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- Generation of Synthetic Data Using AIPublication . MELO, BRUNO OLIVEIRA; Santos, Veríssimo Manuel Brandão LimaThe process of accurately monitoring tire pressure in a vehicle is crucial for safety, tire longevity, and fuel efficiency, is present in modern cars due to regulatory changes. Indirect Tire Pressure Monitoring System (iTPMS), one of the systems that monitors tire pressure, is used by accessing already existent sensor data in the vehicle to determine if there is a Tire Pressure Loss (TPL). Despite it being cost-effective and easier to integrate and maintain, it relies on data acquired in a controlled environment to fine-tune the final production vehicle Tire Pressure Monitoring System (TPMS). This work explores the generation of synthetic data for these tests using Artificial Intelligence (AI) techniques, specifically focusing on Machine Learning (ML) models to simulate realistic sensor data to be used for iTPMS development and therefore increase testing capabilities while significantly reducing costs involved in current development, providing a scalable solution for dataset expansion. The generation process utilizes regression techniques to model complex relations between different vehicle parameters and sensors, including data preprocessing, model training, and a validation phase, ensuring fidelity to real-world scenarios. Additionally, a Graphical User Interface (GUI) solution is provided, bridging the gap between the ease of use, and users without technical knowledge of ML solutions, providing easy-to-read interfaces and straightforward variable entry for synthetic data generation. Both the models and GUI solution are created using MATLAB.