Energetic Impacts Evaluation of Eco-Driving on Mixed Traffic With Driver Behavioral Diversity

Yao Ma, Junmin Wang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This study presents a fuel-economical driving strategy for connected and automated vehicles (CAVs) and investigates its impacts on human-driven platoon's fuel efficiency, considering driver behavioral diversity. In mixed traffic where a limited number of CAVs and many human-driven vehicles share the road, the Eco-Driving strategy of CAVs, accomplished through vehicle connectivity and longitudinal dynamics control, can significantly reduce fuel consumption of CAVs, particularly during transient traffic conditions, by avoiding unnecessary braking and acceleration maneuvers which lead to excessive fuel consumption. This is implemented and validated by a vehicle longitudinal dynamic control design with an instantaneous power-based fuel rate estimation model. Moreover, such an Eco-Driving strategy will also help most of the following human-driven vehicles to improve fuel performance despite a highly diverse and uncertain driving characteristics spectrum. The car-following behaviors of a human-driven platoon are described by a microscopic traffic model with dedicated representations of the individual drivers' preferences. A comprehensive statistical investigation shows the fuel-saving benefits for a human-driven platoon by adopting the proposed Eco-Driving strategy for CAVs. Some special cases are discussed, as well. The results collectively demonstrate the positive impacts of CAVs, even with a low penetration rate, could potentially have on the energy efficiency of the transportation sector within the foreseeable future.

Original languageEnglish
JournalIEEE Transactions on Intelligent Transportation Systems
DOIs
StateAccepted/In press - 2020

Keywords

  • Autonomous vehicles
  • dynamic programming
  • fuel economy
  • human factors
  • statistical analysis.

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