Application of Edge Intelligence and Vehicle-To-Vehicle Communication in Next-Generation Autonomous Driving

Abstract

Self-driving vehicles have been one of the most promising technologies in the 21st century; however, today’s self-driving is way behind optimal autonomous driving where the car can take complete control. This is mainly due to a lack of accuracy and speed of critical car operation decisions made by the electronic control unit from artificial intelligence learning. Using data learning, the accuracy of the calculations is strongly related to the amount of environmental data input; nevertheless, this also proposes that current onboard processors are not powerful enough. This article discusses future solutions such as vehicle-to-vehicle communication and edge intelligence, elaborating on their mechanisms involving artificial intelligence, edge computing, and the internet of vehicles. Moreover, system and mathematic models related to vehicle-to-vehicle communication and edge intelligence are summarized; together with challenges and solutions further in the article. Overall, this article has attempted to provide a theoretical basis for a more profound exploration of deeper autonomous driving techniques and possible ways to achieve so.