Data to Decisions: An AI-Driven Test Intelligence Platform for Integrated Analysis of Raw Data, Test Reports, and Specification Compliance in Brake System Validation

Abstract

Validation of brake systems is increasing in complexity due to electrification, software-defined architecture, integrated control modules, and higher functional safety requirements. Although physical testing remains the primary source of engineering evidence, interpretation of results including DVP&R/PVP&R compliance verification, anomaly detection, documentation, and milestone decision support continues to rely heavily on manual engineering analysis. This results in extended feedback cycles, inconsistent interpretation across teams, and limited traceability between raw data, reports, and governing specifications. To support engineers with more objective validation processes, there is a growing need for structured, data-driven intelligence that transforms dispersed test artifacts into actionable engineering decisions.This paper presents Data to Decisions, an AI-driven Test Intelligence Platform designed for integrated analysis of raw measurement data, test reports, validation plans, and specification requirements in brake system development. The platform has been applied to foundation brake systems (EPB and hydraulic calipers), brake control modules (IBC/EB100), and related actuation subsystems. It ingests heterogeneous inputs including DVP&R documents, customer specifications, test summaries, deviation logs, parameter files, and build configurations and converts them into structured, traceable validation datasets.A specification centered reasoning framework extracts governing limits, acceptance thresholds, instrumentation requirements, and staged validation criteria directly from source documents. Using natural language processing, rule-based logic, and pattern recognition models, the system evaluates both discrete and continuous data sets over an unlimited range of performance characterization metrics such as leakage, drag torque, piston travel, fatigue life, NVH behavior, structural durability, and actuator performance characteristics. Results are assessed against extracted specification limits to automatically identify compliance gaps, borderline conditions, parameter inconsistencies, and build-specific variations. All findings are traceable to original requirements and test evidence.The platform further enables closed-loop validation by linking physical test outcomes with virtual analysis results, supporting correlation studies and identifying opportunities for test optimization or targeted retesting. Automated generation of engineering and management level summaries reduces documentation effort while improving consistency and auditability.Pilot deployments demonstrate reduced manual data review effort, improved traceability of specification compliance decisions, enhanced anomaly detection, and faster decision making during and after DV and PV milestones. By combining rule-based validation logic with AI and Generative AI for document interpretation, pattern recognition, and automated summarization, the platform supports engineers in efficiently navigating large volumes of test data and specifications. This paper presents the system architecture, compliance evaluation methodology, and deployment results, illustrating how AI-enabled test intelligence can serve as a practical decision-support layer in modern brake system validation workflows.