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Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms.

Carlos H Espino-Salinas1, Huizilopoztli Luna-García1, José M Celaya-Padilla2

  • 1Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico.

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Summary
This summary is machine-generated.

This study introduces a novel method using genetic algorithms for driver identification, achieving up to 90.74% accuracy. Feature selection is key to optimizing driver identification in advanced driver assistance systems (ADAS).

Keywords:
ADASdriver identificationfeature extractiongenetic algorithmsrandom forest

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Area of Science:

  • Automotive Engineering
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Driver identification is crucial for advanced driver assistance systems (ADAS) to understand road user behavior.
  • Current methods often rely on sensor data from real-world driving, potentially introducing bias due to environmental variations.
  • Developing efficient and objective driver identification models remains an active research area.

Purpose of the Study:

  • To develop a novel method for intelligent and objective driver identification.
  • To overcome potential biases in existing driver identification methodologies.
  • To optimize driver identification by selecting salient statistical features from vehicle motor activity.

Main Methods:

  • Utilized genetic algorithms for the intelligent selection of statistical features from vehicle motor activity.
  • Employed a Random Forest Classifier (RFC) for driver identification.
  • Focused on motor activity data generated from main vehicle elements.

Main Results:

  • Achieved 90.74% accuracy in identifying two distinct drivers.
  • Attained 62% accuracy when identifying four drivers.
  • Demonstrated that comprehensive feature selection significantly enhances driver identification performance.

Conclusions:

  • A comprehensive selection of statistical features using genetic algorithms can greatly optimize driver identification.
  • The proposed method offers a newer approach compared to existing state-of-the-art techniques.
  • The findings highlight the importance of robust feature selection for accurate driver identification in ADAS.