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 journalArticle

1 Citation (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 languageEnglish
Article number443
JournalSensors (Switzerland)
Volume18
Issue number2
DOIs
Publication statusPublished - 3 Feb 2018

Fingerprint

roads
genetic algorithms
Accidents
Genetic algorithms
abnormalities
ROC Curve
Monitoring
Logistic Models
Maintenance
Sensors
Accelerometers
Safety
Fuel consumption
accidents
Global positioning system
Logistics
Railroad cars
fuel consumption
streets
causes

All Science Journal Classification (ASJC) codes

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

Cite this

Celaya-Padilla, J. M., Galván-Tejada, C. E., López-Monteagudo, F. E., Alonso-González, O., Moreno-Báez, A., Martínez-Torteya, A., ... Gamboa-Rosales, H. (2018). Speed bump detection using accelerometric features: A genetic algorithm approach. Sensors (Switzerland), 18(2), [443]. https://doi.org/10.3390/s18020443
Celaya-Padilla, Jose M. ; Galván-Tejada, Carlos E. ; López-Monteagudo, F. E. ; Alonso-González, O. ; Moreno-Báez, Arturo ; Martínez-Torteya, Antonio ; Galván-Tejada, Jorge I. ; Arceo-Olague, Jose G. ; Luna-García, Huizilopoztli ; Gamboa-Rosales, Hamurabi. / Speed bump detection using accelerometric features: A genetic algorithm approach. In: Sensors (Switzerland). 2018 ; Vol. 18, No. 2.
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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 2018, 'Speed bump detection using accelerometric features: A genetic algorithm approach', Sensors (Switzerland), vol. 18, no. 2, 443. https://doi.org/10.3390/s18020443

Speed bump detection using accelerometric features: A genetic algorithm approach. / Celaya-Padilla, Jose M.; Galván-Tejada, Carlos E.; López-Monteagudo, F. E.; Alonso-González, O.; Moreno-Báez, Arturo; Martínez-Torteya, Antonio; Galván-Tejada, Jorge I.; Arceo-Olague, Jose G.; Luna-García, Huizilopoztli; Gamboa-Rosales, Hamurabi.

In: Sensors (Switzerland), Vol. 18, No. 2, 443, 03.02.2018.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Speed bump detection using accelerometric features: A genetic algorithm approach

AU - Celaya-Padilla, Jose M.

AU - Galván-Tejada, Carlos E.

AU - López-Monteagudo, F. E.

AU - Alonso-González, O.

AU - Moreno-Báez, Arturo

AU - Martínez-Torteya, Antonio

AU - Galván-Tejada, Jorge I.

AU - Arceo-Olague, Jose G.

AU - Luna-García, Huizilopoztli

AU - Gamboa-Rosales, Hamurabi

PY - 2018/2/3

Y1 - 2018/2/3

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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Celaya-Padilla JM, Galván-Tejada CE, López-Monteagudo FE, Alonso-González O, Moreno-Báez A, Martínez-Torteya A et al. Speed bump detection using accelerometric features: A genetic algorithm approach. Sensors (Switzerland). 2018 Feb 3;18(2). 443. https://doi.org/10.3390/s18020443