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. © 2023 South China University of Technology. All rights reserved.
