Revolutionizing Vehicle Diagnostics by New SOVD Incorporation and Development in Adaptive AUTOSAR

SOVD is a new standard defined by ASAM that can be employed in vehicle diagnostics within new architectures for HPCs supporting multiple operating systems. SOVD is grounded in state-of-the-art IT technologies (HTTP, REST, JSON, OAuth) simplifying client implementation by eliminating the need for an...

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Bibliographic Details
Published in:2024 14th International Conference on Electrical Engineering (ICEENG) pp. 193 - 195
Main Authors: Helmy, Mona, Hekal, Mazen, Emad, Youssef
Format: Conference Proceeding
Language:English
Published: IEEE 21.05.2024
Subjects:
Adaptive systems
Association for Standardization of Automation and Measuring Systems (ASAM)
Hardware
High Performance Computers (HPCs)
Hypertext Transfer Protocol (HTTP)
JavaScript Object Notation (JSON)
Open Authorization (OAuth)
Operating systems
Representational State Transfer (REST)
Scalability
Service Oriented Vehicle Diagnostics (SOVD)
Software
Software architecture
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Summary:SOVD is a new standard defined by ASAM that can be employed in vehicle diagnostics within new architectures for HPCs supporting multiple operating systems. SOVD is grounded in state-of-the-art IT technologies (HTTP, REST, JSON, OAuth) simplifying client implementation by eliminating the need for an automotive-specific stack. SOVD can be used in application scenarios Remote, Proximity, and In-vehicle. In this paper, we present our proposed design and implementation for SOVD, suitable for HPCs running on Adaptive AUTOSAR, along with a client implementation accessing diagnostic data via SOVD. The proposed solution addresses the problems caused by traditional diagnostic techniques, which find it difficult to adjust to the dynamic nature of modern automotive systems, particularly when diagnosing complex software problems. This paper aims to enable a smooth integration of SOVD into automotive diagnostics by presenting a design and implementation framework that is optimized for HPCs running on Adaptive AUTOSAR. This framework offers flexibility and scalability while maintaining compatibility with evolving hardware and software architectures.
DOI:10.1109/ICEENG58856.2024.10566427