Alternative Sensor System and MLP Neural Network for Vehicle Pedal Activity Estimation
Electronics Department, Polytechnics, University of Alcalá, Campus Universitario s/n, 28871 Alcalá de Henares, Madrid, Spain
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Sensors 2010, 10(4), 3798-3814; https://doi.org/10.3390/s100403798
Received: 19 January 2010
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Revised: 21 February 2010
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Accepted: 22 March 2010
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Published: 14 April 2010
(This article belongs to the Special Issue Instrumentation, Signal Treatment and Uncertainty Estimation in Sensors)
It is accepted that the activity of the vehicle pedals (i.e., throttle, brake, clutch) reflects the driver’s behavior, which is at least partially related to the fuel consumption and vehicle pollutant emissions. This paper presents a solution to estimate the driver activity regardless of the type, model, and year of fabrication of the vehicle. The solution is based on an alternative sensor system (regime engine, vehicle speed, frontal inclination and linear acceleration) that reflects the activity of the pedals in an indirect way, to estimate that activity by means of a multilayer perceptron neural network with a single hidden layer.
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Keywords:
brake estimator; clutch estimator; throttle estimator; pedals activity estimation; multilayer perceptron; ANN estimator; on-board sensorial system
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MDPI and ACS Style
Wefky, A.M.; Espinosa, F.; Jiménez, J.A.; Santiso, E.; Rodríguez, J.M.; Fernández, A.J. Alternative Sensor System and MLP Neural Network for Vehicle Pedal Activity Estimation. Sensors 2010, 10, 3798-3814. https://doi.org/10.3390/s100403798
AMA Style
Wefky AM, Espinosa F, Jiménez JA, Santiso E, Rodríguez JM, Fernández AJ. Alternative Sensor System and MLP Neural Network for Vehicle Pedal Activity Estimation. Sensors. 2010; 10(4):3798-3814. https://doi.org/10.3390/s100403798
Chicago/Turabian StyleWefky, Ahmed M., Felipe Espinosa, José A. Jiménez, Enrique Santiso, José M. Rodríguez, and Alfredo J. Fernández. 2010. "Alternative Sensor System and MLP Neural Network for Vehicle Pedal Activity Estimation" Sensors 10, no. 4: 3798-3814. https://doi.org/10.3390/s100403798
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