Alternative Sensor System and MLP Neural Network for Vehicle Pedal Activity Estimation

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.

[1]  William B. Ribbens Understanding automotive electronics , 1988 .

[2]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[3]  B. Carnahan,et al.  A drowsy driver detection system for heavy vehicles , 1998, 17th DASC. AIAA/IEEE/SAE. Digital Avionics Systems Conference. Proceedings (Cat. No.98CH36267).

[4]  Erwin R. Boer,et al.  Toward an Integrated Model of Driver Behavior in Cognitive Architecture , 2001 .

[5]  Martin T. Hagan,et al.  Neural network design , 1995 .

[6]  Wilmar Hernandez,et al.  Improving the Responses of Several Accelerometers Used in a Car Under Performance Tests by Using Kalman Filtering , 2001 .

[7]  Felipe Espinosa,et al.  Electronic Application To Evaluate The Driver’sActivity On The Polluting Emissions OfRoad Traffic , 2009 .

[8]  Inés María Galván,et al.  Multilayer perceptron as inverse model in a ground-based remote sensing temperature retrieval problem , 2008, Eng. Appl. Artif. Intell..

[9]  Alois Ferscha,et al.  Driver Activity Recognition from Sitting Postures , 2007, Mensch & Computer Workshopband.

[10]  P. Raksincharoensak,et al.  Driver behavior modeling based on database of personal mobility driving in urban area , 2008, 2008 International Conference on Control, Automation and Systems.

[11]  Yanbo Huang,et al.  Advances in Artificial Neural Networks - Methodological Development and Application , 2009, Algorithms.

[12]  Per Fahlén,et al.  Estimation of operative temperature in buildings using artificial neural networks , 2006 .

[13]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[14]  Mubarak Shah,et al.  Monitoring head/eye motion for driver alertness with one camera , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[15]  Hüseyin Abut,et al.  Biometric identification using driving behavioral signals , 2004, 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763).

[16]  Kazuya Takeda,et al.  Multimedia data collection of in-car speech communication , 2001, INTERSPEECH.

[17]  Yuh-Min Chen,et al.  Developing a multi-layer reference design retrieval technology for knowledge management in engineering design , 2005, Expert Syst. Appl..

[18]  O. Nelles Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models , 2000 .

[19]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[20]  Francklin Rivas-Echeverria,et al.  Data analysis techniques for neural networks-based virtual sensors , 2007 .

[21]  Kazuya Takeda,et al.  Cepstral Analysis of Driving Behavioral Signals for Driver Identification , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[22]  Kazuya Takeda,et al.  Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification , 2007, Proceedings of the IEEE.

[23]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[24]  Wilmar Hernandez,et al.  A Survey on Optimal Signal Processing Techniques Applied to Improve the Performance of Mechanical Sensors in Automotive Applications , 2007, Sensors (Basel, Switzerland).

[25]  Felipe Espinosa,et al.  Gear Predictor of Manual Transmission Vehicles based on Artificial Neural Network , 2009 .

[26]  Alex Pentland,et al.  Modeling and Prediction of Human Behavior , 1999, Neural Computation.

[27]  Hakim Baha,et al.  A Novel Neural Network-Based Technique for Smart Gas Sensors Operating in a Dynamic Environment , 2009, Sensors.

[28]  T.,et al.  Training Feedforward Networks with the Marquardt Algorithm , 2004 .

[29]  Alex Pentland,et al.  Graphical models for driver behavior recognition in a SmartCar , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[30]  Michael Brost,et al.  Action Recognition and Prediction for Driver Assistance Systems Using Dynamic Belief Networks , 2002, Agent Technologies, Infrastructures, Tools, and Applications for E-Services.

[31]  Kazuya Takeda,et al.  Modeling of individualities in driving through spectral analysis of behavioral signals , 2005, Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005..

[32]  Mukesh Khare,et al.  Artificial neural networks in vehicular pollution modelling , 2006 .

[33]  Mukesh Khare,et al.  Artificial Neural Networks in Vehicular Pollution Modelling (Studies in Computational Intelligence) , 2006 .

[34]  Shun-Feng Su,et al.  Robust support vector regression networks for function approximation with outliers , 2002, IEEE Trans. Neural Networks.