Reliable and transparent in-vehicle agents lead to higher behavioral trust in conditionally automated driving systems

Abstract

Trust is critical for human-automation collaboration, especially under safety-critical tasks such as driving. Providing explainable information on how the automation system reaches decisions and predictions can improve system transparency, which is believed to further facilitate driver trust and user evaluation of the automated vehicles. However, what the optimal level of transparency is and how the system communicates it to calibrate drivers’ trust and improve their driving performance remain uncertain. Such uncertainty becomes even more unpredictable given that the system reliability remains dynamic due to current technological limitations. To address this issue in conditionally automated vehicles, a total of 30 participants were recruited in a driving simulator study and assigned to either a low or a high system reliability condition. They experienced two driving scenarios accompanied by two types of in-vehicle agents delivering information with different transparency types: “what”-then-wait (on-demand) and “what + why” (proactive). The on-demand agent provided some information about the upcoming event and delivered more information if prompted by the driver, whereas the proactive agent provided all information at once. Results indicated that the on-demand agent was more habitable, or naturalistic, to drivers and was perceived with faster system response speed compared to the proactive agent. Drivers under the high-reliability condition complied with the takeover request (TOR) more (if the agent was on-demand) and had shorter takeover times (in both agent conditions) compared to those under the low-reliability condition. These findings inspire how the automation system can deliver information to improve system transparency while adapting to system reliability and user evaluation, which further contributes to driver trust calibration and performance correction in future automated vehicles.

Perspective Taking Reflects Beliefs About Partner Sophistication: Modern Computer Partners Versus Basic Computer and Human Partners

“…Recent, rapid progress in the field of AI has led to considerable developments in the design, functionality, and complexity of intelligent systems, which have gone beyond simply providing an automated interface to more advanced behavior, such as multimodal communication (Mazhar, Navarro, Ramdani, Passama, & Cherubini, 2019;Yohanan & MacLean, 2012) or incorporating virtual reality (Yu, Hsueh, Sun, & Liu, 2021). Human user behavior, in turn, is observed to be sensitive to the apparent capability of AI systems, such as the degree of autonomy they exhibit (Brügger, Richter, & Fabrikant, 2019;Taylor, Wang, & Jeon, 2023). Evidence also shows that social robots are now comparable to, and in some cases more effective than, humans in various learning and communicative situations (e.g., Beattie, Edwards, & Edwards, 2020;Edwards, Edwards, Spence, Harris, & Gambino, 2016;Kim et al, 2013).…”