车路协同系统关键技术研究进展

doi:10.12141/j.issn.1000-565X.230200

华南理工大学学报(自然科学版) ›› 2023, Vol. 51 ›› Issue (10): 46-67.doi: 10.12141/j.issn.1000-565X.230200

所属专题： 2023绿色智慧交通系统专辑

车路协同系统关键技术研究进展

林泓熠¹ 刘洋¹ 李深² 曲小波¹

^1.清华大学车辆与运载学院，北京 100084
^2.清华大学土木水利学院，北京 100084

收稿日期:2023-04-09 出版日期:2023-10-25 发布日期:2023-06-26
通信作者: 刘洋（1991-），男，博士，助理研究员，主要从事机器学习与智能交通系统研究；李深（1989-），男，博士，助理研究员，主要从事智能交通系统与智能车辆研究。 E-mail:thu＿ets＿lab@tsinghua.edu.cn;sli299@tsinghua.edu.cn
作者简介:林泓熠（1999-），男，博士生，主要从事智慧车辆与智能交通系统研究。E-mail:hy-lin22@mails.tsinghua.edu.cn
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

LIN Hongyi¹ LIU Yang¹ LI Shen² QU Xiaobo¹

^1.School of Vehicle and Mobility，Tsinghua University，Beijing 100084，China
^2.School of Civil Engineering，Tsinghua University，Beijing 100084，China

Received:2023-04-09 Online:2023-10-25 Published:2023-06-26
Contact: 刘洋（1991-），男，博士，助理研究员，主要从事机器学习与智能交通系统研究；李深（1989-），男，博士，助理研究员，主要从事智能交通系统与智能车辆研究。 E-mail:thu＿ets＿lab@tsinghua.edu.cn;sli299@tsinghua.edu.cn
About author:林泓熠（1999-），男，博士生，主要从事智慧车辆与智能交通系统研究。E-mail:hy-lin22@mails.tsinghua.edu.cn
Supported by:
the National Natural Science Foundation of China(52220105001)

摘要/Abstract

摘要：

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。

关键词: 车路协同系统, 融合感知, 驾驶认知, 自主决策, 协同控制

Abstract:

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.

Key words: cooperative vehicle infrastructure system, fusion perception, driving cognition, autonomous decision-making, cooperative control

中图分类号:

U495

林泓熠, 刘洋, 李深, 等. 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.

LIN Hongyi, LIU Yang, LI Shen, et al. Research Progress on Key Technologies in the Cooperative Vehicle Infrastructure System[J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(10): 46-67.

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算法类别	算法名称	优点	缺点
传统算法	图搜索法	适合全局路径规划	环境建模复杂，算法收敛速度慢
	曲线插值法	规划的路径较平滑且曲率连续	评价函数的求解速度较慢
	人工势场法	规划的路径平滑且安全	易陷入局部最优，难以描述复杂环境
	群体智能优化法	寻找全局最优的能力强	收敛速度慢，易陷入局部最优
机器学习算法	直接策略学习法	可缓解数据不足导致的各类限制，可修复当前错误	查询效率低，数据收集器不准确，泛化能力较差
	强化学习法	可从反馈中进行连续学习和优化，生成能够适应复杂环境的路径规划策略	需要大量的计算资源和训练时间
	并行学习法	能在虚拟和真实环境中同时进行学习，学习效率高，决策速度快	需要复杂的计算架构和算法设计

算法	优点	缺点
采样方法	适用于高维度非线性问题，不需全局信息和先验知识	需大量采样点，其数量和质量对结果影响大，计算成本高
曲线插值法	可调整路径的形状，计算成本低，适用于实时规划	难处理复杂环境下的规划，依赖初始路径，易出现局部最优解
数值优化法	可处理复杂环境下的路径规划问题，并满足特定需求	计算成本高，不适用于实时规划，对初值的选择较敏感
深度强化学习法	可适应多种环境和任务，结合传感器信息和周围环境来进行规划	需大量数据和计算资源，会出现数据偏差和模型不可解释等问题

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[4]	王振民黄石生薛家祥. 软开关双丝脉冲熔化极活性气体保护焊逆变电源[J]. 华南理工大学学报（自然科学版）, 2006, 34(7): 31-34,59.
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车路协同系统关键技术研究进展

Research Progress on Key Technologies in the Cooperative Vehicle Infrastructure System

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