<p>The Autonomous Vehicle (AV) industry, commonly called the self-driving car sector, epitomizes a rapidly advancing field at the confluence of technology, engineering, and transportation. This industry is dedicated to the research, development, manufacturing, and deployment of vehicles capable of navigating autonomously without human intervention. Quality control is of critical importance within the AV sector, given the intricate technologies involved and the significant safety implications of potential failures. A robust quality control framework is essential for organizations to safeguard their reputations, particularly in a marketplace that is acutely sensitive to safety concerns. This article focuses on a double sampling plan, which facilitates a more nuanced approach in determining whether to accept or reject a lot based on sampling inspections. When process outcomes follow a Poisson distribution, the occurrence of defects is a rare event which enhance product quality; furthermore, the integration of Markovian models significantly augments to refine this quality. In addition, Bayesian Networks (BNs) provide a powerful framework for augmenting quality control processes through probabilistic reasoning, effective management of uncertainty, and clear visual representation of interrelationships among attributes. Key tables and figures are meticulously developed, accompanied by relevant illustrations applicable to the AV industries are provided.</p>