Eco-driving assistance system for a manual transmission bus based on machine learning

Hongjie Ma, Hui Xie, David Brown

Research output: Contribution to journalArticlepeer-review

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Abstract

Driving Assistance Systems (DAS) is a key technology to improve fuel-economy for in-use vehicles. This also reduces the operational cost of running a fleet of these vehicles. In this paper we develop a novel white box evaluation model using machine learning based on previous research about consumption sensitivity to driving style. Using the proposed evaluation model, an algorithm for Learning Path Planning (LPP) for a driving style is also proposed. The LPP method plans a step-by-step shortest learning path for different driving styles to achieve eco-driving while increasing the driver's acceptance and adaptation of DAS. Simulation results based on vehicle and engine physical models show that the proposed evaluation model, a pure data model, can be used as an alternative to physical model for the eco-driving prompt strategy. The results of the verification show that the proposed strategy can progressively guide the driver to improve the fuel consumption by 6.25% with minimal changes to driver’s driving task and driving style.
Original languageEnglish
Pages (from-to)572-581
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Volume19
Issue number2
DOIs
Publication statusPublished - 14 Dec 2017

Keywords

  • eco-driving
  • driving style
  • driver evaluation
  • driving assistance system
  • decision tree

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