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Autonomous road transport systems: a stakeholder perspective

Mayat, Mohammed (2018) Autonomous road transport systems: a stakeholder perspective. Doctoral thesis, University of Huddersfield.

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Abstract

Society has gripped the concept of road transport and has utilised it for social, personal and economic gain. Amidst the apparent benefits, a number of concerns exist around the dangers, congestion, and monetary loss associated with vehicular transport. To counteract this, the introduction of driverless vehicles is being discussed by manufacturers and the Government. Whilst there are a number of apparent benefits, there is an overwhelming need to consider public perception and acceptance of autonomous vehicles. This research study therefore investigates the aforementioned, analysing and presenting the major issues and concerns related to their uptake.

An interview and focus group based approach was adopted for this research, using the Charmaz (2006) constructivist grounded theory methodology. Interviews were conducted with a range of stakeholders and the results of the study detailed that the environment the vehicle and user operate in presents associated issues influencing perceptions, and that technology acceptance is strongly influenced by levels of Motivation in Intention, Acceptance/Usage and Control. Furthermore, acceptance is perceived differently by various stakeholder groups, each with their individual concerns and speculations. The discussion of the study considers the gathered perception to ascertain how best to introduce autonomous vehicles to the public market, highlighting and satisfying the current implications of doing so. This study highlights the need for further research in this discipline, based on the identification of many knowledge gaps. Further work is discussed and recommended in order to combat the limitations and opportunities identified within this thesis.

Item Type: Thesis (Doctoral)
Subjects: T Technology > T Technology (General)
T Technology > TE Highway engineering. Roads and pavements
Schools: School of Computing and Engineering
Depositing User: Andrew Strike
Date Deposited: 21 Jun 2018 11:28
Last Modified: 14 Aug 2018 13:15
URI: http://eprints.hud.ac.uk/id/eprint/34518

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