## 4 Conclusions and Policy Suggestions  
Contributions: This study, based on the architecture of the vehicle-road collaborative intelligent system, has constructed a model for the allocation of vehicle-road intelligent capabilities and a cost-sharing model. It has decomposed the intelligent capabilities of the vehicle side and roadside in terms of perception and computation, hardware and software, clarifying the mechanism by which vehicle-side intelligent capabilities are transferred to the roadside within the collaborative intelligent system. The study has built a quantification sub-model for perception capabilities and a matching sub-model for computing power, analyzing the allocation plans for vehicle-road intelligent capabilities to achieve different levels of autonomous driving functions. Different road types have different scenario characteristics. This study constructs a vehicle-road intelligent cost-sharing model and selects Beijing as a case study. Combining statistical data on the mileage scale and usage intensity of different highways and urban roads, as well as forecast data on vehicle ownership and the penetration rate of intelligent vehicles, it calculates the function-cost effectiveness of roadside intelligent schemes for different road types and analyzes the selection of roadside intelligent schemes and the corresponding deployment order for different road types in Beijing at different stages of development. From the perspective of the allocation of vehicle-road intelligent capabilities, the research findings indicate that primary intelligent infrastructure primarily serves the intermediate autonomous driving of ICVs, while intermediate and advanced intelligent  
Figure 17: Revenue and cost analysis of communication carrier in Beijing. infrastructure can better cater to the advanced autonomous driving of ICVs, significantly reducing the cost of perception configurations and computing power on the vehicle side. In terms of the impact of roadside intelligent infrastructure deployment on the intelligent cost of vehicles, the primary intelligent infrastructure can reduce the lifecycle cost of the vehicle-side intelligent scheme for intermediate autonomous driving from \(\Psi\)65,301 to \(\Psi\)37,703. Advanced intelligent infrastructure can reduce the lifecycle cost of advanced autonomous driving from \(\Psi\)126,938 to \(\Psi\)42,180. In the future, with the deployment and utilization of urban cloud platforms, through collaborative planning and decision-making at the city road network level, the lifecycle cost for advanced autonomous driving on the vehicle side will further decrease to \(\Psi\)40,687. The per-vehicle per-kilometer allocation cost will be reduced from \(\Psi\)0.205/km to \(\Psi\)0.197/km. From the perspective of the function-cost effectiveness of intelligent infrastructure for the autonomous driving functions of ICVs, urban roads are generally superior to highways. The order of intelligent upgrading for urban roads is urban expressways, urban main roads, and urban secondary roads, while for highways, it is expressways, class-1 highways, class-2 highways, and class-3 highways. Among them, urban expressways, with the smallest mileage scale and the highest \"function-cost effectiveness,\" can be prioritized for intelligent upgrading on urban roads, directly selecting advanced intelligent infrastructure schemes to support the demonstration and implementation of advanced autonomous driving. In contrast, class-3 highways will only gradually become function-cost effective after deploying advanced intelligent infrastructure after 2035 and can be considered the last objects for intelligent infrastructure upgrading in the urban road network. From the perspective of the external support service, the urban communication operator is also the focus of this study in terms of costs and expected returns.