Faculty of Engineering, School of Civil Engineering, Universiti Teknologi Malaysia, Skudai, Johor 81310, Malaysia.
Department of Civil Engineering, Islamic Azad University, Tabriz 5157944533, Iran.
Sensors (Basel). 2020 Jun 8;20(11):3274. doi: 10.3390/s20113274.
Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle's kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors' knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques.
车辆分类（VC）是智能交通系统中的一种基础方法，广泛应用于交通流量监测、自动化停车系统和安全执法等各种应用中。现有的 VC 方法通常具有局部性，如果目标车辆通过固定传感器、通过短程覆盖监测区域或这两种方法的混合，就可以对车辆进行分类。全球定位系统（GPS）可以提供有关运动特征的可靠全球信息；然而，这些方法缺乏有关车辆物理参数的信息。此外，在现有研究中，智能手机或便携式 GPS 设备被用作提取车辆运动特征的来源，但对于车辆的实时跟踪和分类不可靠。为了解决现有 VC 方法的局限性，研究了潜在的实时状态下识别物理和运动特征的全局方法。车对车自组织网络（VANET）是智能互联车辆的网络，能够以实时方式提供每辆车的类型、速度、方向和位置等交通参数。在本研究中，引入了 VANET 来进行 VC，并从现有文献中介绍了它们可以用于上述目的的功能。据作者所知，这是首次将 VANET 用于 VC 目的的研究。最后，进行了比较，结果表明 VANET 优于传统技术。
