Deng Chao, Wu Chaozhong, Lyu Nengchao, Huang Zhen
Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, Hubei, China.
Engineering Research Center for Transportation Safety, Wuhan University of Technology, Wuhan, Hubei, China.
PLoS One. 2017 Aug 24;12(8):e0182419. doi: 10.1371/journal.pone.0182419. eCollection 2017.
Since the advantage of hidden Markov model in dealing with time series data and for the sake of identifying driving style, three driving style (aggressive, moderate and mild) are modeled reasonably through hidden Markov model based on driver braking characteristics to achieve efficient driving style. Firstly, braking impulse and the maximum braking unit area of vacuum booster within a certain time are collected from braking operation, and then general braking and emergency braking characteristics are extracted to code the braking characteristics. Secondly, the braking behavior observation sequence is used to describe the initial parameters of hidden Markov model, and the generation of the hidden Markov model for differentiating and an observation sequence which is trained and judged by the driving style is introduced. Thirdly, the maximum likelihood logarithm could be implied from the observable parameters. The recognition accuracy of algorithm is verified through experiments and two common pattern recognition algorithms. The results showed that the driving style discrimination based on hidden Markov model algorithm could realize effective discriminant of driving style.
由于隐马尔可夫模型在处理时间序列数据方面的优势，且为了识别驾驶风格，基于驾驶员制动特性，通过隐马尔可夫模型对三种驾驶风格（激进型、适中型和温和型）进行合理建模，以实现高效的驾驶风格识别。首先，从制动操作中收集一定时间内的制动冲量和真空助力器的最大制动单位面积，然后提取一般制动和紧急制动特性以对制动特性进行编码。其次，利用制动行为观察序列来描述隐马尔可夫模型的初始参数，并引入用于区分的隐马尔可夫模型的生成以及由驾驶风格进行训练和判断的观察序列。第三，从可观测参数中可以隐含出最大似然对数。通过实验和两种常见的模式识别算法验证了算法的识别准确率。结果表明，基于隐马尔可夫模型算法的驾驶风格判别能够实现对驾驶风格的有效判别。
