CAN-Based Model Interface Standardization and Model Integration Framework

With the rapid increase in the complexity of automotive electronic control systems and advancements in software-defined vehicles, the efficient integration of diverse simulation models has become a critical requirement in vehicle development. Virtual testing (VT) and scenario-based validation are now considered essential for safety certification and regulatory compliance. However, current modeling practices are often hampered by inconsistencies in signal definitions, repetitive manual processes, and the inherent complexity of integrating various development environments and tools, which can lead to time-consuming workflows and an increased risk of errors. Engineers typically match controller and plant model signals by hand or rely on limited automation solutions that may lack scalability and robustness for large-scale, collaborative simulation environments. In this study, we propose a CAN-based integration framework that incorporates a standardized interface library and a Python-based automated signal mapping tool. This framework enables seamless alignment and connectivity of signals across multiple platforms, ensuring consistent unit conversion and signal initialization. Practical validation demonstrated a significant reduction in manual effort and increased reusability of configuration data, resulting in more efficient simulation workflows. The framework lays a solid foundation for standardized and collaborative model-based development in automotive R&D. Future work will focus on expanding the coverage of the library and refining signal validation logic to further support faster and more accurate virtual validation for next-generation vehicles.