Transforming Embedded Systems Calibration Through Intelligent Automation Frameworks

Abstract

The escalating complexity of modern automotive embedded systems has rendered conventional manual calibration workflows structurally inadequate. As software-defined vehicle (SDV) platforms proliferate across the automotive industry, the number of calibration parameters managed per vehicle has grown exponentially -- with Euro-6-compliant diesel platforms exceeding 80,000 individual parameters and multi-domain body control systems introducing thousands of additional interdependent values [1]. Manual approaches to parameter extraction, comparison, merging, and traceability are unable to operate reliably at this scale, producing measurable increases in validation rework, release delays, and configuration errors. This paper presents an intelligent calibration automation framework designed to replace manual calibration workflows with governed, scalable, and reproducible engineering processes. The framework uses automated data processing pipelines for ingestion and delta detection‚ structured change management systems for versioned traceability‚ and orchestration systems for cross-platform validation and deployment․ An empirical evaluation against manual calibration baselines shows the framework reduced the calibration processing time by 73%‚ the parameter merge error rates by 68%‚ and provided 100% traceability coverage across platform variants․ With operator-dependent variability removed and work aligned to the CI‚ the framework allows calibration to become a first-class infrastructure capability‚ rather than being engineer-dependent and reactive․ The contribution establishes a replicable architecture applicable to enterprise-scale embedded systems programs and provides a foundation for future extensions incorporating machine learning-based predictive calibration and digital twin synchronization.