## 2 Vehicle-Road Intelligent Capability Allocation Model
### Model Framework
#### 2.2.2 Computing Power Matching Sub-Model  
The computing chips of intelligent vehicles mainly consist of CPUs, MCUs, and AI processing chips, including NPUs and GPUs. The CPU is primarily responsible for the logical calculations involved in path planning and controls the decision-making of intelligent vehicle; the MCU is in charge of functional safety and vehicle control; and AI processing chips mainly undertake the substantial computational demands of environmental perception and deep learning. As the perception capabilities of intelligent vehicles increase and the complexity of data-driven neural network algorithms soars, AI computing power has become a key factor in determining whether the vehicle's intelligent capabilities can be enhanced. At the same time, the computational requirements and costs of CPUs and MCUs are significantly lower than those of AI processing chips. This paper only discusses the AI computing power required for intelligent vehicles and intelligent infrastructure. \"Cloud-edge-device\" is the current mainstream layout for computing infrastructure, with roadside computing facilities divided into three tiers: the central cloud, the edge cloud, and the mobile edge computers (MEC). The central cloud's applications in the fields of transportation and automotive include traffic model training, full-domain monitoring and overall system status monitoring, city-level route planning and scheduling, and storage and updating of high-precision maps. The edge cloud's applications include information services such as traffic intersection signal information and functional services like planning, decision-making, and precise distribution. The application of MEC includes real-time roadside perception data fusion and dynamic map updating and distribution, where real-time roadside perception data fusion mainly involves the disassembly and integration of video data, LiDAR scanning data, and microwave perception data to form structured data such as traffic flow, vehicle speed, and queue length. Since the cloud platform's services are not limited to the fields of intelligent transportation and autonomous driving but also include government services, city management, etc., its computing power matching is  
Figure 5: Comparison of perception capabilities of various schemes. not discussed in this paper. MECs will serve intelligent driving more, and subsequent modeling and analysis of their computing power matching will be conducted. In terms of vehicle autonomous driving software, the end-to-end AI model has become an industry consensus. It uses sensor data (images, point clouds, radar) as input and outputs control commands (accelerating, braking, steering) for the vehicle. The intermediate process is completed through neural network models (perception network + planning and control network). This paper assumes that automotive companies and related technology companies will develop autonomous driving software according to the end-to-end AI solution. With the improvement of vehicle intelligence, more complex neural network software schemes and larger amounts of data are needed to train the model to handle the needs of multi-source fusion perception and complex decision-making. The number of convolutional neural network layers required for primary, intermediate, and advanced autonomous driving is 10, 20, and 80, respectively [43]. Based on current industry practice, the perception network, as a large network, occupies about 80% of the network layers because of the characteristics of perceptual information and the technical characteristics of the perception network, and the planning and control network, as a small network, accounts for about 20% of the network layers. The specific parameters of the end-to-end AI model are shown in Table 4.  
In terms of roadside MEC software, since roadside perception at different levels achieves full coverage of the road area, the difference lies only in the types of perception devices. Therefore, the scope of the feature maps for roadside perception at all levels is the same.