Perception and trust in autonomous vehicles post cyber security incidents

Gorine, Adam; Khan, Sana

Abstract

The integration of Autonomous Vehicles (AVs) into modern systems of transportation brings with it a new and transformative era. Central to the successful realisation of this transformation is the public’s trust in these vehicles and their safety, particularly in the aftermath of cyber security breaches. The following research therefore explores the various factors underpinning this trust in the context of cyber security incidents. A dual-methodological approach was used in the study. Quantitative data was gathered from structured questionnaires distributed to and completed by a cohort of 151 participants and qualitative data, from comprehensive semi-structured interviews with AV technology and cyber security experts. Rigorous Structural Equation Modelling of the quantitative data then allowed for the identification of the key factors influencing public trust from the standpoint of the research participants including the perceived safety of AV technology, the severity of cyber security incidents, the historic cyber security track record of companies and the frequency of successful cyber security breaches. The role of government regulations, though also influential, emerged as less so. The qualitative data, processed via thematic analysis, resonated with the findings from the quantitative data. This highlighted the importance of perceived safety, incident severity, regulatory frameworks and corporate legacy in shaping public trust. Whilst cyber incidents no doubt erode trust in AVs, a combination of technological perception, regulatory scaffolding and corporate history critically impacts this. These insights are instrumental for stakeholders, from policymakers to AV manufacturers, in charting the course of AV assimilation successfully in future.

• Journal Article Type: Article
• Acceptance Date: Oct 2, 2024
• Online Publication Date: Oct 18, 2024
• Publication Date: Oct 18, 2024
• Deposit Date: Nov 27, 2024
• Publicly Available Date: Nov 28, 2024
• Journal: American Journal of Computer Science and Technology
• Print ISSN: 2640-0111
• Electronic ISSN: 2640-012X
• Publisher: Science Publishing Group
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Issue: 4
• Pages: 122-138
• DOI: https://doi.org/10.11648/j.ajcst.20240704.11
• Public URL: https://uwe-repository.worktribe.com/output/13364438