Cooperative Localization in Transportation 5.0

IF 14 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-03-18 DOI:10.1109/TIV.2024.3377163

Letian Gao;Xin Xia;Zhaoliang Zheng;Hao Xiang;Zonglin Meng;Xu Han;Zewei Zhou;Yi He;Yutong Wang;Zhaojian Li;Yubiao Zhang;Jiaqi Ma

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引用次数: 0

Abstract

In the era of future mobility within Transportation 5.0, autonomy and cooperation across all road users and smart infrastructure stand as the key features to enhance transportation safety, efficiency, and sustainability, supported by cooperative perception, decision-making and planning, and control. An accurate and robust localization system plays a vital role in enabling these modules for future mobility and is constrained by environmental uncertainties and sensing limitations. To achieve precise and resilient localization in this new era, this letter introduces emerging technologies including edge computing, hybrid data-driven and physical model approaches, foundation models as well as parallel intelligence, that are beneficial for next-generation localization systems. On top of these key technologies, by integrating real-world testing and digital twin technology, we further put forward a Decentralized Autonomous Service (DAS)-based cooperative localization framework for future mobility systems to enhance the resilience, robustness, and safety of transportation systems.

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运输中的合作定位 5.0

在未来交通 5.0 时代，在合作感知、决策、规划和控制的支持下，所有道路使用者和智能基础设施的自主与合作是提高交通安全、效率和可持续性的关键特征。准确而稳健的定位系统在实现未来移动性的这些模块中发挥着至关重要的作用，并受到环境不确定性和传感限制的制约。为了在这个新时代实现精确而有弹性的定位，这封信介绍了边缘计算、混合数据驱动和物理模型方法、基础模型以及并行智能等新兴技术，这些技术对下一代定位系统大有裨益。在这些关键技术的基础上，通过整合真实世界测试和数字孪生技术，我们进一步为未来的移动系统提出了基于分散式自主服务（DAS）的合作定位框架，以增强交通系统的弹性、稳健性和安全性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Intelligent Vehicles Mathematics-Control and Optimization

CiteScore

12.10

自引率

13.40%

发文量

177

期刊介绍： The IEEE Transactions on Intelligent Vehicles (T-IV) is a premier platform for publishing peer-reviewed articles that present innovative research concepts, application results, significant theoretical findings, and application case studies in the field of intelligent vehicles. With a particular emphasis on automated vehicles within roadway environments, T-IV aims to raise awareness of pressing research and application challenges. Our focus is on providing critical information to the intelligent vehicle community, serving as a dissemination vehicle for IEEE ITS Society members and others interested in learning about the state-of-the-art developments and progress in research and applications related to intelligent vehicles. Join us in advancing knowledge and innovation in this dynamic field.