Abstract
Chinese market penetration rate of automated driving systems (ADS) is increasing rapidly. Users are willing to try ADS, but the negative feeling is along with the substantial experience as well. One reason is the gap between users experience and expectation of ADS function, which was formed based on the market information. The other reason is ADS cannot meet different individuals’ driving preferences and habits in short term.
As a consequence, users might decrease their trust with ADS, therefore reducing the usage frequency and losing opportunities to rebuild trust. This counteract with the original intention of ADS, which is to improve driving safety.
In the human-machine cooperative ADS, trust repair is necessary for maintaining the trust between the human and the system; in terms of method, anthropomorphic in-vehicle voice communication can enhance the degree of trust. However, there are scarce amounts of studies regarding these two concepts.
Regarding the circumstance, our research proposes a trust repair strategy that is centered on voice communication in ADS. Based on existing ADS technical capabilities, our goal is to improve users' trust and experience with ADS in the early stage of use.
Through market user research, our team systematically summarized the types of scenarios and reasons for the reduction of trust in ADS as the basis of our research. Furthermore, based on the concept of trust repair, a voice-communication-based interaction strategy for ADS is established, and specific dialogues are designed. Finally, a scenario simulated user test (N = 60) was conducted to verify the effectiveness of the strategy: this trust repair approach can significantly improve users' trust in the early use of ADS and their subjective attitudes to use it. Overall, the results provide a new perspective and direct implications for ADS and in-vehicle voice assistant designers.
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Song, Z., Yang, Y., Deng, M. (2023). Trust Repair of Automated Driving System: A New In-Vehicle Communication Strategy of Voice Assistant. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2023 Posters. HCII 2023. Communications in Computer and Information Science, vol 1836. Springer, Cham. https://doi.org/10.1007/978-3-031-36004-6_14
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