上海交通大学学报

上海交通大学学报（自然版）

• 交通运输 • 上一篇    

基于汽车操纵信号的驾驶员疲劳状态检测

李伟1，何其昌1，范秀敏1，2   

  1. (1.上海市网络化制造与企业信息化重点实验室，上海 200030；2.上海交通大学 机械系统与振动国家重点实验室，上海 200240)
  • 收稿日期:2009-04-01 修回日期:1900-01-01 出版日期:2010-02-26 发布日期:2010-02-26

Detection of Driver’s Fatigue Based on Vehicle Performance Output

LI Wei1，HE Qichang1，FAN Xiumin1,2   

  1. (1.Shanghai Key Laboratory of Advanced Manufacturing Environment, Shanghai 200030, China；2.State Key Laboratory of Mechanical System & Vibration, Shanghai Jiaotong University, Shanghai 200240, China)
  • Received:2009-04-01 Revised:1900-01-01 Online:2010-02-26 Published:2010-02-26

摘要： 将驾驶方向盘运动信息及道路偏移值作为驾驶员疲劳表征信息，通过模拟器的模拟驾驶实验，采集10名驾驶员疲劳表征数据，建立神经网络模型对驾驶员疲劳状态进行检测.基于PVT（Psychomotor Vigilance Task）测试结果及驾驶录像，采集清醒状态与疲劳状态的实验数据，并进行分析；然后对数据进行离散化和归一化，作为神经网络模型的输入.采用BP算法对神经网络模型进行训练，直至满足误差要求.实验结果表明，该方法检测驾驶员疲劳状态的准确率较高，实用性较强.

关键词: 疲劳驾驶, 方向盘转角, 道路偏移, 人工神经网络

Abstract: The steering wheel motion and lateral position were used to evaluate the driver’s fatigue based on the driving experiments on driving simulator. The driver’s fatigue detection system was developed by neural network. Data of ten driver’s driving experiments are collected and divided into two levels of fatigue based on the PVT (psychomotor vigilance task) testing and record video. And then, the data are processed offline to find the difference between alert and fatigue. As the input data of neural network, the steering wheel angle and lateral position should be discretized and normalized. The neural network is trained by BP algorithm until the expected error is reached. The experiment results show that this method is effective and practical to identify the driver’s fatigue level.

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