收稿日期: 2023-04-09
网络出版日期: 2023-06-26
基金资助
国家自然科学基金资助项目(52220105001)
Research Progress on Key Technologies in the Cooperative Vehicle Infrastructure System
Received date: 2023-04-09
Online published: 2023-06-26
Supported by
the National Natural Science Foundation of China(52220105001)
随着城市汽车保有量的稳步增长，道路交通拥堵问题日益凸显，给城市发展带来了巨大压力。为了有效应对这一挑战，开发能够提高交通效率并降低能源消耗的方法显得至关重要。在当前环境下，车路协同系统作为实现绿色智慧交通系统的一种理想选择，可通过整合和优化各种交通资源，实现交通效率的提升和能源消耗的降低，进而为实现“双碳”目标提供了重要技术支持，已成为交通领域研究和实践的重要方向。本文详细解析了车路协同的基本概念、研究方法和应用场景，并深入讨论了其4个核心技术模块：融合感知、驾驶认知、自主决策和协同控制。文章回顾并总结了这些模块中从传统方法到最新的深度强化学习方法的研究成果，并深入探讨了这些技术和方法在提升交通效率、降低能源消耗和增强道路安全性方面的应用潜力。最后，文章剖析了车路协同系统在实际应用中可能遇到的诸多挑战，如信息传输的安全性、系统的稳定性、环境的复杂性等。为了克服这些挑战，文章从开发整合车端和路端信息的数据集、提升多源感知信息的融合精度、增强车路协同系统的实时性和安全性与优化复杂条件下多车协同决策控制的方法等4个方面展望了未来的发展方向。因此，本文不仅对于车路协同技术的进一步发展具有重要的参考价值，也对于城市交通系统的未来规划和建设具有重要的指导意义。
林泓熠 , 刘洋 , 李深 , 曲小波 . 车路协同系统关键技术研究进展[J]. 华南理工大学学报(自然科学版), 2023 , 51(10) : 46 -67 . DOI: 10.12141/j.issn.1000-565X.230200
With the steady growth of urban car ownership, the issue of traffic congestion is becoming increasingly prominent, bringing great pressure to urban development. To respond effectively to this challenge, it is critical to develop methods that can improve transport efficiency and reduce energy consumption. In current context, the Cooperative Vehicle Infrastructure System (CVIS), an ideal solution for realizing green and intelligent transportation systems, has become an important direction in both transportation research and practice. By integrating and optimizing various traffic resources, CVIS not only enhances traffic efficiency and reduces energy consumption but also provides key technical support for achieving “dual carbon” goals. This paper thoroughly analyzed the fundamental concepts, research methodologies and application scenarios of CVIS, and delved into its four core technological modules: fusion perception, driving cognition, autonomous decision-making, and cooperative control. The paper reviewed and summarized research achievements within these modules, ranging from traditional methods to the latest in deep reinforcement learning techniques. It also explored the potential applications of these technologies and methods for enhancing traffic efficiency, reducing energy consumption, and improving road safety. Finally, the paper scrutinized numerous challenges that CVIS may encounter in practical applications, including the security of information transmission, system stability, and environmental complexity. To overcome these challenges, the paper looked forward to the future development in four areas: developing datasets that integrate vehicle-side and roadside information, enhancing the fusion accuracy of multi-source perception information, improving the real-time performance and safety of CVIS, and optimizing multi-vehicle cooperative decision-making control methods under complex conditions. As a result, this paper not only has important reference value for the advancement of CVIS technology, but also provides important guidance for the future planning and construction of urban transportation systems.
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