The Virtual Driving Coach - design and preliminary testing of a predictive eco-driving assistance system for heavy-duty vehicles

Daniel Heyes, Thomas J. Daun, Andreas Zimmermann, Markus Lienkamp

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Purpose: The commercial vehicle sector is characterized by high competitive pressure. Fuel consumption is one major factor that influences the transport efficiency and competitiveness of logistics companies. Therefore, an eco-driving assistance system (EDAS) is developed in order to support the driver in sustainably maintaining an efficient driving style—the Virtual Driving Coach (ViDCo). In this paper, we describe the design and development process of ViDCo as well as results of the first steps of evaluation and preliminary testing. Methods: An EDAS is developed that uses knowledge of infrastructure based on digital maps in order to proactively and predictively provide the driver with driving advice. The system’s algorithms are structured within the modules “situation detection”, “driving error detection”, and “message filtering and prioritization”. The evaluation of ViDCo comprises preliminary field-testing on public roads as well as a driving simulator experiment. Results: Driving tests show that the Virtual Driving Coach is capable of enhancing fuel efficiency for commercial vehicles in real-world scenarios. The results of the driving simulator experiment indicate a positive level of user acceptance and system safety. Furthermore, the results point towards a positive correlation between user acceptance and the subjects’ judgment of learning. Conclusions: The Virtual Driving Coach’s concept is a promising approach for efficient and environmentally friendly road transport.

Original languageEnglish
Article number25
JournalEuropean Transport Research Review
Volume7
Issue number3
DOIs
StatePublished - 27 Sep 2015

Keywords

  • Driver assistance system
  • Driving simulator experiment
  • Eco-driving
  • Fuel efficiency
  • Judgment of learning
  • Prediction
  • System safety
  • User acceptance

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