Exploring Personalised Autonomous Vehicles to Influence User Trust

Xu Sun, Jingpeng Li, Pinyan Tang, Siyuan Zhou, Xiangjun Peng, Hao Nan Li, Qingfeng Wang

Research output: Journal PublicationArticlepeer-review

18 Citations (Scopus)
1 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)1170-1186
Number of pages17
JournalCognitive Computation
Volume12
Issue number6
DOIs
Publication statusPublished - Nov 2020

Keywords

  • Autonomous vehicle
  • Driving characteristics
  • Driving style
  • Human factors
  • Personalisation
  • Trust
  • User experience
  • User study

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Cognitive Neuroscience

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