## 2 Vehicle-Road Intelligent Capability Allocation Model
### Model Framework
#### 2.2.3 Vehicle-Road Intelligent Capability Allocation Scheme  
The differences among vehicles with different levels of autonomous driving are primarily reflected in the variations in perception, planning, and control capabilities, with the level of vehicle intelligence equated to the representation of integrated perceptual, planning, and control capabilities. Intelligent vehicles of different levels have perceptual hardware configurations with different scopes of perceptual feature maps, which require integration with perceptual neural networks of corresponding layers to leverage the performance of the vehicle's perceptual configuration. Roadside perceptual devices, combined with MEC, can achieve real-time fusion of roadside perceptual data. The roadside perceptual information, obtained through real-time communication between the roadside unit RSU and the onboard unit OBU, can greatly enhance the vehicle's perceptual capabilities, reducing the demand \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multirow{2}{*}{**Facility**} & \multicolumn{2}{c}{**Amount of Data**} & \multirow{2}{*}{**Application**} \\ \cline{3-3} \cline{5-5}  & & **Generated (Mb/s)** & & \\ \hline \multirow{2}{*}{Millimeter-wave radar} & long-distance & 10 & \(\mathbf{\succ}\) & Front/Rear \\ \cline{2-5}  & short-distance & 5 & \(\mathbf{\succ}\) & Corner \\ \hline \multirow{2}{*}{LiDAR} & \multirow{2}{*}{64-line} & \multirow{2}{*}{50} & \(\mathbf{\succ}\) & Front and Rear of intermediate perception \\  & & & \(\mathbf{\succ}\) & Corner of advanced perception \\ \cline{2-5}  & & 128-line & 100 & \(\mathbf{\succ}\) & Front and Rear of advanced perception \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._for vehicle perceptual configuration and the complexity of the corresponding perceptual neural networks. The deployment of roadside RSUs is matched with roadside perceptual and computing devices, meaning one set of roadside perceptual devices is paired with an RSU responsible for the transmission and reception of information. Based on the quantification sub-model for perception capability in Section 2.2.1, the perception capabilities of different vehicle-based perception schemes and vehicle-road collaborative perception schemes can be obtained. In the absence of cloud computing support, since the roadside MEC generally does not participate in the vehicle's planning and control, the onboard central computing platform must independently carry out vehicle planning and control. By matching the corresponding planning and control neural networks on the basis of meeting the perceptual capabilities of the corresponding level of intelligence, the corresponding level of autonomous driving can be achieved. The deployment of cloud platforms can leverage the advantages of abundant computing power and easy scheduling at the roadside and in the cloud, achieving applications such as multi-vehicle collaborative planning and decision-making, as well as road network-level route planning and scheduling. Based on expert interviews, the deployment and application of cloud platforms are expected to reduce the complexity of vehicle planning and control neural networks by 25%, thereby reducing the computing power requirements related to planning and decision-making. Based on the computing power matching sub-model in Section 2.2.2, the MEC required computing power for different roadside perception schemes, as well as the onboard required computing power for achieving a certain level of intelligence under different vehicle-road collaborative intelligence schemes, can be obtained.