摘要
高铁司机警觉与行车安全密切相关。为实现司机警觉状态评估,该研究设计了一种基于多通道数据的高铁司机警觉评估实验方案。依托高铁驾驶模拟器,以连续驾驶的方式诱发高铁司机警觉衰退,实验过程中同步采集其脑电、心电、眼动以及简单反应时间和疲劳程度主观量表。基于神经网络、支持向量机等机器学习技术构建司机警觉评估模型技术方案。以司机神经、生理等融合特征为输入,以司机疲劳状态为输出,构建司机警觉评估模型,对提出的实验方案进行探讨,验证提出的实验方案的合理性与可应用性。
[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.
作者
郭峤枫
周明
刘帆洨
郭孜政
史磊
GUO Qiaofeng;ZHOU Ming;LIU Fanxiao;GUO Zizheng;SHI Lei(School of Transportation and Logistics,Southwest Jiaotong University,Chengdu 611756,China;Comprehensive Transportation Key Laboratory of Sichuan Province,Southwest Jiaotong University,Chengdu 611756,China;National United Engineering Laboratory of Integrated and Intelligent Transportation,Southwest Jiaotong University,Chengdu 611756,China)
出处
《实验技术与管理》
CAS
北大核心
2024年第1期14-25,共12页
Experimental Technology and Management
基金
国家自然科学基金项目(52072320)。
关键词
高铁司机
警觉评估
高铁模拟实验
高铁安全
实验设计
high-speed railway drivers
vigilance estimation
high-speed railway simulation experiments
high-speed railroad safety
experimental design
