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Feasibility of Virtual Environments to Develop Future Driving Cycles
Technical Paper
2018-01-1816
ISSN: 0148-7191, e-ISSN: 2688-3627
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Abstract
The current procedure for testing emissions from new vehicles, the World Harmonised Light Vehicle Test Procedure (WLTP), was introduced in September 2017. The WLTP was developed by collecting over 765,000 kilometres worth of data in order to isolate driver behaviour from other real world variables. However, this is a very time consuming and costly process. This paper discusses the suitability of a cheaper and more time efficient alternative.
Driver behaviour has a significant impact on the emissions produced from the same vehicle. This study explores the feasibility of utilising virtual environments as an alternative to real world testing to isolate driver behaviour to develop future drive cycles. The use of virtual environments have some significant advantages over real world testing: they can be strictly controlled in terms of the weather, topography and vehicle characteristics, thereby aiding the isolation of driver behaviour from other variables.
A driving simulator facility based at the University of West of England was used to assess the suitability of determining driver behaviour using a virtual environment. A track was created based on a local route in the virtual environment. The virtual route was driven by volunteers and their driving behaviours were identified. The same route in the real world was driven by the same volunteers. The driving behaviour of the volunteers from both the virtual environment and the real world are compared to assess the realism of the virtual driving experience in terms of driver behaviour. Finally the data from the virtual environment were analysed to determine if driver behaviour can be isolated, along with the impact on vehicle emissions, with a view to using virtual environments to develop future drive test cycles for emissions testing.
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Kay, P., "Feasibility of Virtual Environments to Develop Future Driving Cycles," SAE Technical Paper 2018-01-1816, 2018, https://doi.org/10.4271/2018-01-1816.Data Sets - Support Documents
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