Journal Article

Monitoring and Analyzing Driver Physiological States Based on Automotive Electronic Identification and Multimodal Biometric Recognition Methods

Duan, Qin, Liu, Xiaosong, Wang, Li, Zhang, Nanfeng, Xiao, Jinchao, Yang, Jingfeng, Zhou, Shengpei

2024

Overview

In an intelligent driving environment, monitoring the physiological state of drivers is crucial for ensuring driving safety. This paper proposes a method for monitoring and analyzing driver physiological characteristics by combining electronic vehicle identification (EVI) with multimodal biometric recognition. The method aims to efficiently monitor the driver’s heart rate, breathing frequency, emotional state, and fatigue level, providing real-time feedback to intelligent driving systems to enhance driving safety. First, considering the precision, adaptability, and real-time capabilities of current physiological signal monitoring devices, an intelligent cushion integrating MEMSs (Micro-Electro-Mechanical Systems) and optical sensors is designed. This cushion collects heart rate and breathing frequency data in real time without disrupting the driver, while an electrodermal activity monitoring system captures electromyography data. The sensor layout is optimized to accommodate various driving postures, ensuring accurate data collection. The EVI system assigns a unique identifier to each vehicle, linking it to the physiological data of different drivers. By combining the driver physiological data with the vehicle’s operational environment data, a comprehensive multi-source data fusion system is established for a driving state evaluation. Secondly, a deep learning model is employed to analyze physiological signals, specifically combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The CNN extracts spatial features from the input signals, while the LSTM processes time-series data to capture the temporal characteristics. This combined model effectively identifies and analyzes the driver’s physiological state, enabling timely anomaly detection. The method was validated through real-vehicle tests involving multiple drivers, where extensive physiological and driving behavior data were collected. Experimental results show that the proposed method significantly enhances the accuracy and real-time performance of physiological state monitoring. These findings highlight the effectiveness of combining EVI with multimodal biometric recognition, offering a reliable means for assessing driver states in intelligent driving systems. Furthermore, the results emphasize the importance of personalizing adjustments based on individual driver differences for more effective monitoring.

• Publisher: MDPI AG
• Subject: Accuracy / adaptive weight adjustment / Anomalies / Artificial neural networks / Automobile drivers / Biometrics / Biometry / convolutional neural network / Cushions / Data analysis / Data collection / Data entry / Data integration / Deep learning / driver physiological monitoring / electronic vehicle identification / Heart beat / Heart rate / Long Short-Term Memory (LSTM) network / Machine learning / Methods / Microelectromechanical systems / multimodal biometric recognition / Neural networks / Optical measuring instruments / Physiology / Pressure distribution / Real time / Recognition / Sensors / Signal monitoring / Spatial data / System effectiveness / Traffic accidents & safety / Traffic safety / Vehicle identification / Vehicle safety