Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms
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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/2/784 |
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| author | Carlos H. Espino-Salinas 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 Klinge Orlando Villalba-Condori |
| author_facet | Carlos H. Espino-Salinas 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 Klinge Orlando Villalba-Condori |
| author_sort | Carlos H. Espino-Salinas |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T11:17:41Z |
| format | Article |
| id | doaj.art-d761b72e3b234346a7962da7ba5d41ad |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:17:41Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d761b72e3b234346a7962da7ba5d41ad2023-12-01T00:27:34ZengMDPI AGSensors1424-82202023-01-0123278410.3390/s23020784Driver Identification Using Statistical Features of Motor Activity and Genetic AlgorithmsCarlos H. Espino-Salinas0Huizilopoztli Luna-García1José M. Celaya-Padilla2Jorge A. Morgan-Benita3Cesar Vera-Vasquez4Wilson J. Sarmiento5Carlos E. Galván-Tejada6Jorge I. Galván-Tejada7Hamurabi Gamboa-Rosales8Klinge Orlando Villalba-Condori9Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, MexicoUnidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, MexicoCONACYT, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, MexicoUnidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, MexicoIngeniería Mecanica, Universidad Continental, Arequipa 04002, PeruIngeniería en Multimedia, Universidad Militar de Nueva Granada, Cra 11, Bogotá 101-80, ColombiaUnidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, MexicoUnidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, MexicoUnidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, MexicoVicerrectorado de Investigación, Universidad Católica de Santa María, Arequipa 04002, PeruDriver 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.https://www.mdpi.com/1424-8220/23/2/784driver identificationgenetic algorithmsfeature extractionADASrandom forest |
| spellingShingle | Carlos H. Espino-Salinas 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 Klinge Orlando Villalba-Condori Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms Sensors driver identification genetic algorithms feature extraction ADAS random forest |
| title | Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms |
| title_full | Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms |
| title_fullStr | Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms |
| title_full_unstemmed | Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms |
| title_short | Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms |
| title_sort | driver identification using statistical features of motor activity and genetic algorithms |
| topic | driver identification genetic algorithms feature extraction ADAS random forest |
| url | https://www.mdpi.com/1424-8220/23/2/784 |
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