This study proposes a quantitative approach for developing SOTIF (Safety of the Intended Functionality) for intelligent vehicles, ensuring the safety of critical system functions by establishing assurance cases. With the rapid advancement of autonomous driving technology, demands for safety in intelligent vehicle systems are increasing. The ISO/PAS 21448 standard (Road vehicles - Safety of the intended functionality), focusing on mitigating unreasonable risk due to hazards resulting from functional insufficiencies of the intended functionality or from reasonably foreseeable misuse by persons, provides a guideline. However, research applying this standard to vehicle safety design is limited. This study aims to bridge this gap by proposing a quantitative approach to conduct system engineering and model analysis to enhance system safety assurance levels. In this paper, we present a quantitative framework, including probabilistic analysis, cause-effect analysis, and genetic algorithm-based automated reasoning, to analyze and evaluate the safety levels of in-vehicle systems. Furthermore, we conduct a case study to verify the advantages of the proposed approach in enhancing the safety assessment and development efficiency of intelligent driving vehicle systems and discuss future improvements and application directions. This study provides an analysis framework based on quantitative approaches for the development of SOTIF in intelligent driving vehicle systems, offering a feasible solution for implementing safety assurance of intelligent vehicle technology in accordance with the ISO/PAS 21448 standard.