<p>Electronic Control Units (ECUs) of modern vehicles are heavily equipped with Advanced Driver Assistance System (ADAS) cameras to enhance road safety by providing reliable traffic sign recognition and driver assistance. However, strong traffic sign identification is not yet achievable in a wide range of real-life situations like night, mist, evening, and rain. The traditional Original Equipment Manufacturer (OEM) validation techniques are relied largely on manual laboratory and on-road validation. They are not automated at high-fidelity ground-truth validation, which reduces their capability of isolating detection failures systematically. To overcome this problem, the current paper suggests an automated validation framework, named Integrated System Performance Evaluation and Correlation Testing (INSPECT), that utilizes a German Traffic Sign Detection Model (GTSDM) as an authoritative ground-truth reference to a real-time assessment of ADAS camera ECU outputs. To perform the GTSDM, YOLOv4 and CSPDarknet53 backbone are used to generate good reference detections to facilitate the analysis of good detections, missed signs, false positives, and misclassifications in detail. A C-ADAS ECU prototype on an NVIDIA Jetson Nano platform was experimentally validated in different conditions, which include day, night, dusk, cloudy, foggy day, and foggy night. Key findings convey that the proposed INSPECT-GTSDM model can provide a dependable and scalable solution to automated real-time validation, as well as enhance the accuracy, robustness, and safety of traffic sign recognition in the ADAS camera ECUs. The framework also offers a viable foundation to regression testing, model continuous enhancement and development of camera-based ADAS technologies.</p>