Car Interaction Design Lab, College of Arts and Media, Tongji University, Shanghai 201804, China.
Shenzhen Research Institute, Sun Yat-Sen University, Shenzhen 518057, China.
Sensors (Basel). 2022 May 20;22(10):3875. doi: 10.3390/s22103875.
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.
基于透明度理论，本研究在主动交互场景下，通过语音和视觉两种渠道，研究了车内机器人表达适当透明度信息的数量。实验通过驾驶模拟器收集主观和客观数据，测试和评估了车内机器人不同渠道下不同透明度水平和信息组合，重点关注驾驶员在驾驶条件下的安全、可用性、信任和情感维度。结果表明，适当的透明度表达能够提高驾驶员的驾驶控制和主观评价，并且驾驶员在不同类型的任务中需要不同数量的透明度信息。
