Abstract

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. 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. 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. The Virtual Driving Coach’s concept is a promising approach for efficient and environmentally friendly road transport.

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http://link.springer.com/article/10.1007/s12544-015-0174-4/fulltext.html,
http://link.springer.com/content/pdf/10.1007/s12544-015-0174-4,
http://dx.doi.org/10.1007/s12544-015-0174-4 under the license cc-by
http://mediatum.ub.tum.de/doc/1344468/document.pdf,
http://dx.doi.org/10.1007/s12544-015-0174-4 under the license http://creativecommons.org/licenses/by/4.0
https://etrr.springeropen.com/track/pdf/10.1007/s12544-015-0174-4,
https://trid.trb.org/view/1362792,
https://core.ac.uk/display/81614449,
https://paperity.org/p/73638683/the-virtual-driving-coach-design-and-preliminary-testing-of-a-predictive-eco-driving,
https://etrr.springeropen.com/articles/10.1007/s12544-015-0174-4,
http://mediatum.ub.tum.de/node?id=1274033,
https://academic.microsoft.com/#/detail/1854914975
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Published on 01/01/2017

Volume 2017, 2017
DOI: 10.1007/s12544-015-0174-4
Licence: Other

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