A comparative study of time series modeling for driving behavior towards prediction
A comparative study of time series modeling for driving behavior towards prediction
复制标题
驱动行为预测的时间序列建模的比较研究
DOI：
10.1109/apsipa.2013.6694284
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Masumi Egawa
中科院分区：
文献类型：
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作者：
Ryunosuke Hamada;Takatomi Kubo;K. Ikeda;Zujie Zhang;T. Bando;Masumi Egawa
驾驶行为预测是开发下一代驾驶支持系统的重要问题。为了考虑不同的驾驶情况，有必要对多个驾驶操作时间序列数据进行建模。在这项研究中，我们建模多个驾驶操作时间序列与四种建模方法，包括贝塔过程自回归隐马尔可夫模型（BP-AR-HMM），我们在我们以前的研究中使用。我们定量地比较了建模方法的预测精度，并得出结论，BP-AR-HMM优于其他建模方法在建模多个驾驶操作时间序列和预测未知的驾驶操作。结果表明，BP-AR-HMM能够较好地估计驾驶员的行为和行为之间的转移概率，这是因为BP-AR-HMM能够处理多个时间序列之间的共性和差异，而其他方法不能。因此，BP-AR-HMM可以帮助我们预测驾驶员在真实的环境中的行为，并开发下一代驾驶支持系统。
Prediction of driving behaviors is an important problem in developing a next-generation driving support system. In order to take diverse driving situations into account, it is necessary to model multiple driving operation time series data. In this study we modeled multiple driving operation time series with four modeling methods including beta process autoregressive hidden Markov model (BP-AR-HMM), which we used in our previous study. We quantitatively compared the modeling methods with respect to prediction accuracies, and concluded that BP-AR-HMM excelled the other modeling methods in modeling multiple driving operation time series and predicting unknown driving operations. The result suggests that BP-AR-HMM estimated behaviors of a driver and transition probabilities between the behaviors more successfully than the other methods, because BP-AR-HMM can deal with commonalities and differences among multiple time series, but the others cannot. Therefore BP-AR-HMM may help us to predict driver behaviors in real environment and to develop the next-generation driving support system.
通过对制动压力信号建模来估计驾驶阶段
DOI：
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发表时间：
2009
期刊：
影响因子：
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作者：
Hiroki Mima;他5名
通讯作者：
他5名