The development of smart tires is reshaping the future of mobility, particularly in the context of new CASES mobility requirements. This presentation introduces a simulation-driven approach to tire engineering that combines advanced 3D CAE & FEA modeling with sensor signal analysis and big data workflows. Using Abaqus Explicit, we demonstrate how to overcome legacy modeling limitations, enabling system-level design and real-time performance evaluation. We explore a couple of sensor mounting scenarios and the concept of a Virtual Intelligent Tire Belt with multiple acceleration sensors. These configurations generate extensive datasets across varied load cases, necessitating automated post-processing and efficient data management. Our methodology supports predictive analytics, ADAS algorithm development, and aligns with CASES mobility requirements. By integrating simulation, automation, and data science, this work provides a scalable solution for smart tire R&D and advances engineering innovation in the automotive sector.

• Integration of 3D CAE & FEA modeling with sensor signal analysis for smart tire engineering.
• Exploration of Intelligent Tire Belt equipped with multiple acceleration sensors.
• Automated pre and post-processing and efficient data management for extensive datasets.
• Predictive analytics supporting ADAS algorithm development and CASES mobility requirements.
• Scalable solutions for smart tire R&D through simulation, automation, and data science.