Visualization of Driving Behavior Based on Hidden Feature Extraction by Using Deep Learning
Visualization of Driving Behavior Based on Hidden Feature Extraction by Using Deep Learning
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DOI：
10.1109/tits.2017.2649541
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发表时间：
2017-09-01
影响因子：
8.5
通讯作者：
Bando, Takashi
中科院分区：
文献类型：
--
作者：
Liu, HaiLong;Taniguchi, Tadahiro;Bando, Takashi
在本文中，我们提出了一种可视化的方法，驾驶行为，帮助人们认识到独特的驾驶行为模式，在连续的驾驶行为数据。可以使用连接到控制区域网络的各种类型的传感器来测量驾驶行为。所测量的多维时间序列数据被称为驾驶行为数据。在许多情况下，时间序列数据的每个维度在统计意义上并不是相互独立的。例如，加速器开度和纵向加速度是相互依赖的。我们假设，只有少量的隐藏功能，是必不可少的驾驶行为产生的多元驾驶行为数据。因此，从冗余的驾驶行为数据中提取必要的隐藏特征是开发有效的驾驶行为可视化方法所要解决的问题。在本文中，我们提出使用深度稀疏自动编码器（DSAE）来提取隐藏的特征，以实现驾驶行为的可视化。基于DSAE，我们提出了一种可视化方法，称为驱动颜色地图，通过映射提取的3-D隐藏功能的红绿色蓝（RGB）的颜色空间。通过将颜色放置在地图上的相应位置来产生驾驶颜色地图。主观实验表明，基于DSAE的特征提取方法是有效的可视化。此外，还利用模式识别方法对其性能进行了数值评估。我们还提供了在实际问题中使用驾驶彩色地图的应用程序的示例。综上所述，基于DSAE的驾驶颜色图有助于更好地可视化驾驶行为。
In this paper, we propose a visualization method for driving behavior that helps people to recognize distinctive driving behavior patterns in continuous driving behavior data. Driving behavior can be measured using various types of sensors connected to a control area network. The measured multi-dimensional time series data are called driving behavior data. In many cases, each dimension of the time series data is not independent of each other in a statistical sense. For example, accelerator opening rate and longitudinal acceleration are mutually dependent. We hypothesize that only a small number of hidden features that are essential for driving behavior are generating the multivariate driving behavior data. Thus, extracting essential hidden features from measured redundant driving behavior data is a problem to be solved to develop an effective visualization method for driving behavior. In this paper, we propose using deep sparse autoencoder (DSAE) to extract hidden features for visualization of driving behavior. Based on the DSAE, we propose a visualization method called a driving color map by mapping the extracted 3-D hidden feature to the red green blue (RGB) color space. A driving color map is produced by placing the colors in the corresponding positions on the map. The subjective experiment shows that feature extraction method based on the DSAE is effective for visualization. In addition, its performance is also evaluated numerically by using pattern recognition method. We also provide examples of applications that use driving color maps in practical problems. In summary, it is shown the driving color map based on DSAE facilitates better visualization of driving behavior.