With different cognitive abilities and driving style preferences, car-following behaviors can vary significantly among human drivers. To facilitate the replications of human driving behaviors on chassis dynamometer using a robot driver, this paper proposes a novel fuzzy logic driver model that attempts to perform humanized driving behaviors in the car-following regimes. An adaptive neuro-fuzzy inference system was developed to tune the fuzzy model using real driving data collected from an instrumented vehicle. Driver’s cognition parameters, such as headway distance, vehicle speed and pedal positions, were modelled as system inputs. Meanwhile, driver’s action parameters, such as pedal movements and gear selection, were selected as system outputs. Three models that possess different driving styles were calibrated using the system. Afterwards, in order to evaluate its performance of emulating human behaviors, the established fuzzy models were examined in a simulation scenario that is anchored to standard WLTC drive cycle tests.

Original languageEnglish
Title of host publicationAdvances in Human Aspects of Transportation - Proceedings of the AHFE 2018 International Conference on Human Factors in Transportation, 2018
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319938844
Publication statusPublished - 1 Jan 2019
EventAHFE International Conference on Human Factors in Transportation, 2018 - Orlando, USA United States
Duration: 21 Jul 201825 Jul 2018

Publication series

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


ConferenceAHFE International Conference on Human Factors in Transportation, 2018
Country/TerritoryUSA United States


  • Car-following model
  • Driving style simulation
  • Fuzzy logic
  • Human factors
  • Real driving data
  • WLTC

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)


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