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This research work proposes an innovative approach to categorizing race drivers, diverging from the current FIA regulations. Drivers are assigned a numerical grade on a scale from 1 to 100, where a lower score indicates higher proficiency. This method facilitates a simulation of traditional driver classification, preserving the ‘Pro’ and ‘Am’ categories. Using neural networks implemented in Matlab® Toolbox, the model utilizes 37 driver-specific parameters as input to predict driver grades. Initial results reveal model complexity and a risk of overfitting, suggesting avenues for improvement such as integrating additional car-related parameters or refining driver evaluations.

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Categorization of Professional Race Car Drivers Using Artificial Intelligence Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Luis Isasi-Sánchez,
  • Ignacio Villanueva-Freije

摘要

This research work proposes an innovative approach to categorizing race drivers, diverging from the current FIA regulations. Drivers are assigned a numerical grade on a scale from 1 to 100, where a lower score indicates higher proficiency. This method facilitates a simulation of traditional driver classification, preserving the ‘Pro’ and ‘Am’ categories. Using neural networks implemented in Matlab® Toolbox, the model utilizes 37 driver-specific parameters as input to predict driver grades. Initial results reveal model complexity and a risk of overfitting, suggesting avenues for improvement such as integrating additional car-related parameters or refining driver evaluations.