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Sensors 2017, 17(6), 1350; doi:10.3390/s17061350

Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques

1
Hyundai Motor Company, Hwaseong-si 18280, Korea
2
Department of Mechanical Engineering, Korea University, Seoul 02841, Korea
3
Department of Control and Instrumentation Engineering, Korea University, Sejong 30019, Korea
*
Authors to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 26 April 2017 / Revised: 1 June 2017 / Accepted: 7 June 2017 / Published: 10 June 2017
(This article belongs to the Special Issue Sensors for Transportation)
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Abstract

Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver’s intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver’s intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN) models, and the augmented information is fed to a support vector machine (SVM) to detect the driver’s intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver’s intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics. View Full-Text
Keywords: advanced driver assistance system (ADAS); lane change; driver’s intention; artificial neural network (ANN); support vector machine (SVM) advanced driver assistance system (ADAS); lane change; driver’s intention; artificial neural network (ANN); support vector machine (SVM)
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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. (CC BY 4.0).

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MDPI and ACS Style

Kim, I.-H.; Bong, J.-H.; Park, J.; Park, S. Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques. Sensors 2017, 17, 1350.

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