Prediction of driver’s intention of lane change by augmenting sensor information using machine learning techniques

Il Hwan Kim, Jae Hwan Bong, Jooyoung Park, Shinsuk Park

Research output: Contribution to journalArticle

37 Citations (Scopus)

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.

Original languageEnglish
Article number1350
JournalSensors (Switzerland)
Volume17
Issue number6
DOIs
Publication statusPublished - 2017 Jun 10

Keywords

  • Advanced driver assistance system (ADAS)
  • Artificial neural network (ANN)
  • Driver’s intention
  • Lane change
  • Support vector machine (SVM)

ASJC Scopus subject areas

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

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