## 6 Using MEC as Enabler for Vehicular Applications
### Collision Avoidance  
Road Traffic Accidents (RTAs) continue to be a major source of fatalities and harm worldwide, generating a huge cost in terms of deaths and hospitalization. According to the World Health Organization (WHO), the lives of almost 1.3 million people are negatively affected by the consequences of road accidents every year. To prevent accidents, safety-related information might be relayed to nearby vehicles or to the edge infrastructure. The edge processors are placed adjacent to moving vehicles by the RSUs (MEC servers). They also enable the receipt and analysis of sensor information in near real time [11]. Cellular BSs that offer wireless access to moving cars within their coverage are taken into consideration in [134]. In a VEC system, each BS connects to a dedicated MEC host, enabling cooperative collision avoidance (CAV) operations. These MEC hosts are linked to a centralized cloud DC through a network backbone, ensuring a constant flow of updated data. Communication between MEC servers and the cloud is facilitated by a central broker service. Each vehicle is equipped with processors, interfaces, sensors, and cellular User Equipment (UE) for detecting road hazards. Interactions between the CAV system and vehicles use standard-compliant messages such as Cooperative Awareness Messages (CAMs) and Decentralized Environmental Notification Messages (DENMs) through Vehicle-to-Network (V2N) and ITS-G5 communication protocols. With continuous contact with the MEC server, vehicles can have access to up-to-date data. The CAV system incorporates a web interface module (through HTTP) that enables visualization of cars and danger areas on OpenStreetMap [135], establishing the predefined distance for the accident risk zone of each vehicle and keeping the database up to date. To increase the safety of Vulnerable Road Users (VRUs), researchers provide solutions for Car-to-Pedestrian (C2P) communication with MEC assistance in [136]. They rely on proven Car-to-Car (C2C) communication strategies and expand this idea to include pedestrians in the framework. The problem with this approach is that all computation and communication must occur on a cellphone that the user is carrying. Since specialized movement identification algorithms must be run and the findings must be combined into suitable Collision Detection Algorithms (CDAs), this raises issues with energy consumption and delay. To address these restrictions, authors suggested an offloading approach that relies on MEC and leverages a machine learning system for real-time pedestrian movement detection. All CAMs transmitted between cars and passengers utilize cellular communication as a substitute for Device-to-Device (D2D) methods and are therefore carried via an eNB (and the LTE core network). In the collision warning service, the operation involves multiple stages. As a vehicle or a person with a cellphone moves along the road, it regularly sends Cooperative Awareness Messages (CAMs) containing Context Information (Ctx-Info) to other User Equipment (UE) through the LTE interface, notifying them of its presence. Other UEs will then receive these CAMs in the same context. Then, UEs carry out a local CDA to determine the potential of collisions with other UEs and, if necessary, initiate collision identification procedures. In ref. [137], scientists describe a hybrid architecture for V2X services that uses MEC and cloud-based capabilities jointly to enhance the system's end-to-end functionality. The proposed VRU-Safe service is designed to quickly detect and prevent potential collisions between VRUs and nearby vehicles. Simultaneously, it leverages the advantages of a novel 5G design, and it is capable of dynamically steering processing queries toward the MEC or cloud infrastructure, forwarding effective V2X action procedures, and reducing the latency for critical situations. A collision avoidance technique for automotive scenarios based on the edge computing paradigm has been presented in [138].