## 6 Conclusions  
By integrating the SOR theoretical framework with the Grounded Theory methodology, this study conducted in-depth interviews with 25 drivers from different occupational backgrounds to systematically investigate their preferences for voice assistant interaction content, format, and frequency under varying levels of driving fatigue. The findings reveal that drivers' interaction demands are significantly influenced by their fatigue state. Specifically, under severe fatigue, most drivers preferred highly stimulating voice prompts--such as rhythmic question-and-answer formats or humorous motivational messages--to rapidly enhance alertness. During mild fatigue, they tended to favor more soothing and gentle conversational styles, such as weather updates or music recommendations. In a non-fatigued state, drivers generally preferred a quiet driving environment and only engaged with voice interactions when necessary. Clear differences were also observed across driver groups: long-haul truck drivers prioritized practicality and frequent wakefulness prompts; taxi drivers favored interactive modes integrating navigation and social features; while private car owners showed a stronger preference for personalized and emotionally supportive interactions.  
To validate the qualitative findings, a driving simulation experiment was conducted to empirically examine the effect of different levels of voice stimulus intensity on driver alertness across varying fatigue levels. The experimental results supported the interview-based conclusions, particularly highlighting that high-intensity voice interactions significantly improved alertness under severe fatigue. This reinforces the practical value of voice interaction as an effective fatigue intervention strategy.  
Overall, this study not only extends the application of the SOR framework to the field of intelligent voice interaction but also provides empirical evidence for designing personalized voice assistants based on drivers' fatigue states and occupational characteristics. This study highlights the behavioral adaptability potential of voice interaction systems in traffic safety interventions, promoting a shift from purely passive fatigue detection to an active human-machine collaborative intervention model. This approach holds significant social safety value and broad prospects for widespread application. Future research can further integrate multimodal sensing technologies to deepen the understanding of contextual factors and cultural differences, thereby enhancing the safety and user experience of intelligent driving systems and advancing their innovation and widespread adoption.  
Conceptualization, C.Z., L.W. and Y.Y.; Methodology, C.Z., L.W. and Y.Y.; Data curation, C.Z.; Writing--original draft, C.Z. and L.W.; Writing--review & editing, Y.Y.; Project administration, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Ethical review and approval were waived for this study, due to the use of anonymized data collected through interviews. According to the \"Implementation of Ethical Review Measures for Human-Related Life Science and Medical Research\" issued by the Chinese government, studies that involve anonymized data and do not pose risk to participants may be exempt from ethical review (Article 32). This study was conducted in accordance with the Declaration of Helsinki.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.