Modeling Driver Behavior in Car-Following Interactions with Automated and Human-Driven Vehicles and Energy Efficiency Evaluation

Mehmet Fatih Ozkan, Yao Ma

Research output: Contribution to journalArticlepeer-review

Abstract

Human drivers can have diverse car-following behaviors when interacting with connected and automated vehicles (CAVs) and other human-driven vehicles in mixed traffic where many human-driven vehicles and a limited number of CAVs frequently interact and share the road. In this study, Inverse Reinforcement Learning (IRL) is used to model unique car-following behaviors of different human drivers when interacting with the CAV and another human-driven vehicle by using their driving demonstrations collected from in-field driving tests. The learned driver behavior model is shown that the personalized driving behaviors accurately and consistently can be characterized when following the different types of preceding vehicles in a variety of traffic situations. Furthermore, the energy efficiency of different human-driven vehicles when interacting with the CAV and the human-driven vehicle is investigated with the heterogeneous characteristics of drivers' behaviors, considering driving behaviors have significant influences on vehicle fuel economy. A detailed analysis reveals the significant fuel-saving benefits of the CAV to the following human-driven vehicles during the car-following scenario and the extent of such benefits varies among tested human drivers owing to their intrinsic preferences and perception of CAV. These findings suggest that human-CAV interactions can be effectively leveraged to improve the energy efficiency of mixed traffic.

Original languageEnglish
Article number9411849
Pages (from-to)64696-64707
Number of pages12
JournalIEEE Access
Volume9
DOIs
StatePublished - 2021

Keywords

  • Connected and automated vehicles
  • driver behavior modeling
  • fuel economy
  • inverse reinforcement learning

Fingerprint Dive into the research topics of 'Modeling Driver Behavior in Car-Following Interactions with Automated and Human-Driven Vehicles and Energy Efficiency Evaluation'. Together they form a unique fingerprint.

Cite this