A Scalable Diagnostics Infrastructure Framework for Multi-Platform Automotive Systems
Keywords:
Automotive Diagnostics, AUTOSAR, Software-Defined Vehicle, Embedded Systems, Diagnostic Orchestration, Fault Detection, Multi-Platform ArchitectureAbstract
Modern automotive embedded systems are defined by software-driven architectures in which diagnostic functions must operate consistently across multiple vehicle programs, electronic control unit (ECU) variants, and continuously evolving software configurations. Conventional diagnostics frameworks, designed for isolated, program-specific deployment, are structurally misaligned with these demands: manual signal interpretation, fragmented fault definitions, and the absence of reuse mechanisms produce inconsistent detection behavior, delayed validation readiness, and compounding engineering effort across vehicle programs. This article presents a scalable diagnostics infrastructure framework that repositions automotive diagnostics from a reactive, per-program engineering task to a governed, reusable, and continuously deployable system-level asset. The proposed framework introduces three interdependent architectural innovations: (1) centralized diagnostic libraries that encode fault semantics, detection logic, and response behavior independent of individual applications; (2) automated signal-to-diagnostic mapping and deployment orchestration that propagates validated diagnostic behavior deterministically across platforms; and (3) an integrated validation and observability layer that provides real-time system visibility and cross-program comparability. Results from multiple General Motors vehicle programs show both time and effort reductions when deploying diagnostics‚ as well as reductions in cross-vehicle diagnostics fault detection mismatch‚ manual diagnostic engineering effort per vehicle program‚ and measurable improvements in ISO 26262-relevant fault detection coverage․These results demonstrate that infrastructure-centric diagnostics architecture delivers scalable gains in reliability, safety assurance, and engineering productivity in software-defined vehicle development environments.
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