Research Progress on Key Technologies in the Cooperative Vehicle Infrastructure System

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

Abstract

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

CLC Number:

U495

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