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Volume 23
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10.3390/s23020784
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Article

Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms

by
Carlos H. Espino-Salinas
1,†,
Huizilopoztli Luna-García
1,*,
José M. Celaya-Padilla
2,
Jorge A. Morgan-Benita
1,†,
Cesar Vera-Vasquez
3,
Wilson J. Sarmiento
4,
Carlos E. Galván-Tejada
1,
Jorge I. Galván-Tejada
1,
Hamurabi Gamboa-Rosales
1 and
Klinge Orlando Villalba-Condori
5
1
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico
2
CONACYT, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico
3
Ingeniería Mecanica, Universidad Continental, Arequipa 04002, Peru
4
Ingeniería en Multimedia, Universidad Militar de Nueva Granada, Cra 11, Bogotá 101-80, Colombia
5
Vicerrectorado de Investigación, Universidad Católica de Santa María, Arequipa 04002, Peru
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sensors 2023, 23(2), 784; https://doi.org/10.3390/s23020784
Submission received: 9 December 2022 / Revised: 31 December 2022 / Accepted: 5 January 2023 / Published: 10 January 2023
(This article belongs to the Section Vehicular Sensing)
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Abstract

Driver identification refers to the process whose primary purpose is identifying the person behind the steering wheel using collected information about the driver him/herself. The constant monitoring of drivers through sensors generates great benefits in advanced driver assistance systems (ADAS), to learn more about the behavior of road users. Currently, there are many research works that address the subject in search of creating intelligent models that help to identify vehicle users in an efficient and objective way. However, the different methodologies proposed to create these models are based on data generated from sensors that include different vehicle brands on routes established in real environments, which, although they provide very important information for different purposes, in the case of driver identification, there may be a certain degree of bias due to the different situations in which the route environment may change. The proposed method seeks to intelligently and objectively select the most outstanding statistical features from motor activity generated in the main elements of the vehicle with genetic algorithms for driver identification, this process being newer than those established by the state-of-the-art. The results obtained from the proposal were an accuracy of 90.74% to identify two drivers and 62% for four, using a Random Forest Classifier (RFC). With this, it can be concluded that a comprehensive selection of features can greatly optimize the identification of drivers.
Keywords: driver identification; genetic algorithms; feature extraction; ADAS; random forest driver identification; genetic algorithms; feature extraction; ADAS; random forest

Share and Cite

MDPI and ACS Style

Espino-Salinas, C.H.; Luna-García, H.; Celaya-Padilla, J.M.; Morgan-Benita, J.A.; Vera-Vasquez, C.; Sarmiento, W.J.; Galván-Tejada, C.E.; Galván-Tejada, J.I.; Gamboa-Rosales, H.; Villalba-Condori, K.O. Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms. Sensors 2023, 23, 784. https://doi.org/10.3390/s23020784

AMA Style

Espino-Salinas CH, Luna-García H, Celaya-Padilla JM, Morgan-Benita JA, Vera-Vasquez C, Sarmiento WJ, Galván-Tejada CE, Galván-Tejada JI, Gamboa-Rosales H, Villalba-Condori KO. Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms. Sensors. 2023; 23(2):784. https://doi.org/10.3390/s23020784

Chicago/Turabian Style

Espino-Salinas, Carlos H., Huizilopoztli Luna-García, José M. Celaya-Padilla, Jorge A. Morgan-Benita, Cesar Vera-Vasquez, Wilson J. Sarmiento, Carlos E. Galván-Tejada, Jorge I. Galván-Tejada, Hamurabi Gamboa-Rosales, and Klinge Orlando Villalba-Condori. 2023. "Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms" Sensors 23, no. 2: 784. https://doi.org/10.3390/s23020784

APA Style

Espino-Salinas, C. H., Luna-García, H., Celaya-Padilla, J. M., Morgan-Benita, J. A., Vera-Vasquez, C., Sarmiento, W. J., Galván-Tejada, C. E., Galván-Tejada, J. I., Gamboa-Rosales, H., & Villalba-Condori, K. O. (2023). Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms. Sensors, 23(2), 784. https://doi.org/10.3390/s23020784

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