大连理工大学学报2018，Vol.58Issue(2)：194-201,8.DOI:10.7511/dllgxb201802013
基于改进HMM的驾驶疲劳险态识别方法
Driver fatigue risk identification methods based on improved HMM
摘要
Abstract
The generation of driver fatigue is a progressive dynamic process.Relevant research based on hidden Markov model (HMM)must determine the model's initial values firstly and the training process tends to fall into local optimum.Therefore,particle swarm optimization (PSO)algorithm is introduced into the process of training HMM to improve the above existing problems.What's more, the improved method and forward-backward (BW)algorithm are compared in details based on typical driver fatigue data set.Experimental and analytical test results show that the improved method is more accurate and stable than BW algorithm in driver fatigue prediction.关键词
驾驶疲劳/隐马尔可夫模型/前向后向算法/粒子群优化算法Key words
driver fatigue/hidden Markov model/forward-backward algorithm/particle swarm optimization algorithm分类
信息技术与安全科学引用本文复制引用
张明恒,翟晓娟,朱有明,赵秀栋..基于改进HMM的驾驶疲劳险态识别方法[J].大连理工大学学报,2018,58(2):194-201,8.基金项目
国家自然科学基金资助项目(51675077) （51675077）
中国博士后科学基金资助项目(2015M581329,2017T100178) （2015M581329,2017T100178）
中央高校基本科研业务费专项资金资助项目(DUT16QY42). （DUT16QY42）
