###### Abstract
Different road types have different scene characteristics and require different intelligent infrastructure schemes, combined with the various usage intensities. This study constructs a vehicle-road intelligent cost-sharing model to guarantee the deployment planning of intelligent infrastructure to be carried out under the premise of meeting function-cost effectiveness, avoiding the waste of road intelligent infrastructure as a social public resource, and achieving a higher level of autonomous driving with the lowest total cost. Beijing has 7.128 million motor vehicles [35], ranking first among Chinese cities. Urban transportation's safety, congestion, environmental, and energy issues are prominent [36], and the demand \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \multicolumn{1}{c}{**Classification**} & \multicolumn{1}{c}{**Literature and Contribution**} & \multicolumn{1}{c}{**Limitation**} \\ \hline \multirow{8}{*}{Micro-level: research on functions and technologies} & [PERSON] conducted a study on the functional distribution and optimization from the perspective of intelligent connected cloud control systems [16]. & \\ \cline{2-3}  & [PERSON] et al. outlined the application scenario requirements and key technology for high-level autonomous driving vehicles in the vehicle-road collaborative system [17]. & Constrained to analysis and optimization of specific functions and technologies involved in collaborative intelligence. \\ \cline{2-3}  & [PERSON] et al. proposed an intelligent transportation plan using collaborative driving strategy [18]. & \\ \cline{2-3}  & [PERSON] et al. established a optimization model for optimizing vehicle trajectories and signal timing at intersections under mixed traffic conditions [19]. & \\ \hline \multirow{8}{*}{Micro-level: research on the layout of certain roadside devices} & Sensors [16; 21; 22; 23; 24]. & \\ \cline{2-3}  & Roadside units [25; 26; 27; 28]. & Weak correlation with the functional requirements of ICVs, especially with autonomous driving. \\ \cline{2-3}  & Edge computing platforms [31; 32]. & \\ \hline \multirow{8}{*}{Macro-level: research on collaborative intelligent system} & The China Highway Society proposed a classification for vehicle-road collaborative autonomous driving systems [33]. & Limited to qualitative analysis. \\ \cline{2-3}  & [PERSON] et al. designed the deployment methods and upgrade routes of intelligent transportation infrastructures by sorting out the demands of ICVs [14]. & Lack of discussion on the perception and computing capabilities required to achieve advanced autonomous driving. \\ \cline{2-3}  & [PERSON] et al. designed the cost evaluation model for the cooperative intelligent system and corresponding comprehensive costs to realize advanced autonomous driving are evaluated [34]. & Lack of functional-cost effectiveness analysis for deploying intelligent infrastructure. \\ \cline{2-3}  & Innovations and Contributions: This paper optimized the allocation of vehicle-road intelligent capabilities to realize different levels of autonomous driving. By constructing the vehicle-road intelligent cost-sharing model, this study guaranteed the deployment planning of intelligent infrastructure on different road types satisfying function-cost effectiveness. The findings can provide a reference for city managers. \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \end{tabular}
\end{table} 
Table 1: Comparison of related studies and literature. for the intelligent transformation of transportation infrastructure is urgent. This study selects Beijing for case analysis, and the findings provide a reference for city managers when making decisions about deploying intelligent infrastructure. This study is organized as follows: Firstly, it combs typical vehicle-side and roadside perception schemes.