## 4 Experimental Design and Results
### Ablation Experiment
This chart effectively visualizes how multimodal fusion enhances system performance in diverse driving contexts and emphasizes the critical role of each sensor in monitoring driver fatigue and stress. There are significant differences in the contribution of various sensors to system accuracy across different driving states. In high-speed and rapid driving conditions, drivers experience higher physiological stress, making the heart rate and respiratory rate the most important signal sources. Fluctuations in the heart rate reflect the driver's physiological load, while the respiratory rate is more sensitive to capturing stress responses. In these high-intensity driving environments, the combination of these two signals enables the accurate detection of driver fatigue and stress, significantly improving the overall accuracy of the system. In contrast, during low-speed driving and traffic congestion, although the driver's physiological load is lower, skin conductance and body pressure distribution signals show higher contributions. Low-speed driving and traffic congestion often involve frequent braking and acceleration, which increases the driver's psychological stress. In this context, skin conductance, as a key physiological signal reflecting the driver's emotional and psychological state, becomes more sensitive. Additionally, body pressure distribution provides valuable information, especially in cases of long-duration driving or uncomfortable driving postures, reflecting the driver's physical fatigue or discomfort, making it highly valuable for fatigue and stress detection. The complementarity of different sensor signals in various driving conditions plays a crucial role in enhancing system performance. For example, in high-speed and rapid driving states, the combination of the heart rate and respiratory rate provides comprehensive physiological feedback. The heart rate helps monitor the driver's tension level, while the respiratory rate reveals stress responses. In these high-pressure environments, the  
Figure 5: Performance curves of the ablation experiments for each modality signal. contribution of a single sensor might not fully reflect the driver's physiological state, and multimodal sensor fusion significantly improves system accuracy. In low-speed driving and traffic congestion, the complementarity of skin conductance and body pressure distribution is particularly evident. Skin conductance is highly sensitive to psychological stress, while body pressure distribution signals complement the monitoring of physical fatigue, especially in prolonged or fatigued driving conditions. By combining these two signals, the system is able to more comprehensively assess driver fatigue and stress, thereby improving the overall accuracy of the system. Figure 7 depicts the correlation changes between the driver's reaction time and the physiological states detected by the system under different driving conditions. It shows the relationship between reaction time and the system-identified fatigue or stress states, revealing the correlation between the driver's reaction time and changes in the physiological status. This helps illustrate how the multimodal fusion system captures these variations. The results of the ablation experiments indicate that the multimodal data-fusion system exhibits strong adaptability across different speed scenarios. Particularly in high-speed and rapid driving conditions, the fusion of heart rate and respiratory rate signals significantly enhances the system's monitoring accuracy of driver states. Although skin conductance and body pressure distribution signals contribute less in certain situations, they provide additional auxiliary information in specific contexts (such as traffic congestion and low-speed driving). Through multimodal fusion, the system effectively addresses the complex physiological signal variations in different driving environments, improving overall monitoring effectiveness. Figure 6: (**a**) Importance variation curves of each modality signal.