The Impact of Modality, Technology Suspicion, and NDRT Engagement on the Effectiveness of AV Explanations

• Published in: IEEE access Vol. 11; pp. 81981 - 81994
• Main Authors: Zhang, Qiaoning, Esterwood, Connor, Pradhan, Anuj K., Tilbury, Dawn, Yang, X. Jessie, Robert, Lionel P.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: advanced driver assistance system (ADAS); Anxiety; Anxiety disorders; Automated vehicle (AV); Automation; Driving conditions; explanation; modality; non-driving-related task (NDRT); Safety; Simulator fidelity; System effectiveness; Task analysis; technology suspicion; Technology utilization; Timing; unsafety; Vehicles; Visualization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3302261

Explanations - reasons or justifications for action - are being used to promote the acceptance of automated vehicles (AVs). Yet, it is unclear whether and how the modality of explanation affects its effectiveness. Despite its importance in the technology acceptance literature, the impact of technology suspicion on the adoption of AVs is yet to be fully examined. To expand our understanding of AV explanation, we conducted a within-subjects experiment with 32 participants using a high-fidelity driving simulator. Four conditions were presented to participants: (1) auditory explanation with a non-driving-related task (NDRT), (2) auditory explanation without NDRT, (3) visual explanation with NDRT, and (4) visual explanation without NDRT. The results indicate that auditory explanations are more effective in reducing anxiety and unsafety perception for high-suspicion individuals, especially in the absence of NDRT. Conversely, individuals who are less technology suspicious prefer visual explanations, which can result in lower levels of anxiety and perceived unsafety. The study highlights the importance of considering individuals' technology suspicion and engagement with NDRT when selecting the appropriate explanation modality, and the findings can guide the design of future AV systems to promote effective human-machine interaction.