Integrated Quality Assurance Frameworks for Testing Advanced Automotive Systems in the Era of ADAS and E-Mobility

As the automotive industry rapidly transitions toward intelligent, electrified, and connected mobility, ensuring the safety and performance of advanced vehicle systems has become increasingly complex. Technologies such as Advanced Driver Assistance Systems (ADAS), electric powertrains, and software-defined architectures demand robust quality assurance (QA) frameworks capable of addressing the multi-domain nature of modern vehicle platforms. This paper presents an integrated QA methodology that combines traditional testing protocols with model-based validation, digital twin environments, and real-time system monitoring to comprehensively evaluate the reliability and functionality of next-generation automotive systems. The proposed framework focuses on end-to-end traceability from requirements to validation, encompassing hardware-in-the-loop (HIL), software-in-the-loop (SIL), and over-the-air (OTA) testing strategies. Emphasis is placed on simulating complex scenarios for ADAS, battery thermal management in electric vehicles, and dynamic system updates in connected platforms. Additionally, this study identifies key challenges in aligning QA practices with evolving regulatory landscapes and highlights gaps between compliance benchmarks and operational performance under real-world conditions. By integrating predictive analytics and digital twins, the QA process shifts from reactive to proactive, enabling early fault detection, lifecycle validation, and continuous improvement. The paper concludes by outlining a strategic roadmap for QA professionals and test engineers to adapt to emerging industry requirements. The proposed framework not only supports safety and reliability but also facilitates faster time-to-market, reduced development cost, and enhanced customer trust in advanced vehicle technologies.