Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/31724
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Exploring Personalised Autonomous Vehicles to Influence User Trust
Author(s): Sun, Xu
Li, Jingpeng
Tang, Pinyan
Zhou, Siyuan
Peng, Xiangjun
Li, Hao Nan
Wang, Qingfeng
Keywords: Autonomous vehicle
Driving characteristics
Driving style
Personalisation
Trust
User experience
User study
Human factors
Issue Date: Nov-2020
Date Deposited: 23-Sep-2020
Citation: Sun X, Li J, Tang P, Zhou S, Peng X, Li HN & Wang Q (2020) Exploring Personalised Autonomous Vehicles to Influence User Trust. Cognitive Computation, 12 (6), pp. 1170-1186. https://doi.org/10.1007/s12559-020-09757-x
Abstract: Trust is a major determinant of acceptance of an autonomous vehicle (AV), and a lack of appropriate trust could prevent drivers and society in general from taking advantage of such technology. This paper makes a new attempt to explore the effects of personalised AVs as a novel approach to the cognitive underpinnings of drivers’ trust in AVs. The personalised AV system is able to identify the driving behaviours of users and thus adapt the driving style of the AV accordingly. A prototype of a personalised AV was designed and evaluated in a lab-based experimental study of 36 human drivers, which investigated the impact of the personalised AV on user trust when compared with manual human driving and non-personalised AVs. The findings show that a personalised AV appears to be significantly more reliable through accepting and understanding each driver’s behaviour, which could thereby increase a user’s willingness to trust the system. Furthermore, a personalised AV brings a sense of familiarity by making the system more recognisable and easier for users to estimate the quality of the automated system. Personalisation parameters were also explored and discussed to support the design of AV systems to be more socially acceptable and trustworthy.
DOI Link: 10.1007/s12559-020-09757-x
Rights: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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