Zhang Wenbin, Wang Qi, Suo Chunguang
Department of Automation of Testing and Control, Harbin Institute of Technology, Harbin, China.
MEMS Center, Harbin Institute of Technology, Harbin, China.
Sensors (Basel). 2008 Nov 5;8(11):6952-6971. doi: 10.3390/s8116952.
This paper presents a new vehicle classification and develops a traffic monitoring detector to provide reliable vehicle classification to aid traffic management systems. The basic principle of this approach is based on measuring the dynamic strain caused by vehicles across pavement to obtain the corresponding vehicle parameters - wheelbase and number of axles - to then accurately classify the vehicle. A system prototype with five embedded strain sensors was developed to validate the accuracy and effectiveness of the classification method. According to the special arrangement of the sensors and the different time a vehicle arrived at the sensors one can estimate the vehicle's speed accurately, corresponding to the estimated vehicle wheelbase and number of axles. Because of measurement errors and vehicle characteristics, there is a lot of overlap between vehicle wheelbase patterns. Therefore, directly setting up a fixed threshold for vehicle classification often leads to low-accuracy results. Using the machine learning pattern recognition method to deal with this problem is believed as one of the most effective tools. In this study, support vector machines (SVMs) were used to integrate the classification features extracted from the strain sensors to automatically classify vehicles into five types, ranging from small vehicles to combination trucks, along the lines of the Federal Highway Administration vehicle classification guide. Test bench and field experiments will be introduced in this paper. Two support vector machines classification algorithms (one-against-all, one-against-one) are used to classify single sensor data and multiple sensor combination data. Comparison of the two classification method results shows that the classification accuracy is very close using single data or multiple data. Our results indicate that using multiclass SVM-based fusion multiple sensor data significantly improves the results of a single sensor data, which is trained on the whole multisensor data set.
本文提出了一种新的车辆分类方法，并开发了一种交通监测检测器，以提供可靠的车辆分类，辅助交通管理系统。该方法的基本原理是基于测量车辆通过路面时引起的动态应变，以获取相应的车辆参数——轴距和轴数，进而准确地对车辆进行分类。开发了一个带有五个嵌入式应变传感器的系统原型，以验证分类方法的准确性和有效性。根据传感器的特殊布置以及车辆到达传感器的不同时间，可以准确估计车辆速度，这与估计的车辆轴距和轴数相对应。由于测量误差和车辆特性，车辆轴距模式之间存在大量重叠。因此，直接为车辆分类设置固定阈值往往会导致准确率较低的结果。使用机器学习模式识别方法来处理这个问题被认为是最有效的工具之一。在本研究中，支持向量机（SVM）被用于整合从应变传感器提取的分类特征，以便按照联邦公路管理局的车辆分类指南，将车辆自动分类为从小型车辆到组合卡车的五种类型。本文将介绍测试台和现场实验。两种支持向量机分类算法（一对多、一对一）被用于对单传感器数据和多传感器组合数据进行分类。两种分类方法结果的比较表明，使用单数据或多数据时分类准确率非常接近。我们的结果表明，基于多类支持向量机融合多传感器数据显著提高了单传感器数据的结果，单传感器数据是在整个多传感器数据集上进行训练得到的。
