Edge Intelligence for Autonomous Vehicles: Examining the use of edge intelligence to enable autonomous decision-making in vehicles

Abstract

Autonomous vehicles (AVs) are poised to revolutionize transportation by providing safe, efficient, and convenient mobility solutions. However, the realization of fully autonomous driving faces significant challenges, including the need for real-time decision-making capabilities. Edge intelligence, which involves processing data near the source at the network edge, has emerged as a promising solution to enhance the autonomy of vehicles. This paper presents a comprehensive review of edge intelligence techniques for autonomous vehicles, including their applications, challenges, and future directions. We first discuss the concept of edge intelligence and its relevance to AVs. Then, we explore how edge intelligence can enable autonomous decision-making in vehicles by providing real-time data processing and analysis capabilities. Next, we examine the various edge intelligence techniques and algorithms used in AVs, such as edge computing, machine learning, and sensor fusion. We also discuss the benefits of using edge intelligence for AVs, including improved response times, reduced bandwidth requirements, and enhanced privacy and security. Furthermore, we highlight the challenges and open research questions in this field, such as ensuring the reliability and robustness of edge intelligence systems in dynamic environments. Finally, we present future research directions for leveraging edge intelligence to enhance the autonomy of vehicles, including the integration of 5G networks, blockchain technology, and swarm intelligence. Overall, this paper provides a comprehensive overview of edge intelligence for autonomous vehicles and highlights its potential to revolutionize the future of transportation.