Escuela Técnica Superior de Ingeniería de Sistemas Informáticos, Universidad Politécnica de Madrid, 28031 Madrid, Spain.
Facultad de Ingeniería, Universidad Espíritu Santo, Samborondón 092301, Ecuador.
Sensors (Basel). 2020 Mar 18;20(6):1692. doi: 10.3390/s20061692.
Identifying driving styles using classification models with in-vehicle data can provide automated feedback to drivers on their driving behavior, particularly if they are driving safely. Although several classification models have been developed for this purpose, there is no consensus on which classifier performs better at identifying driving styles. Therefore, more research is needed to evaluate classification models by comparing performance metrics. In this paper, a data-driven machine-learning methodology for classifying driving styles is introduced. This methodology is grounded in well-established machine-learning (ML) methods and literature related to driving-styles research. The methodology is illustrated through a study involving data collected from 50 drivers from two different cities in a naturalistic setting. Five features were extracted from the raw data. Fifteen experts were involved in the data labeling to derive the ground truth of the dataset. The dataset fed five different models (Support Vector Machines (SVM), Artificial Neural Networks (ANN), fuzzy logic, k-Nearest Neighbor (kNN), and Random Forests (RF)). These models were evaluated in terms of a set of performance metrics and statistical tests. The experimental results from performance metrics showed that SVM outperformed the other four models, achieving an average accuracy of 0.96, F1-Score of 0.9595, Area Under the Curve (AUC) of 0.9730, and Kappa of 0.9375. In addition, Wilcoxon tests indicated that ANN predicts differently to the other four models. These promising results demonstrate that the proposed methodology may support researchers in making informed decisions about which ML model performs better for driving-styles classification.
使用车载数据的分类模型识别驾驶风格可以为驾驶员的驾驶行为提供自动化反馈，特别是如果他们的驾驶行为安全的话。尽管已经开发了几种分类模型，但对于哪种分类器在识别驾驶风格方面表现更好，尚未达成共识。因此，需要更多的研究通过比较性能指标来评估分类模型。本文提出了一种基于数据驱动的机器学习方法来对驾驶风格进行分类。该方法基于成熟的机器学习（ML）方法和与驾驶风格研究相关的文献。该方法通过一项涉及在自然环境中从两个不同城市的 50 名驾驶员收集数据的研究进行了说明。从原始数据中提取了五个特征。十五位专家参与了数据标注，以得出数据集的真实情况。该数据集馈送到五个不同的模型（支持向量机（SVM）、人工神经网络（ANN）、模糊逻辑、k-最近邻（kNN）和随机森林（RF））。这些模型根据一组性能指标和统计测试进行了评估。性能指标的实验结果表明，SVM 优于其他四个模型，平均准确率为 0.96，F1 得分为 0.9595，曲线下面积（AUC）为 0.9730，Kappa 为 0.9375。此外，Wilcoxon 检验表明，ANN 与其他四个模型的预测方式不同。这些有希望的结果表明，所提出的方法可以为研究人员提供有关哪种 ML 模型在驾驶风格分类方面表现更好的决策信息。
