互联与自动移动传感器的选择与布置概览

IF 4.6 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-10-14 DOI:10.1109/OJITS.2024.3481328
Mehmet Kiraz;Fikret Sivrikaya;Sahin Albayrak
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引用次数: 0

摘要

互联与自动驾驶汽车(CAM)中不断发展的技术的整合对实现完全自主交通的进程产生了重大影响。通过提高效率、安全性和环境可持续性,互联与自动驾驶汽车(CAV)有可能彻底改变我们的交通系统。自动班车和公共汽车、智能交通信号、智能乘用车和智能环岛只是正在规划和积极研究的将其集成到交通系统中的技术的几个例子。传感器是实现这一目标的关键。本文将对车载和车外传感器的选择和定位研究进行结构化概述,以实现协同、互联和自动交通。文章介绍了传感器在车辆和基础设施中的一般集成和使用情况,对所研究的内容进行了详细分类,并提出了未来的研究方向,包括量化传感器性能和应对当前趋势的解决方案。本文的研究结果还强调了在创建通用框架方面存在的诸多挑战、新型机器学习方法的应用不足以及多传感器设置建模的复杂性。
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A Survey on Sensor Selection and Placement for Connected and Automated Mobility
The progress towards fully autonomous mobility is significantly impacted by the integration of evolving technologies in connected and automated mobility (CAM). Connected and automated vehicles (CAVs) have the potential to revolutionize our transportation system by improving efficiency, safety, and environmental sustainability. Automated shuttles and public buses, smart traffic signals, intelligent passenger cars, and smart roundabouts are just a few examples of technologies that are being planned and actively researched for integration into transportation systems. Sensors are essential in making this possible. This article provides a structured overview of research on the selection and positioning of sensors on- and off-vehicle to achieve cooperative, connected, and automated mobility. The general integration and usage of sensors in vehicles and infrastructure is described, a detailed taxonomy of the examined research is provided, and future research directions are presented, involving solutions for quantification of sensor performance and addressing current trends. The findings of this article also highlight numerous challenges in creating a universal framework, the lack of application of novel machine learning methods, and the complexity of modeling multi-sensor settings.
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