Diagnostics of Automotive Service-Oriented Architectures with SOVD

<div class="section abstract"><div class="htmlview paragraph">The term Software-Defined Vehicle (SDV) describes the vision of software-driven automotive development, where new features, such as improved autonomous driving, are added through software updates. Groups like SOAFEE advocate cloud-native approaches – i.e., service-oriented architectures and distributed workloads – in vehicles. However, monitoring and diagnosing such vehicle architectures remain largely unaddressed. ASAM’s SOVD API (ISO 17978) fills this gap by providing a foundation for diagnosing vehicles with service-oriented architectures and connected vehicles based on high-performance computing units (HPCs).</div><div class="htmlview paragraph">For service-oriented architectures, aspects like the execution environment, service orchestration, functionalities, dependencies, and execution times must be diagnosable. Since SDVs depend on cloud services, diagnostic functionality must extend beyond the vehicle to include the cloud for identifying the root cause of a malfunction. Due to SDVs’ dynamic nature, vehicle systems must be monitored as service degradation is more likely than a complete failure. Established monitoring and error analysis approaches for cloud environments cannot easily be transferred to vehicles. Monitored values must be aggregated and correlated to error events before cloud transmission, or suspects must be created in the vehicle for thorough analysis, reducing the data exchanged with the backend.</div><div class="htmlview paragraph">The SOVD API provides a good foundation to diagnose service-oriented architectures and HPCs. While SOVD offers a wide range of diagnostic and monitoring features, it currently lacks solutions for diagnosing certain aspects and especially monitoring of a service-oriented architecture. This paper addresses these gaps, showcasing approaches and techniques to enhance monitoring and diagnostics.</div></div>