Real-Time Defect Detection of Capacitive Touch Pads for Hands-Off Detection in Advanced Driver Assistance Systems

The hands-off detection (HOD) function plays a critical role in accurately identifying driver hand contact in advanced driver assistance systems (ADAS), thereby ensuring system reliability and safety compliance. Capacitive touch pads, which are extensively utilized for this purpose, are prone to various defects arising from their manufacturing process. These defects include pad friction, plating anomalies, pattern deformation, surface scratches, and press gaps. Despite their extensive utilization, a systematic methodology capable of detecting both surface-level and internal microstructural defects remains to be established. The present study proposes a capacitance defect detection algorithm grounded in charge quantity (Q) analysis. A dedicated main control board was developed, integrating signal amplification, analog-to-digital conversion, noise filtering, defect classification logic, and real-time visualization through a graphical user interface (GUI). The system was implemented on an operational automotive production line and validated through the inspection of over 240,000 capacitive touch pads under real-world manufacturing conditions. In this setting, the system successfully identified subtle defects that conventional visual inspection methods failed to detect. The proposed method addresses the limitations of traditional inspection techniques and introduces a structured approach to detecting complex defects in capacitive touch sensors. This research is of practical relevance in industrial settings and contributes a systematic framework for future advancements in HOD system reliability and quality assurance. Subsequent research endeavors will investigate the integration of artificial intelligence (AI) and machine learning techniques to facilitate predictive maintenance and intelligent defect management.