Please use this identifier to cite or link to this item: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1636
Title: Speed Bump Detection Using Accelerometric Features: A Genetic Algorithm Approach
Authors: Celaya Padilla, José María
Galván Tejada, Carlos Eric
López Monteagudo, Francisco Eneldo
Alonso González, Omero
Moreno Báez, Arturo
Martínez Torteya, Antonio
Galván Tejada, Jorge
Arceo Olague, José Guadalupe
Luna García, Huizilopoztli
Gamboa Rosales, Hamurabi
Issue Date: 20-Feb-2018
Publisher: MDPI Publishers
Abstract: AmongthecurrentchallengesoftheSmartCity,trafficmanagementandmaintenanceareof utmostimportance. Roadsurfacemonitoringiscurrentlyperformedbyhumans,buttheroadsurface 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.
Description: AmongthecurrentchallengesoftheSmartCity,trafficmanagementandmaintenanceareof utmostimportance. Roadsurfacemonitoringiscurrentlyperformedbyhumans,buttheroadsurface 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.
URI: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1636
https://doi.org/10.48779/pj6f-3h80
ISSN: 14248220
Other Identifiers: info:eu-repo/semantics/publishedVersion
Appears in Collections:*Documentos Académicos*-- M. en Ciencias de la Ing.

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