###### Abstract  
The vehicle-road collaborative intelligence approach has become an industry consensus. It can efficiently tackle the technical hurdles and reduce the performance requirements and costs of on-board perception and computing devices. There is a need for in-depth quantitative studies to optimize the allocation of vehicle-road intelligent capabilities for collaborative intelligence. However, current research tends to focus more on qualitative analysis, and there is little research on the redistribution of vehicle and roadside intelligent capabilities. In this paper, we present a model for distributing perception and computing capabilities between vehicle-side and roadside, ensuring to meet the needs of various autonomous driving levels. Meanwhile, the collaborative intelligence approach will also introduce the costs of intelligent infrastructure deployment, energy, and maintenance. Different roads have varying scene characteristics and usage intensities. It is necessary to conduct a cost-effectiveness analysis of the intelligent deployment of different road types. A vehicle-road cost allocation model is developed based on the lifecycle traveled distance of vehicles and the lifecycle traffic flow of various roads to evaluate the function-cost effectiveness. Our study presents several vehicle-road intelligent schemes that meet the needs of various autonomous driving levels and selects Beijing for case analysis. The results indicate that primary intelligent infrastructure can reduce the lifecycle cost of the vehicle-side intelligent scheme for intermediate autonomous driving from V65,301 to V37,703, and advanced intelligent infrastructure can reduce the lifecycle cost for advanced autonomous driving from V126,938 to V42,180. Considering the distributed cost of vehicle-side and roadside, urban roads in Beijing have higher function-cost effectiveness compared to highways, especially urban expressways, which are expected to generate 43.3 times the vehicle-function-cost benefits after the advanced intelligent upgrades. The corresponding research findings can serve as a reference for city managers to make decisions on intelligent road deployment. collaborative intelligence; intelligent vehicle; intelligent infrastructure; intelligent capacity distribution; function-cost effectiveness +
Footnote †: journal: Applied sciences  
**Citation:** [PERSON]; [PERSON]. Intermediate and advanced autonomous driving are still mainly in the testing and regional demonstration stages. Although the commercialization process in simple scenarios such as closed parks and trunk logistics is accelerating, there are still many bottlenecks. Addressing the technical hurdles inherent to a higher level of autonomous driving as well as the economics associated with large-scale application, intelligent connected vehicles (ICVs) are globally acknowledged as the pivotal path for future development, which is powered by the latest generation of information and communication technology [11; 12]. More and more countries and companies are gradually turning to the vehicle-road collaborative intelligent route [13]. Under a collaborative intelligent technical route, ICVs interact with roadside intelligent infrastructure to enhance their environmental perception and make optimized decisions and driving behaviors. Therefore, the intelligent infrastructure should match the functions and technical requirements of ICVs and complete the intelligent upgrade [14]. Intelligent infrastructure can provide vehicles with a wider range of perception data, expand the vehicle's perception range and capabilities, improve safety, and solve a series of \"perception long-tail\" problems such as perception occlusion and extreme weather [15]. Intelligent infrastructure can also reduce the performance requirements for on-board sensing equipment, thereby reducing the on-board computing requirements related to perception.