| 基于HMM的驾驶员疲劳识别在智能汽车空间的应用 |
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| 引用本文： | 郁伟炜,吴卿.基于HMM的驾驶员疲劳识别在智能汽车空间的应用[J].计算机应用与软件,2011(10). |
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| 作者姓名： | 郁伟炜 吴卿 |
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| 作者单位： | 杭州电子科技大学计算机学院; |
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| 基金项目： | 国家自然科学基金项目(60703088) |
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| 摘 要： | 智能汽车空间作为普适计算一种具体而集中的表现,对此提出一个基于隐马尔科夫模型(HMM)的驾驶员疲劳识别应用。选取PERCLOS特征变量作为测评驾驶员疲劳的低层上下文,通过大量样本数据的训练,建立HMM,用Viterbi算法从观察序列中识别出最有可能的驾驶员隐藏状态,提醒驾驶员以确保安全的驾驶行为。最后通过在模拟实验环境中的案例验证了该方法的有效性。
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| 关 键 词： | 智能汽车空间 隐马尔科夫模型 上下文推理 驾驶员疲劳 PERCLOS |
APPLYING HMM-BASED DRIVER FATIGUE RECOGNITION IN SMART VEHICLE SPACE |
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| Yu Weiwei Wu Qing.APPLYING HMM-BASED DRIVER FATIGUE RECOGNITION IN SMART VEHICLE SPACE[J].Computer Applications and Software,2011(10). |
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| Authors: | Yu Weiwei Wu Qing |
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| Affiliation: | Yu Weiwei Wu Qing(School of Computer Science and Technology,Hangzhou Dianzi University,Hangzhou 310018,Zhejiang,China) |
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| Abstract: | Smart vehicle space is a specific and focused performance of pervasive computing;this paper presents an application about driver fatigue recognition based on hidden Markov model(HMM).The authors select PERCLOS feature variable as a low-level context of driver fatigue evaluation,and establish the HMM through a large number of sample data training.Then they identify the most likely driver's hidden state from the observation sequence using Viterbi algorithm,and remind drivers to ensure their safe driving behav... |
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| Keywords: | Smart vehicle space HMM Context reasoning Driver fatigue PERCLOS |
| 本文献已被 CNKI 等数据库收录！ |
