## 2 Vehicle-Road Intelligent Capability Allocation Model
### Model Framework
#### 2.2.1 Quantified Assessment Sub-Model of Perception Scheme
Referring to the current industry's performance evaluation of different sensors across various dimensions [39], the evaluation scores are shown in Table 3.  
The implementation of autonomous driving functions primarily relies on environmental perception around the vehicle to carry out path planning, decision-making, and execution. Integrating other relevant studies and mainstream installation methods of onboard sensors [38; 41; 42], this research defines the effective perception area of a vehicle as an ellipse with a major axis of 200 m and a minor axis of 50 m, centered around the vehicle. This area is evenly divided into six sectors at equal angles: forward (Sub-region A), rearward (Sub-region B), left front (Sub-region C), right front (Sub-region D), left rear (Sub-region E), and right rear (Sub-region F), as shown in Figure 4. It is considered that the effective perception area encompasses all the information required for intelligent vehicles to conduct planning and decision-making. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification** & **Facility** & **Perception Distance (m)** & **Field of Version (FOV) (\({}^{\circ}\))** \\ \hline \multirow{3}{*}{Camera} & short-focus (lateral) & 50 & 150 \\ \cline{2-4}  & medium-focus (front/rear) & 120 & 60 \\ \cline{2-4}  & long-focus (front/rear) & 250 & 30 \\ \hline \multirow{3}{*}{Millimeter-wave radar} & long-distance (front/rear) & 200 & 90 \\ \cline{2-4}  & short-distance (corner) & 50 & 120 \\ \hline \multirow{3}{*}{LiDAR} & short-distance (corner) & 100 & 120 \\ \cline{2-4}  & medium-distance (front/rear) & 150 & 120 \\ \cline{1-1} \cline{2-4}  & long-distance (front/rear) & 250 & 120 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Perception range of various sensors. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & **Detail Resolution** & **Dark Mode** &
\begin{tabular}{c} **Pedestrian Recognition** \\ \end{tabular} & **Bad Weather** & **Stability** \\ \hline Camera & 5 & 0.5 & 5 & 1.8 & 2.5 \\ \hline Millimeter-wave radar & 0.5 & 5 & 1 & 5 & 1 \\ \hline LiDAR & 3 & 5 & 4 & 2.5 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation of various sensors. Quantified Assessment of Perception Schemes  
To scientifically evaluate the perceptual capabilities of different vehicle-based perception schemes and vehicle-road collaborative perception schemes, scope, reliability, and stability are adopted as the three key indicators of assessment. The perceptual scope refers to the area covered by the vehicle's actual perception within the effective perception range. Perceptual reliability primarily represents the difference in overall reliability brought about by the coverage of the same perceptual area by different types of perception devices. Additionally, there are differences in the perception dimensions between vehicle-based perception and collaborative perception. Collaborative perception effectively addresses the occlusion blind spot issues inherent in vehicle-based perception. Therefore, perception stability is used to characterize the differences between vehicle-based perception and vehicle-road collaborative perception in this regard. In terms of perception range, based on the perception distances and FOVs of different perception devices outlined in Table 1, the coverage area of various vehicle perception schemes within the respective sub-regions of the effective perception area can be determined. It is assumed that all three roadside perception schemes can achieve full coverage of the vehicle's effective perception area, with differences lying solely in the types of perception devices used. In terms of perception reliability, based on the performance differences of various perception devices across different dimensions as presented in Table 2, the comprehensive perception reliability of different device combinations of the vehicle-side and roadside within each sub-region \(i\) of the effective perception area is calculated, as shown in Equation (1).