## 2 Vehicle-Road Intelligent Capability Allocation Model
### Model Framework  
This section establishes the logical architecture for the allocation of vehicle-road collaborative intelligent capabilities, as shown in Figure 1. Initially, the key elements of vehicle intelligence and roadside intelligence are deconstructed and taken as the research subjects of this study. The intelligent elements on the vehicle side include perception, computation, communication, and actuation, while the roadside intelligent elements encompass perception, computation, and communication. In this context, high-performance communication equipment on the vehicle and roadside communication devices are considered as added support facilities for collaborative intelligence. Intelligent vehicle actuation devices are matched with the level of autonomous driving achieved. By constructing a quantification model for perception capabilities, the perception capabilities of different levels of vehicle-side perception schemes and those combined with different levels of roadside perception schemes are quantified. A vehicle-roadside computational capability matching model is developed, considering factors such as software complexity and perceptual configuration schemes, to quantify the computational capability requirements on both the vehicle and roadside for different vehicle-road collaborative intelligent schemes. Based on this, combinations of vehicle-road intelligent schemes that meet different levels of autonomous driving can be obtained, scientifically resolving the issue of intelligent capability allocation under the vehicle-road collaborative technical route. Finally, taking Beijing as an example and integrating statistical data on the usage intensity of different types of roads, forecast data on the sales volume and stock of ICVs with different levels of intelligence, and data on the cost and energy consumption of intelligent hardware configurations, a vehicle-roadintelligent cost allocation model is constructed. This model explores the deployment plan of roadside intelligent infrastructure with the highest function-cost effectiveness in different road scenarios under different ICV penetration rates.  
For the reader's understanding, an explanation of the classification of autonomous driving levels in this study is provided. Referring to the SAE's (Society of Automotive Engineers) categorization [37], autonomous driving is divided into six distinct levels based on the relationship between humans and vehicles as well as environmental conditions: no automation (L0), driver assistance (L1), partial automation (L2), conditional automation (L3), high automation (L4), and full automation (L5). In this study, the primary intelligent vehicles referred to have L1-L2 autonomous driving capabilities, where the system can perform one or more operations such as steering and acceleration/deceleration, with the remaining driving operations completed by humans. Intermediate intelligent vehicles have L3 autonomous driving capabilities where the system can complete all driving operations under specific conditions and the driver provides appropriate intervention based on the system's request. Advanced intelligent vehicles have L4-L5 autonomous driving capabilities, where the system completes all driving operations, and the driver may not respond to system requests or may not need to intervene.