Emergency exit signs are a mandatory and essential element for the prevention and planning of evacuation in all types of buildings. In recent decades, some achievements have been made in emergency exit signs research, but there is a lack of literature reviews on the subject. This study focused on exploring the research status and development trends in emergency exit signs using a visualization analysis of bibliometrics. The findings of this paper are as follows: First, through co-authorship analysis, we identified countries, institutions, and authors that have made outstanding contributions in the research area. Second, through co-citation analysis, we revealed important journals, documents, and authors in the research field. Third, through keyword co-occurrence analysis, we found research focuses include sign effectiveness, research methods, and research content. And the research frontiers include virtual reality, visibility, and emergency evacuation. The study can serve as a reference for relevant researchers studying emergency exit signs.

Enhancing transparency through interface design is an effective method for improving driving safety while reducing driver workloads, potentially fostering human–machine collaboration. However, to ensure system usability and safety, operator psychological factors and operational performance must be well balanced. This study investigates how the introduction of transparency design into urban rail transit driving tasks influences drivers’ situational awareness (SA), trust in automation (TiA), sense of agency (SoA), workload, operational performance, and visual behavior. Three transparency driver–machine interface (DMI) information conditions were evaluated: DMI1, which provided continuous feedback on vehicle operating status and actions; DMI1+2, which added inferential explanations; and DMI1+2+3, which further incorporated proactive predictions. Results from simulated driving experiments with 32 participants indicated that an appropriate level of transparency significantly enhanced TiA and SoA, thereby yielding the greatest acceptance. High transparency significantly aided in predictable takeover tasks but affected gains in TiA and SoA, increased workload, and disrupted perception-level SA. Compared with previous research findings, this study indicates the presence of a disparity in transparency needs for low-workload tasks. Therefore, caution should be exercised when introducing high-transparency designs in urban rail transit driving tasks. Nonetheless, an appropriate transparency interface design can enhance the driving experience.