Speed Bump Detection Using Accelerometric Features: A Genetic Algorithm Approach
Jose M. Celaya-Padilla,
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,
Hamurabi Gamboa-Rosales
Affiliations
Jose M. Celaya-Padilla
Unidad Académica de Ingeniería Eléctrica, CONACyT—Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro Histórico, 98000 Zacatecas, Mexico
Carlos E. Galván-Tejada
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro Histórico, 98000 Zacatecas, Mexico
F. E. López-Monteagudo
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro Histórico, 98000 Zacatecas, Mexico
O. Alonso-González
Unidad Académica de Ingeniería I, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro Histórico, 98000 Zacatecas, Mexico
Arturo Moreno-Báez
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro Histórico, 98000 Zacatecas, Mexico
Antonio Martínez-Torteya
Departamento de ingeniería, Universidad de Monterrey, Avenida Ignacio Morones Prieto 4500 Pte., Jesús M. Garza, 66238, San Pedro Garza García, Nuevo León, Mexico
Jorge I. Galván-Tejada
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro Histórico, 98000 Zacatecas, Mexico
Jose G. Arceo-Olague
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro Histórico, 98000 Zacatecas, Mexico
Huizilopoztli Luna-García
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro Histórico, 98000 Zacatecas, Mexico
Hamurabi Gamboa-Rosales
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro Histórico, 98000 Zacatecas, Mexico
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.
