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Recognition of the Driving Style in Vehicle Drivers

机译：车辆驾驶员的驾驶风格识别

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This paper presents three different approaches to recognize driving style based on a hierarchical-model. Specifically, it proposes a hierarchical model for the recognition of the driving style for advanced driver-assistance systems (ADAS) for vehicles. This hierarchical model for the recognition of the style of the car driving considers three aspects: the driver emotions, the driver state, and finally, the driving style itself. In this way, the proposed hierarchical pattern is composed of three levels of descriptors/features, one to recognize the emotional states, another to recognize the driver state, and the last one to recognize the driving style. Each level has a set of descriptors, which can be sensed in a real context. Finally, the paper presents three driving style recognition algorithms based on different paradigms. One is based on fuzzy logic, another is based on chronicles (a temporal logic paradigm), and the last is based on an algorithm that uses the idea of the recognition process of the neocortex, called Ar2p (Algoritmo Recursivo de Reconocimiento de Patrones, for its acronym in Spanish). In the paper, these approaches are compared using real datasets, using different metrics of interest in the context of the Internet of the Things, in order to determine their capabilities of reasoning, adaptation, and the communication of information. In general, the initial results are encouraging, specifically in the cases of chronicles and Ar2p, which give the best results.
机译：本文提出了三种基于分层模型的驾驶风格识别方法。具体而言，它提出了用于识别车辆高级驾驶员辅助系统（ADAS）的驾驶方式的分层模型。这种用于识别汽车驾驶风格的分层模型考虑了三个方面：驾驶员情绪，驾驶员状态以及最终的驾驶风格本身。以这种方式，所提出的分层模式由描述符/特征的三个级别组成，一个级别识别情绪状态，另一个级别识别驾驶员状态，最后一个级别识别驾驶风格。每个级别都有一组描述符，可以在实际上下文中进行感测。最后，本文提出了三种基于不同范式的驾驶风格识别算法。一种基于模糊逻辑，另一种基于编年史（一种时间逻辑范式），最后一种基于一种算法，该算法使用了新皮层的识别过程的思想，称为Ar2p（Algoritmo Recursivo de Reconocimiento de Patrones，用于它的首字母缩写为西班牙语）。在本文中，这些方法是使用真实的数据集，在物联网环境中使用不同的关注指标进行比较的，以确定它们的推理，适应和信息交流能力。通常，最初的结果令人鼓舞，特别是对于编年史和Ar2p而言，结果最好。

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