Edge Computing for Real-Time Decision Making in Autonomous Driving: Review of Challenges, Solutions, and Future Trends

In the coming half-century, autonomous vehicles will share the roads alongside manually operated automobiles, leading to ongoing interactions between the two categories of vehicles. The advancement of autonomous driving systems has raised the importance of real-time decision-making abilities. Edge computing plays a crucial role in satisfying this requirement by bringing computation and data processing closer to the source, reducing delay, and enhancing the overall efficiency of autonomous vehicles. This paper explores the core principles of edge computing, emphasizing its capability to handle data close to its origin. The study focuses on the issues of network reliability, safety, scalability, and resource management. It offers insights into strategies and technology that effectively handle these challenges. Case studies demonstrate practical implementations and highlight the real-world benefits of edge computing in enhancing decision-making processes for autonomous vehicles. Furthermore, the study outlines upcoming trends and examines emerging technologies such as artificial intelligence, 5G connectivity, and innovative edge computing architectures.