摘要
为了减少因驾驶员的生理和心理健康状况变化引发的交通事故,实现对驾驶员健康状态的自动监测和实时优化,提出以控制论的基本理论为基础的驾驶员健康状态闭环反馈系统框架.首先基于驾驶员日志建立个性化健康模型;然后结合各种传感器实时采集的驾驶员、车辆和道路环境等多模态数据,对驾驶员当前健康状态进行估计;最后针对预设健康目标,为驾驶员提供可执行的行为建议,实现对驾驶员健康状态的导航优化.在最关键的实时监测环节,提出基于注意力的卷积神经网络(convolutional neural network,CNN)-长短期记忆网络(long short term memory,LSTM)的多模态融合模型,实现对驾驶员压力、情绪和疲劳3个方面的健康状态估计.在私有数据集和公开数据集上分别开展的实验验证均获得高于90%的检测准确率.实验结果表明,提出的模型和方法可以实时准确监测驾驶员的压力、情绪和疲劳状态,为实现驾驶员的个性化健康导航系统提供有力支撑.
To decrease the number of traffic accidents caused by changes in drivers’physical and mental health conditions and accomplish automatic monitoring and real-time optimization of drivers’health states,a closed-loop feedback system framework for drivers’health states was proposed based on the basic theory of cybernetics.First,a personalized health model was established based on a driver’s log data.Then by combining this model with the real-time multimodal data of the driver,vehicle and road environment from various sensors,the driver’s current health state was estimated.Finally given the health goal of the driver,executable behavior suggestions were provided to navigate the driver to an optimized health state.For the most critical phase of real-time monitoring,a multimodal fusion model based on attentional convolutional neural networks and long short-term memory network(CNN-LSTM)was proposed to estimate the three aspects of driver health,namely,stress,emotion,and fatigue.Experiments on both private and public datasets have achieved a detection accuracy of more than 90%,which demonstrates that the proposed model and methods can accurately monitor drivers’stress,emotion,and fatigue states in real time,thus provide a solid basis for implementing the personalized health navigation system for drivers(PHN-D).
作者
牟伦田
周朝
赵艺远
赵鹏飞
Bahareh Nakisa
Ramesh Jain
尹宝才
MOU Luntian;ZHOU Chao;ZHAO Yiyuan;ZHAO Pengfei;Bahareh Nakisa;Ramesh Jain;YIN Baocai(Beijing Key Laboratory of Multimedia and Intelligent Software Technology,Beijing Institute of Artificial Intelligence,Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;School of Information Technology,Faculty of Science Engineering and Built Environment,Deakin University,Victoria 3216,Australia;Institute for Future Health,University of California,Irvine,Irvine 92697,USA)
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2021年第5期508-519,共12页
Journal of Beijing University of Technology
基金
国家自然科学基金资助项目(61672068)。
关键词
个性化健康导航
多模态融合
注意力
卷积神经网络
长短期记忆网络
卷积神经网络-长短期记忆网络(CNN-LSTM)
personalized health navigation
multimodal fusion
attention
convolutional neural network(CNN)
long short-term memory network(LSTM)
convolutional neural networks and long short-term memory network(CNN-LSTM)
