Vehicle Classification (VC) is a key element of Intelligent Transportation Systems (ITS). Diverse ranges of ITS applications like security systems, surveillance frameworks, fleet monitoring, traffic safety, and automated parking are using VC. Basically, in the current VC methods, vehicles are classified locally as a vehicle passes through a monitoring area, by fixed sensors or using a compound method. This paper presents a pervasive study on the state of the art of VC methods. We introduce a detailed VC taxonomy and explore the different kinds of traffic information that can be extracted via each method. Subsequently, traditional and cutting edge VC systems are investigated from different aspects. Specifically, strengths and shortcomings of the existing VC methods are discussed and real-time alternatives like Vehicular Ad-hoc Networks (VANETs) are investigated to convey physical as well as kinematic characteristics of the vehicles. Finally, we review a broad range of soft computing solutions involved in VC in the context of machine learning, neural networks, miscellaneous features, models and other methods.

中文翻译：

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智能交通系统中的车辆分类：概述、方法和软件视角


车辆分类 (VC) 是智能交通系统 (ITS) 的关键要素。安全系统、监控框架、车队监控、交通安全和自动停车等各种 ITS 应用都在使用 VC。目前的VC方法基本上是在车辆经过监控区域时通过固定传感器或使用复合方法对车辆进行本地分类。本文对 VC 方法的现状进行了广泛的研究。我们介绍了详细的 VC 分类法，并探讨了可以通过每种方法提取的不同类型的流量信息。随后，从不同方面研究了传统和前沿的风险投资系统。具体来说，讨论了现有 VC 方法的优点和缺点，并研究了车辆自组织网络 (VANET) 等实时替代方法，以传达车辆的物理和运动学特性。最后，我们在机器学习、神经网络、各种特征、模型和其他方法的背景下回顾了 VC 中涉及的广泛的软计算解决方案。