## 6 Using MEC as Enabler for Vehicular Applications
### Collision Avoidance
Specifically, a crash-anticipation-assisted steering system based on MEC is created. When delay restrictions are necessary (e.g., in an emergency situation), these messages should be sent as soon as possible and the edge must be used. By the utilization of edge computing nodes, system processes are brought near to end users and finally to the network's edge. A Docker container operated by the Kubernetes orchestrator serves as the controller for the MEC and cloud server. In order to simulatethe proposed mechanism, they utilize standard PCs to simulate the RSU units running in Docker [139] containers controlled by Kubernetes [140] and, for the vehicle traffic, the SUMO simulator is used [141]. The results of the proportion of car crashes demonstrate that using the MEC methodology considerably reduces the number of accidents because of shorter delays. The architecture of the suggested approach is presented in Figure 10.  
Work [142] identifies two paramount issues in MEC-assisted vehicular networks. The first problem is figuring out how to create connection protocols for inter-city traffic scenarios that adequately consider the impact of dynamic traffic flow. Slot demand and duplication will result from the conventional fixed slot-partitioning approaches, which are recommended to handle dynamic traffic situations, particularly in the context of imbalanced traffic. Using the traffic data correctly to control the slot assignment procedure is crucial to addressing this issue. Efficient interaction among RSUs is the second most complex issue. The authors comprehensively evaluate how to offer delay-sensitive broadcast services by taking full advantage of the Vehicle-to-RSU (V2R) connection and inter-RSU collaboration together with the MEC servers. They develop the Collision Prediction and Avoidance MAC (CPA-MAC) protocol (distributed TDMA-based MAC). A new approach is presented in [143]. The proposed architecture consists of three main components, users, Points of Access (POAs), and a collision detection algorithm [144] that runs in MEC servers. The latter is a trajectory-based collision detection system. End users should continuously transmit Basic Safety Messages (BSMs). BSMs are broadcast and users run their own collision detection algorithms. Cars listen to broadcasts, while VRUs are more prone to rely solely on the collision detector's alerts. The driver can be warned by the vehicle's alert system or receive an automatic emergency stop. Cellphones can interrupt any ongoing activity, such as music playback, by alerting their users with any mixture of sound, vibration, and on-screen notice. The MEC concept gives a great deal of versatility in terms of collision detector setup and number of detectors. Figure 10: Crash-anticipation-assisted system based on the MEC framework [138]. A method that utilizes dynamically changing MEC resources to avoid car collisions is presented in [145]. The goal of this approach is to better optimize all MEC assets and ensure the service's optimal results (i.e., shortest delay). This is obtained by employing Deep Learning (DL) to anticipate vehicles' movement and correspondingly allocate processing resources to the car collision identification application. The suggested scheme estimates the movement of the cars using DL, and more specifically Long Short-term Memory (LSTM), from which it obtains the required MEC capabilities. MEC resources are increased or decreased depending on the number of cars involved and the DL module based on LSTM uses a real data set for training and finally anticipates the vehicle trajectory.