## 1 Introduction  
With the rapid development of intelligent transportation and autonomous driving technologies, monitoring the physiological state of drivers has increasingly become a key factor in ensuring driving safety. Physiological signals, such as heart rate, respiratory rate, and fatigue levels, directly reflect the driver's mental state and capacity to respond [1]. Previous studies have shown that driver fatigue is a significant factor contributing to traffic accidents, especially during prolonged driving periods or in complex road conditions [2]. The real-time monitoring of a driver's physiological state can effectively mitigate accident risks and enhance driving safety [3]. However, traditional single-sensor monitoring methods have significant limitations. The accuracy of sensor data can often be impacted by different driving postures, and external environmental interference may lead to unstable monitoring data [4; 5]. For instance, while heart rate monitoring can effectively track cardiovascular activity, its accuracy is susceptible to variations in the driver posture, driving conditions, and sensor placement [6; 7]. Additionally, existing monitoring methods typically fail to provide high-precision, low-latency data in complex driving environments, resulting in insufficient timeliness for a driver state assessment [8]. In recent years, the adoption of multimodal biometric recognition technology has opened new possibilities for addressing these challenges. By integrating multiple sensors, such as MEMSs, optical fiber sensors, and electrodermal activity monitoring, multidimensional physiological data from drivers can be captured, significantly improving monitoring accuracy and stability [9; 10]. For example, using the high sensitivity of MEMS sensors enables the real-time capture of subtle changes in heart rate and respiratory rate, while optical fiber sensors effectively resist interference in complex driving environments [11]. In addition, advancements in data-fusion technologies have facilitated the real-time processing of multimodal data. Deep learning models, particularly Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, have demonstrated significant potential in physiological signal analysis. These models excel at handling time-series data and extracting spatial features, enabling the accurate identification of driver fatigue and stress states [12; 13]. For instance, by integrating heart rate, respiratory rate, and driving behavior data, researchers can more effectively predict the physiological load on drivers and trigger alerts when necessary [14]. Despite the promising improvements in monitoring accuracy brought about by these technologies, challenges remain in multimodal data fusion. Key issues include developing effective fusion models across different sensor signals, managing noise and missing data, and ensuring the real-time nature of data fusion [15; 16]. To address these challenges, researchers have proposed various optimization strategies, such as adaptive weighted fusion algorithms that dynamically adjust sensor weights based on real-time signal quality [17]. Additionally, advanced models, like Graph Neural Networks (GNNs), have been introduced to improve the management and analysis of multimodal data fusion and analysis [18]. While multiple factors contribute to road accidents, studies indicate that driver-related factors are responsible for over 90% of traffic incidents [19; 20]. This highlights the critical role of driver health and behavior monitoring in efforts to reduce traffic accidents and improve road safety. In response to these challenges, this paper introduces a method for monitoring the physiological state of drivers based on Electronic Vehicle Identification (EVI) and multimodal biometric recognition technology. The EVI system assigns a unique identifier to each vehicle, enabling it to associate with the physiological data of individual drivers.