AI-Driven Architectures for Real-Time Decision-Making in Autonomous Vehicles

In this paper, we design, develop, and evaluate an AI-based architecture to enhance the intelligence and decisionmaking capabilities of autonomous vehicles. Autonomous vehicles represent a critical emerging application that relies on network services with stringent requirements such as low latency and high data throughput to enable intelligent and timely decisions. The proposed architecture integrates four key components: 1) AIbased Sensors for gathering and interpreting environmental data; 2) Inference Models and Risk Assessment for predicting potential hazards and making proactive decisions; 3) Navigation and Decision-making systems for optimal route planning and control; and 4) Real-Time Data Processing and Event-Driven mechanisms for immediate response to dynamic driving conditions. These components are developed as microservices, allowing for scalable, flexible, and efficient operation. The dynamic interactions among these services ensure that the autonomous vehicle can respond to real-world conditions in real time, resulting in safer and more efficient driving experiences.Our evaluation demonstrates the effectiveness of this architecture in managing complex driving scenarios while maintaining system performance and reliability.