Anthropomorphic robots need to maintain effective and emotive communication with humans as automotive agents to establish and maintain effective human–robot performances and positive human experiences. Previous research has shown that the characteristics of robot communication positively affect human–robot interaction outcomes such as usability, trust, workload, and performance. In this study, we investigated the characteristics of transparency and anthropomorphism in robotic dual-channel communication, encompassing the voice channel (low or high, increasing the amount of information provided by textual information) and the visual channel (low or high, increasing the amount of information provided by expressive information). The results showed the benefits and limitations of increasing the transparency and anthropomorphism, demonstrating the significance of the careful implementation of transparency methods. The limitations and future directions are discussed.

With the continuous development of intelligent product interaction technology, the facial expression design of virtual images on the interactive interface of intelligent products has become an important research topic. Based on the current research on facial expression design of existing intelligent products, we symmetrically mapped the PAD (pleasure–arousal–dominance) emotion value to the image design, explored the characteristics of abstract expressions and the principles of expression design, and evaluated them experimentally. In this study, the experiment of PAD scores was conducted on the emotion expression design of abstract expressions, and the data results were analyzed to iterate the expression design. The experimental results show that PAD values can effectively guide designers in expression design. Meanwhile, the efficiency and recognition accuracy of human communication with abstract expression design can be improved by facial auxiliary elements and eyebrows.

In case of dangerous driving, the in-vehicle robot can provide multimodal warnings to help the driver correct the wrong operation, so the impact of the warning signal itself on driving safety needs to be reduced. This study investigates the design of multimodal warnings for in-vehicle robots under driving safety warning scenarios. Based on transparency theory, this study addressed the content and timing of visual and auditory modality warning outputs and discussed the effects of different robot speech and facial expressions on driving safety. Two rounds of experiments were conducted on a driving simulator to collect vehicle data, subjective data, and behavioral data. The results showed that driving safety and workload were optimal when the robot was designed to use negative expressions for the visual modality during the comprehension (SAT 2) phase and speech at a rate of 345 words/minute for the auditory modality during the comprehension (SAT 2) and prediction (SAT 3) phases. The design guideline obtained from the study provides a reference for the interaction design of driver assistance systems with robots as the interface.

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.

In this paper, the development process and validation of a self-assessment emotion tool (SAET) is described, which establishes an emotion-assessment method to improve pictorial expression design. The tool is based on an emotion set of emotional-cognition-derived rules obtained from an OCC model proposed by Ortony, Clore, and Collins, and the emotion set and expression design are validated by numerical computation of the dimensional space pleasure–arousal–dominance (PAD) and the cognitive assessment of emotion words. The SAET consists of twenty images that display a cartoon figure expressing ten positive and ten negative emotions. The instrument can be used during interactions with visual interfaces such as websites, posters, cell phones, and vehicles, and allows participants to select interface elements that elicit specific emotions. Experimental results show the validity of this type of tool in terms of both semantic discrimination of emotions and quantitative numerical validation.