Volume 4, Issue 4, December 2013, Pages 612–620
Kamyar Hasanzadeh1 and Hwang Li2
1 Department of Geoinformatics Engineering, Aalto University, Espoo, Finland
2 Department of Civil Engineering, University of Helsinki, Helsinki, Finland
Original language: English
Copyright © 2013 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Ever since automobiles evolved as the dominant transportation mode, road safety emerged as one of the governments' greatest concerns. A number of surveys highlight the fact that unpredictable reaction of drivers is one of the major accident reasons, especially on highways and major roads. Researchers have not made many efforts to tackle this issue, which leaves this a rather untouched problem requiring more research. Intelligent transport systems (ITS) technologies are increasingly being accepted by traffic authorities and people. This paper attempts to offer an ITS solution which can help to learn and predict drivers' behaviors which can be useful for predicting their actions and reactions during driving. This approach consists of three major phases: Learning, Modeling and Predicting. An artificial Neural Network (ANN) has been applied for learning phase and then the learned parameters are utilized in generating a fuzzy model of the driver behavior which can be a basis for the third phase which is prediction. In other words, this research uses a neuro-fuzzy approach to learn, model and predict a driver's behavior. Previously, researches have been conducted in providing safer roads by using intelligent systems and inter-vehicle communication. The aim is to implement this process in personal devices, each located in every car, which are inter-connected.
Author Keywords: ITS, Neural network, Fuzzy systems, Neuro-fuzzy, Driving behavior simulation.
Kamyar Hasanzadeh1 and Hwang Li2
1 Department of Geoinformatics Engineering, Aalto University, Espoo, Finland
2 Department of Civil Engineering, University of Helsinki, Helsinki, Finland
Original language: English
Copyright © 2013 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Ever since automobiles evolved as the dominant transportation mode, road safety emerged as one of the governments' greatest concerns. A number of surveys highlight the fact that unpredictable reaction of drivers is one of the major accident reasons, especially on highways and major roads. Researchers have not made many efforts to tackle this issue, which leaves this a rather untouched problem requiring more research. Intelligent transport systems (ITS) technologies are increasingly being accepted by traffic authorities and people. This paper attempts to offer an ITS solution which can help to learn and predict drivers' behaviors which can be useful for predicting their actions and reactions during driving. This approach consists of three major phases: Learning, Modeling and Predicting. An artificial Neural Network (ANN) has been applied for learning phase and then the learned parameters are utilized in generating a fuzzy model of the driver behavior which can be a basis for the third phase which is prediction. In other words, this research uses a neuro-fuzzy approach to learn, model and predict a driver's behavior. Previously, researches have been conducted in providing safer roads by using intelligent systems and inter-vehicle communication. The aim is to implement this process in personal devices, each located in every car, which are inter-connected.
Author Keywords: ITS, Neural network, Fuzzy systems, Neuro-fuzzy, Driving behavior simulation.
How to Cite this Article
Kamyar Hasanzadeh and Hwang Li, “A Neuro-Fuzzy Application Proposal of an Individual Intelligent Driving Behavior Predictor Device,” International Journal of Innovation and Applied Studies, vol. 4, no. 4, pp. 612–620, December 2013.
