Trust building mechanism of multimodal interaction in autonomous driving system

Multimodal interaction technology, as a key means of enhancing trust in autonomous driving systems, integrates multi-channel information such as vision, voice, and touch to build a transparent and explainable human-machine collaborative mechanism. This study systematically explored the role of multimodal interaction in building trust, analyzed interaction design strategies for different driving scenarios, and experimentally verified the significant effect of multimodal feedback on improving user trust. The results show that reasonable multimodal interaction design can enhance users' understanding and prediction of system behavior, reduce uncertainty anxiety, and accelerate trust restoration in abnormal situations. At the same time, the study also revealed issues such as sensor fusion accuracy and ethical privacy faced by current technologies and envisioned future development directions such as anthropomorphic interaction and contextual awareness. The results of this study provide a theoretical basis and practical guidance for the interaction design of autonomous driving systems, which is of great value in promoting the commercialization of the technology.