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In recent decades, the evolution and advancement of automotive technology have played a crucial role in enhancing the safety and security of drivers and passengers. One prominent technology that significantly contributes to vehicle safety is the Anti-lock Braking System (ABS), which notably improves safety during braking, reduces braking distances, and enhances vehicle control across various road surface conditions. This paper introduces an innovative real-time algorithm aimed at improving the performance of ABS systems, leveraging practical data instead of complex mathematical modeling. Unlike traditional modeling approaches that rely on half car model and dynamic tire simulations, this study utilizes actual wheel data to develop and optimize the algorithms. This approach enhances the sensitivity and adaptability of the algorithm to real-world road changes and varying operational conditions. Furthermore, the method effectively addresses and analyzes the phenomena of Jump and Split, which have not been specifically tackled in other studies. Validated through both analytical and empirical models, this approach provides high accuracy in estimating acceleration in scenarios where accelerometer sensors are unavailable. It offers precise responses under varying braking conditions. Simulation results of the algorithm using practical test data indicate that it achieves approximately 49.1% faster detection times compared to other algorithms, offering significant advantages such as reduced braking distances and lower computational costs.

Keywords: Anti-Lock Braking, Surface Detection, Realtime Algorithm, practical Data, Two-Level Testing

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