Research on identification method of heavy vehicle rollover based on hidden Markov model
Research on identification method of heavy vehicle rollover based on hidden Markov model
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基于隐马尔可夫模型的重型车辆侧翻识别方法研究
DOI：
10.1515/phys-2017-0054
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发表时间：
2017-07
期刊：
影响因子：
1.9
通讯作者：
Wang Jinsheng
中科院分区：
文献类型：
--
作者：
Zhao Zhiguo;Wang Yeqin;Hu Xiaoming;Tao Yukai;Wang Jinsheng
摘要针对重型车辆载荷及其重心变化较大时预警可信度下降的问题，提出了基于隐马尔可夫模型(HMM)的重型车辆侧翻状态动态识别方法。该方法以侧向加速度和滚动角作为模型基座的观测值。采用维特比算法对观测序列中概率最大的状态序列进行预测，采用马尔可夫预测算法计算状态转移规律，预测车辆在未来某一时间段的状态。根据双车道变换和转向的组合条件，应用Trucksim和MatLab训练的隐马尔可夫模型，将该模型应用于重型汽车侧翻状态的在线辨识。识别结果表明，该模型能够准确、高效地识别车辆侧翻状态，具有较好的适用性。本研究为车辆主动安全预警与控制提供了一种新的方法和一般策略，对隐马尔可夫理论在碰撞、追尾和车道偏离预警系统中的应用具有参考意义。
Abstract Aiming at the problem of early warning credibility degradation as the heavy vehicle load and its center of gravity change greatly; the heavy vehicle rollover state identification method based on the Hidden Markov Model (HMM, is introduced to identify heavy vehicle lateral conditions dynamically in this paper. In this method, the lateral acceleration and roll angle are taken as the observation values of the model base. The Viterbi algorithm is used to predict the state sequence with the highest probability in the observed sequence, and the Markov prediction algorithm is adopted to calculate the state transition law and to predict the state of the vehicle in a certain period of time in the future. According to combination conditions of Double lane change and steering, applying Trucksim and Matlab trained hidden Markov model, the model is applied to the online identification of heavy vehicle rollover states. The identification results show that the model can accurately and efficiently identify the vehicle rollover state, and has good applicability. This study provides a novel method and a general strategy for active safety early warning and control of vehicles, which has reference significance for the application of the Hidden Markov theory in collision, rear-end and lane departure warning system.
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DOI：
10.4271/931976
发表时间：
1993-11
期刊：
--
影响因子：
--
作者：
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通讯作者：
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影响因子：
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DOI：
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发表时间：
1997-12
期刊：
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影响因子：
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影响因子：
3.1
作者：
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DOI：
10.4271/2011-28-0113
发表时间：
2011-10
期刊：
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影响因子：
--
作者：
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通讯作者：
P. Sharma;Ravi Kiran Cheni;B. Sriram