Expectations for Automated Vehicles, 2018-2023
Automated vehicles (AVs) may represent the most profound technological change in road transport since the rise of vehicle mass production, with reductions in energy demand being one of the many anticipated benefits. This project has explored expectations regarding the potential energy-saving benefits of AVs among two groups ‘professionals’ and the general public. The project has used a Delphi study design. The Delphi method offers an exploratory, flexible and iterative technique to obtain insights into what futures might look like when uncertainty is large. This is the case with AVs as much remains unclear if and when fully autonomous vehicles will be introduced on the UK roads and how automation may interact with electrification and a possible shift away from individual ownership towards forms of shared ownership and use. Delphi studies typically consist of several rounds of surveys that are increasingly conducted online in which participants receive feedback between rounds and can adapt their responses and views based on that feedback. Two separate Delphi studies, each consisting of three rounds, were conducted sequentially in 2019-2020. Delphi studies have traditionally been used to build consensus among participants but this often marginalises more radical imaginings of the future and may underappreciate controversies around future developments. This project has, therefore adopted a dissensus-oriented Delphi, which cultivates divergence of views and is particularly appropriate for emergent topics such as the expected effects on transport and energy of vehicle automation. The project was part of the Digital Society theme within the Centre for Energy Demand Solutions (CREDS), which was funded by UK Research and Innovation (grant number: EP/R035288/1)The Centre for Energy Demand Research Solutions (CREDS) was a national Centre for energy demand research in the UK that existed from 2018 until the end of 2023. The Centre's ambition was to lead whole systems work on energy demand in the UK, collaborating with a wider community both at home and internationally. Its research programme was inter-disciplinary and recognised that technical and social change are inter-dependent and co-evolve. It was organised into six themes, one of which considered the impact of digital technologies on energy demand. One project within that team has considered the possible impacts of automated vehicles (AVs) on energy demand. It focused on AVs as these may represent the most profound technological change in road transport since the rise of vehicle mass production, with reductions in energy demand being one of the many anticipated benefits. The project has explored the expectations regarding the potential energy-saving benefits of AVs among two groups -- ‘professionals’ and the general public. The project has used a Delphi study design. The Delphi method offers an exploratory, flexible and iterative technique to obtain insights into what futures might look like when uncertainty is large. This is the case with AVs as much remains unclear if and when fully autonomous vehicles will be introduced on the UK roads and how automation may interact with electrification and a possible shift away from individual ownership towards forms of shared ownership and use. Delphi studies typically consist of several rounds of surveys that are increasingly conducted online in which participants receive feedback between rounds and can adapt their responses and views based on that feedback. Two separate Delphi studies, each consisting of three rounds, were conducted sequentially in 2019-2020. Delphi studies have traditionally been used to build consensus among participants but this often marginalises more radical imaginings of the future and may underappreciate controversies around future developments. This project has, therefore adopted a dissensus-oriented Delphi, which cultivates divergence of views and is particularly appropriate for emergent topics such as the expected effects on transport and energy of vehicle automation.
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Geographic Coverage:
United Kingdom
Temporal Coverage:
2018-04-01/2023-12-31
Resource Type:
dataset
Available in Data Catalogs:
UK Data Service