## 2 Vehicle-Road Intelligent Capability Allocation Model
### Model Framework
#### 2.2.1 Quantified Assessment Sub-Model of Perception Scheme  
Typical Vehicle-Side and Roadside Perception Scheme  
As the level of autonomous driving increases, intelligent vehicles based on the vehicle intelligence technical route need to significantly increase the quantity and variety of sensors to enhance perceptual reliability in different scenarios. Currently, the integration of multi-sensor perception based on deep learning video streams, LiDAR point clouds, and millimeter-wave data has become the mainstream technology in the perceptual systems of intelligent vehicles [38]. This paper organizes the perceptual hardware configuration schemes for primary (mass production phase), intermediate (mass production phase), and advanced (testing phase) autonomous driving that are commonly adopted in the industry, as shown in Figure 2. The primary perception scheme mainly adopts 5V3R, the intermediate perception scheme mainly adopts 5V5R2L, and the advanced perception scheme mainly adopts 8V8R6L [39]. Figure 1: Intelligent capability allocation model of vehicle-road collaborative intelligent system. Under the vehicle-road collaborative intelligent technology approach, both the vehicle and roadside possess perception capabilities, serving as sources of perceptual information for the vehicle. Vehicles can share perceptual information with surrounding vehicles through V2V communication, and roadside perceptual devices can share perceptual information with vehicles through V2I communication. Referring to the industry group's classification of roadside perception [40], the schemes include primary perception with pure visual coverage, intermediate perception with coverage of both visual and millimeter-wave data, and advanced perception with simultaneous coverage of visual, millimeter-wave, and LiDAR point cloud data, as shown in Figure 3. The specific deployment plan for roadside perceptual devices is related to the road scenario and can refer to previous research on the deployment schemes of intelligent infrastructure for different types of roads [34]. Performance of Various Perception Devices  
Different perception devices have varying perception ranges. Table 2 organizes the average perception distance and field of version (FOV) for various sensors. Cameras mainly include long-focus, medium-focus, and wide-angle cameras. Long-focus and medium  
Figure 3: Perception schemes of roadside. Figure 2: Typical perception schemes of ICVs. focus cameras are primarily used for the forward and rearward perception of vehicles, while wide-angle cameras are mainly applied to the lateral perception. Millimeter-wave radars are primarily divided into long-range and short-range radars, with long-range millimeter-wave radars mainly used for the forward and rearward perception and short-range millimeter-wave radars primarily serving as corner radars installed near the four corners of the vehicle to reduce perceptual blind spots. LiDAR is also categorized into short-range, medium-range, and long-range types, with medium-range and long-range LiDARs mainly used for the forward and rearward perception. Similar to short-range millimeter-wave radars, short-range LiDARs are primarily installed as corner radars near the vehicle's four corners. Additionally, cameras, millimeter-wave radars, and LiDARs each have their own strengths and weaknesses across various dimensions such as detection distance, detail resolution, adverse weather conditions, night mode, and pedestrian recognition. Cameras excel in detail resolution and pedestrian recognition but are greatly affected by dark environments and poor weather. Millimeter-wave radars have the best adaptability in the dark and under adverse weather conditions but fall short in terms of detail resolution and the accuracy of target identification. LiDARs offer the best perceptual stability in complex scenarios but are more significantly impacted by extreme weather conditions.