## 5 Discussion
### Suggestions  
Based on the systematic analysis of fatigue causes, occupational differences, interactive dialogue effects, and voice assistant adaptability in this study, the following recommendations are proposed to provide theoretical support and practical guidance for the design and application of intelligent voice interaction systems, while enriching the knowledge framework in this field:  
1. Incorporate Psychological Factors into Fatigue Detection Models  
Current mainstream fatigue detection primarily relies on physiological indicators such as the heart rate, eye movement, and posture changes. However, this study reveals that psychological states--including emotional fluctuations, feelings of loneliness, and stress--have a significant impact on fatigue formation. Integrating psychological dimensions such as emotion monitoring, speech emotion recognition, and tone analysis into multimodal fatigue detection can not only improve detection accuracy but also provide a basis for personalized intervention, thereby enriching the theoretical framework of fatigue detection. This direction offers new entry points for future research and facilitates the transition of fatigue management technologies from relying solely on physiological parameters to a comprehensive integration of psychological and physiological factors. Implement Personalized Voice Interaction Design Based on Occupational Types  
This study systematically reveals for the first time the significant differences in dialogue needs among drivers of different occupational backgrounds, highlighting the profound influence of the occupation on voice interaction preferences. Intelligent voice systems should tailor interaction strategies according to users' occupational characteristics: emphasizing functional, task-oriented Q&A modes for long-haul freight drivers; focusing on social and emotional regulation dialogue content for taxi and rideshare drivers; and designing relaxed, entertainment-oriented, low-intensity interactions for private car owners. This design concept not only supplements the understanding of user diversity in intelligent driving human-machine interaction but also provides clear pathways for developers to implement differentiated customization. Develop a Dynamic Adaptation Mechanism for Fatigue Dialogue Intervention  
Drivers' acceptance of voice interaction varies significantly with fatigue levels. By integrating real-time fatigue detection, dynamically adjusting dialogue intensity and content can achieve precise and effective vocal arousal and psychological relief. This mechanism overcomes the static limitations of traditional fatigue management and promotes the system's shift from passive detection to active intervention. Future research may explore more intelligent interaction strategies based on this framework, such as deep integration of multimodal sensor fusion and context awareness, thereby enhancing the safety assurance capabilities of intelligent driving systems. Enhance Contextual Awareness and Adaptation to Individual Differences  
Fatigue manifestations are influenced with multiple factors, including the driving environment, time cycles, and task pressure. Future intelligent voice assistants should combine context-aware technologies--such as time, geographic location, and task type sensing--with user historical behavior and emotional preference modeling to realize more precise and personalized dialogue strategy adjustments. This not only enriches the adaptability theory of voice interaction but also offers a technical route for optimizing the user experience in intelligent driving systems, contributing to improved user retention and application effectiveness. Shift Fatigue Management from Passive Detection to Active Intervention  
Traditional fatigue management largely depends on post-event reminders, which have limited intervention timeliness. This study demonstrates that moderate voice interactions can provide psychological relief and restore attention at early stages of fatigue, effectively enhancing driving safety.