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Article

Speed Bump Detection Using Accelerometric Features: A Genetic Algorithm Approach

1
Unidad Académica de Ingeniería Eléctrica, CONACyT—Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro Histórico, 98000 Zacatecas, Mexico
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Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro Histórico, 98000 Zacatecas, Mexico
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Unidad Académica de Ingeniería I, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro Histórico, 98000 Zacatecas, Mexico
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Departamento de ingeniería, Universidad de Monterrey, Avenida Ignacio Morones Prieto 4500 Pte., Jesús M. Garza, 66238, San Pedro Garza García, Nuevo León, Mexico
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(2), 443; https://doi.org/10.3390/s18020443
Received: 10 November 2017 / Revised: 5 January 2018 / Accepted: 5 January 2018 / Published: 3 February 2018
(This article belongs to the Special Issue Smart Vehicular Mobile Sensing)
Among the current challenges of the Smart City, traffic management and maintenance are of utmost importance. Road surface monitoring is currently performed by humans, but the road surface condition is one of the main indicators of road quality, and it may drastically affect fuel consumption and the safety of both drivers and pedestrians. Abnormalities in the road, such as manholes and potholes, can cause accidents when not identified by the drivers. Furthermore, human-induced abnormalities, such as speed bumps, could also cause accidents. In addition, while said obstacles ought to be signalized according to specific road regulation, they are not always correctly labeled. Therefore, we developed a novel method for the detection of road abnormalities (i.e., speed bumps). This method makes use of a gyro, an accelerometer, and a GPS sensor mounted in a car. After having the vehicle cruise through several streets, data is retrieved from the sensors. Then, using a cross-validation strategy, a genetic algorithm is used to find a logistic model that accurately detects road abnormalities. The proposed model had an accuracy of 0.9714 in a blind evaluation, with a false positive rate smaller than 0.018, and an area under the receiver operating characteristic curve of 0.9784. This methodology has the potential to detect speed bumps in quasi real-time conditions, and can be used to construct a real-time surface monitoring system. View Full-Text
Keywords: smart car; surface monitoring; speed bump detection smart car; surface monitoring; speed bump detection
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MDPI and ACS Style

Celaya-Padilla, J.M.; Galván-Tejada, C.E.; López-Monteagudo, F.E.; Alonso-González, O.; Moreno-Báez, A.; Martínez-Torteya, A.; Galván-Tejada, J.I.; Arceo-Olague, J.G.; Luna-García, H.; Gamboa-Rosales, H. Speed Bump Detection Using Accelerometric Features: A Genetic Algorithm Approach. Sensors 2018, 18, 443. https://doi.org/10.3390/s18020443

AMA Style

Celaya-Padilla JM, Galván-Tejada CE, López-Monteagudo FE, Alonso-González O, Moreno-Báez A, Martínez-Torteya A, Galván-Tejada JI, Arceo-Olague JG, Luna-García H, Gamboa-Rosales H. Speed Bump Detection Using Accelerometric Features: A Genetic Algorithm Approach. Sensors. 2018; 18(2):443. https://doi.org/10.3390/s18020443

Chicago/Turabian Style

Celaya-Padilla, Jose M., Carlos E. Galván-Tejada, F. E. López-Monteagudo, O. Alonso-González, Arturo Moreno-Báez, Antonio Martínez-Torteya, Jorge I. Galván-Tejada, Jose G. Arceo-Olague, Huizilopoztli Luna-García, and Hamurabi Gamboa-Rosales. 2018. "Speed Bump Detection Using Accelerometric Features: A Genetic Algorithm Approach" Sensors 18, no. 2: 443. https://doi.org/10.3390/s18020443

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