A survey of ranging techniques for vehicle localization in intelligence transportation system: challenges and opportunities

Author:

Bakhuraisa YaserORCID,Aziz Azlan AbdORCID,Geok Tan KimORCID,Jamian SaifulnizanORCID,Mustakim FajaruddinORCID

Abstract

<span lang="EN-US">Observing the vehicles movement becomes an urgent necessity due to exponentially increasing numbers of vehicles in the world. However, to this regard, a good deal of research had been presented to estimate the exact physical position of the vehicle. The major challenges faced vehicle localization systems are large coverage areas required, positioning at diverse environments and positioning during a high-speed movement. However, in this paper, the challenges of employing the vehicle localization techniques, which rely on the propagation signal properties, are discussed. Moreover, a comparison between these techniques, in terms of accuracy, responsiveness, scalability, cost, and complexity, is conducted. The presented positioning technologies are classified into five categories: satellite based, radio frequency based, radio waves based, optical based, and sound based. The discussion shows that, both of satellite-based technology and cellular-based technology are emerge solutions to overcome the challenges of vehicle positioning. Satellite-based can provide a high accurate positioning in open outdoor environment, whereas the cellular-based can provide accurate and reliable vehicle localization in urban environment, it can support non-line of sight (NLOS) positioning and provide large coverage and high data transmission. The paper also shows that, the standalone localization technology still has limitations. Therefore, we discussed how the presented techniques are integrated to improve the positioning performance.</span>

Publisher

Institute of Advanced Engineering and Science

Subject

Electrical and Electronic Engineering,General Computer Science

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Wireless Ad-Hoc Federated Learning for Cooperative Map Creation and Localization Models;2023 IEEE 9th World Forum on Internet of Things (WF-IoT);2023-10-12

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