cruise control & Vehicle, MEC server & V2V-V2I \\
[155] & Infrastructure-focused platoon management & Vehicle, MEC server & V2V-V2I-V2N \\
[156] & Centralized control of a platoon & MEC server, cloud server & V2I \\
[158] & Generating and controlling behaviors of the & Vehicle, MEC server & V2V-V2I \\  & platooning vehicles & MEC server & V2I \\
[159] & Migration of the platoon controller & MEC server & V2I \\  & Controlling power of task offloading for & MEC server & V2I \\  & vehicular platooning & MEC server & V2I \\
[168] & Tele-operated transport approach & MEC server & V2I \\
[172] & Video streaming & MEC server & V2V-V2I \\
[173] & Video streaming with caching mechanism & MEC server & V2I \\
[175] & Video streaming with bandwidth saving and reduced & MEC server & V2I \\  & data communication times & MEC server & V2I \\
[176] & Video streaming with a buffering method & MEC server & V2I \\
[177] & Video streaming clustered video caching & Vehicle, MEC server & V2V-V2I \\
[179] & Video streaming with hierarchical cooperative & Vehicle, MEC server & V2V-V2I \\  & cache architecture & MEC server & V2V-V2I \\
[180] & MEC cache for 360-degree video streaming & MEC server & V2I \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of recent works on MEC-assisted vehicular applications. several cars, enhancing parallel computing to deliver efficiency and low latency. Furthermore, task offloading is addressed as a decision-making framework, aiming to maximize the overall reward. After identifying the cooperative vehicles, the optimal task partition ratio is subsequently determined. Moreover, the application of a Double Deep Q-network (DDQN) enables the selection of optimal dual actions. Researchers, in [184], developed a framework to enhance collaborative processing in a VN. They used stochastic analysis to assess data transmission likelihood in dynamic V2V and V2I communications, considering factors such as car movement and channel status. They also developed a model for computing tasks in VNs and found optimal CPU scheduling, aiming to minimize application execution time. In this optimization process, they consider the computation reliability by factoring in both the computation capacity and the application requirements. Furthermore, their approach explores the potential of distributing a vehicular application to several processing tasks to compute simultaneously. Furthermore, they utilize the availability of resource-rich facilities such as a cloudlet or a central cloud, or MEC, which may offer significantly stronger processing capabilities than vehicles [185].