Collaborative Mobile Edge Computing in eV2X: A Solution for Low-Cost Driver Assistance Systems

Abstract

Collision avoidance systems are of great importance in advanced driver assistance systems (ADAS) to increase the level of safety. However, as a result of their excessive costs, are not considered as a feature in lower-end vehicles. The concept of vehicular communication has emerged recently and added a new dimension for promoting safety in automotive. In this paper, the opportunities for implementation of the cost-effective ADAS through next generation of vehicular communication are discussed. A conceptual model for a vision-based driver assistance system based on collaborative mobile edge computing (MEC) is proposed. Cloudlets are the edges of the 5G cellular network and are capable of providing MEC solutions to their connected devices. The proposed system monitors the vehicles driving in front of an ego car and notifies the driver upon detecting hazardous conditions caused by other vehicles movements. This system can be employed as a cost-effective driver assistance system and has the potential to reach the mass market and be used in all ranges of vehicles including the lower-end models.