Design of Proactive Interaction for In-Vehicle Robots Based on Transparency

Abstract

Based on the transparency theory, this study investigates the appropriate amount of transparency information expressed by the in-vehicle robot under two channels of voice and visual in a proactive interaction scenario. The experiments are to test and evaluate different transparency levels and combinations of information in different channels of the in-vehicle robot, based on a driving simulator to collect subjective and objective data, which focuses on users’ safety, usability, trust, and emotion dimensions under driving conditions. The results show that appropriate transparency expression is able to improve drivers’ driving control and subjective evaluation and that drivers need a different amount of transparency information in different types of tasks.

The Effects of Transparency and Reliability of In-Vehicle Intelligent Agents on Driver Perception, Takeover Performance, Workload and Situation Awareness in Conditionally Automated Vehicles

“…An appropriate amount of trust is essential to the effectiveness of the automated system: too much trust will lead to misuse of the system, while lack of trust will result in disuse [24]. Studies have shown that increasing information transparency and system reliability is an effective way to promote trust [7,17,28,29]. The present study examined whether the proactive display of information to support in-depth transparency information would aid in mitigating the impact of the system errors [30].…”

“…Keith et al [16] argued that a proactive system should be representative of the user and able to initiate behavior without user commands. Proactive interaction can help human operators collect and process information in the environment, thereby, reducing the user's information processing burden [17]. In proactive interaction, human operators can achieve more supervisory control rather than active control [18].…”