## 3 Model and Algorithm Design  
This paper adopts classic algorithms for data fusion and preprocessing.  
Multimodal data fusion is crucial for the driver physiological state monitoring system, as it integrates signals from multiple sensors to provide a more accurate and stable physiological analysis. By combining data from sensors monitoring the heart rate, respiratory rate, electrodermal activity, and posture, the system mitigates the effects of environmental interference or errors that may arise from individual sensors, thus improving the overall accuracy and robustness of monitoring [26; 27]. The system first filters and denoises the collected raw signals to eliminate environmental noise and sensor interference [22], ensuring that the signals from each sensor are clean and consistent before fusion. To address discrepancies in the timing of different signal sources, Dynamic Time Warping (DTW) is employed for data alignment [23]. Once the signals are aligned, the system moves on to the multimodal data fusion phase. The objective of this fusion is to leverage the strengths of each sensor while minimizing their limitations, ensuring that the final fused data accurately reflect the driver's physiological state.  
To achieve effective multimodal data fusion, this paper adopts a weighted averaging fusion strategy [17]. This approach assigns appropriate weights to signals from each sensor, ensuring that sensors with higher signal quality contribute more to the final fusion result under different driving conditions. The weight distribution is dynamically determined based on real-time assessments of signal quality, including metrics, such as the signal-to-noiseratio and fluctuation amplitude. The system continuously adjusts the weights of individual sensors to accommodate changes in the driver's state or environment [16]. This weighted averaging method is computationally efficient, capable of handling multiple sensor outputs while maintaining signal stability. Compared to traditional fusion methods, it ensures high monitoring accuracy even in the face of posture changes or noisy environments [15].  
To further enhance the robustness of data fusion, this paper introduces redundancy in sensor signals. By placing multiple sensors at key monitoring points, the system ensures redundant signal acquisition, mitigating the impact of sensor failure or signal loss on the monitoring accuracy [10]. During the fusion stage, these redundant signals are processed using a collaborative filtering algorithm, which verifies the final output with data from multiple sources. Additionally, a Kalman filter is applied to the fused signal during fusion to smooth it and reduce the impact of sudden outliers. The Kalman filter not only smooths the signal but also predicts future values, improving the system's responsiveness to changes in the driver's physiological state [24]. To evaluate the effectiveness of multimodal data fusion, this paper conducted multiple experiments to compare monitoring accuracy before and after fusion. Experimental results show that through multimodal data fusion, the system significantly reduces the error rate when monitoring physiological indicators, such as the heart rate and respiratory rate. Moreover, the fused signal maintains high robustness under sudden events (e.g., sudden braking, emergency steering), highlighting the advantages of multimodal fusion in managing complex driving scenarios [9].