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Research on Fatigue Driving Pre-warning System Based on Multi-information Fusion

机译:基于多信息融合的疲劳驾驶预警系统研究

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With the development of science and technology, transportation network has grown faster. But at the same time, the quantity of traffic accidents due to fatigue driving grows faster as well. In the meantime, fatigue driving has been one of the main causes of traffic accidents. Therefore, it is indispensable for us to study the detection of fatigue driving to help to driving safety. There are numerous approaches in discrimination method. Each type of method has its reasonable theoretical basis, but the disadvantages of traditional fatigue driving detection methods have been more and more obvious since we study the traditional physiology and psychological features of fatigue drivers. So we set up a new system based on multi-information fusion and pattern recognition theory. In the paper, the fatigue driving pre-warning system discriminates fatigue by analyzing the characteristic parameters, the parameters derived from the steering wheel angle, the driver's power of gripping and the heart rate. And the data analysis system is established based on fuzzy C-means clustering theory. Finally, KNN classifier is used to establish the relation between feature indexes and fatigue degree. It is verified that the system has the better accuracy, agility and robustness according to our confirmatory experiment.
机译:随着科学技术的发展,交通网络增长了更快。但与此同时,由于疲劳驾驶的交通事故数量也更快地增长。与此同时,疲劳驾驶是交通事故的主要原因之一。因此,我们必须研究检测疲劳驾驶以帮助推动安全性是必不可少的。鉴别方法有许多方法。每种类型的方法都具有合理的理论基础,但传统疲劳驾驶检测方法的缺点是越来越明显,因为我们研究了疲劳司机的传统生理学和心理特征。因此,我们根据多信息融合和模式识别理论建立了一种新系统。在本文中,疲劳驾驶预警系统通过分析特征参数来辨别疲劳,从方向盘角度来源,驾驶员的抓握力和心率。并且数据分析系统是基于模糊C均值聚类理论建立的。最后,knn分类器用于建立特征索引和疲劳度之间的关系。根据我们的确认实验,该系统具有更好的准确性,灵活性和鲁棒性。

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