Wei Leyu, Liang Lichan, Lei Tian, Yin Xiaohong, Wang Yanyan, Gao Mingyu, Liu Yunpeng
School of Electronic and Information, Hangzhou Dianzi University, Hangzhou 310018, China.
CETHIK Group Co., Ltd., Hangzhou 314501, China.
Sensors (Basel). 2023 Jul 27;23(15):6708. doi: 10.3390/s23156708.
Driving behavior recognition can provide an important reference for the intelligent vehicle industry and probe vehicle-based traffic estimation. The identification of driving behavior using mobile sensing techniques such as smartphone- and vehicle-mounted terminals has gained significant attention in recent years. The present work proposed the monitoring of longitudinal driving behavior using a machine learning approach with the support of an on-board unit (OBU). Specifically, based on velocity, three-axis acceleration and three-axis angular velocity data were collected by a mobile vehicle terminal OBU; through the process of data preprocessing and feature extraction, seven machine learning algorithms, including support vector machine (SVM), random forest (RF), k-nearest neighbor algorithm (KNN), logistic regression (LR), BP neural network (BPNN), decision tree (DT), and the Naive Bayes (NB), were applied to implement the classification and monitoring of the longitudinal driving behavior of probe vehicles. The results show that the three classifiers SVM, RF and DT achieved good performances in identifying different longitudinal driving behaviors. The outcome of the present work could contribute to the fields of traffic management and traffic safety, providing important support for the realization of intelligent transport systems and the improvement of driving safety.
驾驶行为识别可为智能汽车行业和基于探测车辆的交通流量估计提供重要参考。近年来，利用智能手机和车载终端等移动传感技术识别驾驶行为受到了广泛关注。本研究提出了一种基于机器学习方法并借助车载单元（OBU）对纵向驾驶行为进行监测的方法。具体而言，移动车辆终端OBU采集基于速度、三轴加速度和三轴角速度的数据；通过数据预处理和特征提取过程，应用包括支持向量机（SVM）、随机森林（RF）、k近邻算法（KNN）、逻辑回归（LR）、BP神经网络（BPNN）、决策树（DT）和朴素贝叶斯（NB）在内的七种机器学习算法，实现对探测车辆纵向驾驶行为的分类和监测。结果表明，SVM、RF和DT这三种分类器在识别不同纵向驾驶行为方面表现良好。本研究结果可为交通管理和交通安全领域做出贡献，为智能交通系统的实现和驾驶安全性的提高提供重要支持。
