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Real-time lane detection and extreme learning machine based tracking control for intelligent self-driving vehicle

  • Sabir Hossain
  • , Oualid Doukhi
  • , Inseung Lee
  • , Deok jin Lee*
  • *Corresponding author for this work
  • Kunsan National University

Research output: Contribution to conferenceConference paperpeer-review

Abstract

The rising self-driving technological innovations are viewed as brimming with challenges and opportunities because of its tremendous research territory. One of the challenges for the autonomous vehicle is straight and curve line detection to enhance the assistance in the autonomous characteristics. We will use a unique way of detecting a curve line algorithm in the vehicle based on the Kalman filter as well as the parabola equation model to calculate the parameters of the curve lane. For robust stability and performance, we will use an on-line sequential extreme learning machine method. We present our proposed result through the simulation study.

Original languageEnglish
Title of host publicationIntelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 2
EditorsYaxin Bi, Rahul Bhatia, Supriya Kapoor
PublisherSpringer Verlag
Pages41-50
Number of pages10
ISBN (Print)9783030295127
DOIs
StatePublished - 2020
EventIntelligent Systems Conference, IntelliSys 2019 - London, United Kingdom
Duration: 2019.09.52019.09.6

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1038
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceIntelligent Systems Conference, IntelliSys 2019
Country/TerritoryUnited Kingdom
CityLondon
Period19.09.519.09.6

Keywords

  • Image transformation
  • Kalman filter
  • Lane detection
  • OS-ELM
  • Self-driving vehicle

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