## 2 Vehicle-Road Intelligent Capability Allocation Model
### Model Framework
#### 2.2.2 Computing Power Matching Sub-Model
The number of perceptual neural network layers is mainly directly related to the scope of the feature maps collected by the perception scheme and has a smaller correlation with the types of perception devices. Hence, the perceptual convolutional neural network layers corresponding to the roadside perception schemes at different levels are all set to 60. Cameras, millimeter-wave radars, and LiDARs, due to their distinct perceptual principles, result in different amounts of data generated per unit of time. The number of pixels or lines in the sensing devices approximately proportionally affects the amount of data produced per unit time. Table 5 illustrates the data volume generated per unit time by different sensors and the application of these sensors at various levels of autonomous driving vehicle perception schemes, as depicted in Figure 2 [44]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Level of** & **Total Layer of** & **Perception Layer** & **Planning-** & **Volume of** \\
**Autonomous Driving** & **AI Model** & **Conto Layer** & **Training Data** \\ \hline Primary & 10 \(\times\) CNN & 8 \(\times\) CNN & 2 \(\times\) CNN & 1 PB \\ \hline Intermediate & 20 \(\times\) CNN & 16 \(\times\) CNN & 4 \(\times\) CNN & 50–100 PB \\ \hline Advanced & 80 \(\times\) CNN & 64 \(\times\) CNN & 16 \(\times\) CNN & 2 EB \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters of AI model for autonomous driving. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification** & **Facility** &
\begin{tabular}{c} **Amount of Data** \\ **Generated (Mb/s)** \\ \end{tabular} & \multicolumn{2}{c}{**Application**} \\ \hline \multirow{5}{*}{Camera} & 2 MP & 500 & \(\succeq\) & Panoramic/short-focus \\ \cline{2-5}  & & & \(\succ\) & Front/medium-focus of primary and \\  & & & & intermediate perception \\ \cline{1-1}  & & & \(\succ\) & Front and Rear/medium-focus of \\ \cline{1-1}  & & & & advanced perception \\ \cline{1-1} \cline{2-5}  & 12 MP & 3000 & \(\succeq\) & Front/long-focus of intermediate and \\ \cline{1-1} \cline{2-5}  & & & advanced perception \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The data volume generated by various sensors. The AI computing power required for autonomous driving primarily depends on two factors: the perception scheme and the complexity of the software. The perception schemes of vehicles and the roadside directly affect the amount of data generated per unit of time. Equations (7) and (8) calculate the data volume produced per unit time for vehicle and roadside perception schemes, respectively. Furthermore, a computational power matching model is established to quantify the AI computing power needed for vehicles and roadside MEC, as shown in Equation (9). \[Data\_volume_{v}=\sum_{s=1}^{7}data\_volume_{s}\times amount_{s} \tag{7}\]  
\[Data\_volume_{r}=\sum_{s=1}^{7}data\_volume_{s}\times deploy\_density_{s} \tag{8}\]  
\[Computing\_Power=Data\_volume\times Layer\_Almodel\times Conversion\_Ratio \tag{9}\]  
\(s=1,2,\ldots,7\) represents 2 MP camera, 8 MP camera, 12 MP camera, long-distance millimeter-wave radar, short-distance millimeter-wave radar, 64-line LiDAR, and 128-line LiDAR, respectively. \(Data\_volume_{v}\) is the amount of data generated per second by the vehicle's perception scheme _v. data_volume\(s\) is the amount of data generated per second by the sensing device _s. amounts_ is the number of sensing devices \(s\) equipped with the vehicle. \(Data\_volume_{s}\) is the amount of data generated per second by the roadside perception scheme _r. deploy_density_ is the deployment density of roadside sensing devices _s. Layer_Almodel_ is the number of neural network layers in the vehicle or roadside MEC. \(Conversion\_Ratio\) is the conversion factor. By conducting a regression analysis based on the perception schemes and convolutional neural network layers of current mainstream vehicle models, a value of 0.001 TOPs/(CNN*Mbit/s) is adopted for the conversion factor.