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

doi:10.3390/s18020443

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

Research output: Contribution to journal › Article › peer-review

71 Citations (Scopus)

Abstract

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.

Original language	English
Article number	443
Journal	Sensors (Switzerland)
Volume	18
Issue number	2
DOIs	https://doi.org/10.3390/s18020443
Publication status	Published - 3 Feb 2018

Bibliographical note

Funding Information:
Jose M. Celaya-Padilla want to thank the CONACyT, for the support under grant “CONACyT Cátedra 129 –Convocatoria 2016”. The authors also thank Joyce Lozano Aguilar, Javier Saldivar Pérez, Jose Alfredo Montes Arellano and Avelina Sánchez Ortiz for the support of the data collection.

Funding Information:
Acknowledgments: Jose M. Celaya-Padilla want to thank the CONACyT, for the support under grant “CONACyT Cátedra 129 –Convocatoria 2016”. The authors also thank Joyce Lozano Aguilar, Javier Saldivar Pérez, Jose Alfredo Montes Arellano and Avelina Sánchez Ortiz for the support of the data collection.

Publisher Copyright:
© 2018 by the authors. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

Analytical Chemistry
Atomic and Molecular Physics, and Optics
Biochemistry
Instrumentation
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/s18020443

Cite this

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. (2018). Speed bump detection using accelerometric features: A genetic algorithm approach. Sensors (Switzerland), 18(2), Article 443. https://doi.org/10.3390/s18020443

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abstract = "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.",

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note = "Funding Information: Jose M. Celaya-Padilla want to thank the CONACyT, for the support under grant “CONACyT C{\'a}tedra 129 –Convocatoria 2016”. The authors also thank Joyce Lozano Aguilar, Javier Saldivar P{\'e}rez, Jose Alfredo Montes Arellano and Avelina S{\'a}nchez Ortiz for the support of the data collection. Funding Information: Acknowledgments: Jose M. Celaya-Padilla want to thank the CONACyT, for the support under grant “CONACyT C{\'a}tedra 129 –Convocatoria 2016”. The authors also thank Joyce Lozano Aguilar, Javier Saldivar P{\'e}rez, Jose Alfredo Montes Arellano and Avelina S{\'a}nchez Ortiz for the support of the data collection. Publisher Copyright: {\textcopyright} 2018 by the authors. Licensee MDPI, Basel, Switzerland.",

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AU - Alonso-González, O.

AU - Moreno-Báez, Arturo

AU - Martínez-Torteya, Antonio

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AU - Arceo-Olague, Jose G.

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N2 - 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.

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Speed bump detection using accelerometric features: A genetic algorithm approach

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

UN SDGs

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