Effects of transparency and message framing on drivers' subjective perceptions and behaviors during takeover requests: An information design-based perspective

With the advancement of advanced driver assistance systems (ADAS) technology, understanding drivers’ responses to takeover requests is crucial for ensuring safety and operational efficiency when driving with ADAS. However, existing research mainly focused on providing information through different modalities (e.g., visual or audial), while the combined effects of transparency and message framing on drivers' perception and behaviors when faced with takeover requests have not been thoroughly explored from an information design-based perspective. This study aimed to investigate the combined effects of transparency (i.e., transparent vs. not transparent) and message framing (i.e., gain vs. loss) on drivers’ perceptions of designed information and behaviors (takeover time, emotional reaction, and message attitude) during takeover requests. Thus, the experiment employed a 2×2 within-subject design, with 31 participants (i.e., 23 females and 8 males) completing a driving simulator study featuring four distinct message prompts. The results indicated that: (1) The transparency significantly impacts drivers’ takeover performance, with longer takeover time observed when the information is not transparent; (2) The message framing significantly influences drivers’ emotional reactions, in which loss-framed messages evoke stronger emotional reactions compared to gain-framed messages; (3) Both the transparency and message framing significantly influence drivers’ attitudes towards the designed messages. More specifically, transparent messages generally result in more positive attitudes, while gain-framed messages consistently lead to higher message attitudes compared to loss-framed messages. The findings of this study underscore the critical role of transparency and message framing in enhancing the safety of drivers when driving with ADAS, which could be used to guide the design of human-machine interaction interfaces, foster drivers’ acceptance of ADAS, and further calibrate drivers’ mental models of and trust in ADAS.