Autonomous Vehicle Diagnostics and Support: A Framework for API-Driven Microservices

The rapid evolution of autonomous vehicles (AVs) demands robust and scalable diagnostic and support systems to ensure seamless operations, safety, and performance. This paper presents a framework for API-driven microservices tailored to address the unique diagnostic and support requirements of AVs. Traditional monolithic systems are insufficient for managing the complex, real-time data generated by AVs, necessitating a shift toward microservices architecture. By leveraging API-driven microservices, the proposed framework ensures modularity, scalability, and real-time communication between various vehicle components and external support systems. The framework is designed to support continuous vehicle monitoring, predictive maintenance, fault detection, and real-time decision-making, all while minimizing latency. APIs play a critical role in enabling interaction between different microservices, allowing for seamless data exchange across diverse platforms. This interoperability facilitates integration with third-party services, such as cloud-based diagnostics, real-time traffic information, and software updates. Additionally, the microservices architecture ensures fault isolation, meaning that failures in one service do not compromise the entire system. The framework also emphasizes the importance of cybersecurity and data privacy. Given the sensitive nature of AV data, API security protocols such as OAuth2 and TLS encryption are incorporated to safeguard communication between microservices and external systems. Furthermore, the design is scalable, allowing for the integration of future AV technologies and third-party service enhancements without disrupting existing functionalities. In conclusion, API-driven microservices provide an efficient, flexible, and secure solution for autonomous vehicle diagnostics and support. This framework has the potential to improve AV reliability, reduce downtime, and enhance safety by enabling real-time, data-driven decision-making and proactive maintenance. Future work may explore the integration of machine learning algorithms to further optimize diagnostics and predictive maintenance capabilities. Keywords: Autonomous Vehicles, Microservices, API-Driven Architecture, Diagnostics, Predictive Maintenance, Real-Time Data, Cybersecurity, Vehicle Support Systems, Modularity, Scalability.