###### Abstract
By providing global route planning and decision optimization for ICVs, collaborative intelligence can meet the challenges of multi-vehicle autonomous driving decision-making games in mixed traffic scenarios, improving traffic efficiency while reducing the computing power related to planning and decision. Meanwhile, the collaborative intelligence route means adding the cost of roadside intelligent infrastructure deployment, energy, and maintenance while significantly reducing the relevant cost on the vehicle side. How to combine roadside intelligent infrastructure with vehicle-side intelligent configurations to meet the needs of different levels of autonomous driving and how to deploy roadside intelligent infrastructure cost-effectively at different stages of technological development are issues that warrant the attention of governments and related enterprises. This study is based on this background. Some related studies and literature are listed in Table 1. Currently, most related research focuses on the analysis and optimization of the specific functions and technologies involved in the collaborative intelligence system [16; 17; 18; 19; 20]. [PERSON] conducted a detailed study on the functional distribution from the perspective of intelligent connected cloud control systems and verified it through a real vehicle test platform [16]. [PERSON] et al. outlined the application scenario requirements for high-level autonomous driving vehicles, clarifying the overall architecture, system functions, and performance requirements of the vehicle-road collaborative system [17]. [PERSON] et al. proposed an intelligent transportation plan using a collaborative driving strategy where each vehicle infers traffic conditions based on its own status and information shared by surrounding vehicles and makes decisions based on real-time inferred traffic levels using fuzzy logic methods [18]. [PERSON] et al. established a two-stage optimization model for optimizing vehicle trajectories and signal timing at intersections under mixed traffic conditions to reduce vehicle delays and fleet carbon emissions [19]. Other scholars have conducted research on the layout of certain hardware facilities involved in the upgrade of transportation infrastructure, such as sensors [16; 21; 22; 23; 24], roadside units [25; 26; 27; 28], communication infrastructure [14; 29; 30], and edge computing platforms [31; 32]. These studies generally establish mathematical optimization models for the layout of certain hardware facilities, aiming to find the most cost-effective, energy-efficient, or optimal performance layout plan. However, its combination with the functional requirements of ICVs is not strong. The China Highway Society has proposed a classification for vehicle-road collaborative autonomous driving systems, believing that the same level of collaborative systems can be achieved through different levels of ICVs and intelligent roads [33], but it is more limited to qualitative analysis. [PERSON] et al. designed the deployment methods and upgrade routes of intelligent infrastructures by sorting out the demands of ICVs [14]. In summary, there is little research on the redistribution of vehicle and roadside intelligent capabilities corresponding to satisfying different levels of autonomous driving under the background of collaborative intelligence. In actual industrial practice, there is still a lack of theoretical basis for the prerequisite of function-cost effectiveness for the intelligent transformation of road infrastructure. The purpose of this study is to clarify the substitution logic of intelligent infrastructure for on-board intelligent configuration, re-coordinate the distribution of vehicle-road intelligent capabilities, and analyze the combination and distribution of vehicle-road intelligent configurations for different levels of autonomous driving. The intelligent-related costs of the vehicle side and the roadside under different schemes are also quantitatively analyzed.