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[Objective] Driving safety on high-speed rail (HSR) is of utmost importance, with a direct correlation between HSR travel safety and driver vigilance levels. This work aimed to develop a robust and comprehensive experimental scheme for estimating vigilance in HSR drivers. [Methods] The methodology integrates multichannel data to realize a nuanced and accurate evaluation of HSR driver vigilance levels. To simulate and reduce alertness among HSR drivers, a state-of-the-art HSR driving simulator is used, which shows continuous driving scenarios that closely imitate real-world conditions. Within this simulated environment, the basis for a multichannel HSR driver's vigilance estimation research and experimental scheme is established. The experimental phase involves the simultaneous collection of different data points, including the driver's EEG, ECG, eye movements, response times to simple tasks, and subjective reports detailing the level of fatigue experienced. These synchronous multichannel datasets, which are rich in information, form the basis for developing a sophisticated driver vigilance estimation model. Machine learning methods, such as neural networks and support vector machines, are exploited to leverage the wealth of multisource fusion features, which include the incorporation of the driver's neural and physiological characteristics as the input and the driver's fatigue state as the output. Central to this work is a careful examination of the effectiveness of the developed multichannel vigilance estimation scheme for HSR drivers, verifying its validity and practical application in real-world HSR driving scenarios. [Results] The experimental design highlights the significance of considering multichannel data and identifying the intricate interplay between occupational responsibilities and environmental factors that influence HSR drivers. The comprehensive experimental plan spans the collection of diverse data sources, including EEG, ECG, eye movement recordings, response times to various tasks, and subjectively reported levels of vigilance. The driver's response time to emergency situations is normalized to establish a vigilance grading standard, such as high, medium, and low, allowing for the identification of the driver's vigilance. This nuanced classification system offers the foundation for proactive measures to improve driving safety and prevent potential railway accidents or delayed responses during crucial situations. Acknowledging the constraints of wearable devices in collecting and transmitting physiological data, we advocate a forward-looking approach. Follow-up studies should encompass a holistic consideration of hardware and software aspects. This includes addressing the challenges associated with the collection, transmission, processing, and analysis of multichannel data. Proposals for improvements in data collection devices and transmission methods are presented to minimize interference and fortify the overall robustness of the experimental design. [Conclusions] In conclusion, this comprehensive approach establishes a solid theoretical and technical foundation for the preliminary design of safety systems for high-speed train applications. By addressing the challenges associated with multichannel data collection and wearable devices, this work considerably contributes to the advancement of HSR driver vigilance estimation and, consequently, plays a key role in improving the overall driving safety in HSR operations. [ABSTRACT FROM AUTHOR] |
