## 5 Datasets and Model Performance Evaluation Metrics
### Datasets  
The full name of NGSIM is Next Generation Simulation, which is a data collection project initiated by the Federal Highway Administration that includes data on construction, vehicle trajectory prediction, driver intent recognition, automatic driving decision planning, etc. By  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **State** & **Action** & **Reward** & **Research Content** \\ \hline \multirow{3}{*}{[PERSON] [88]} & Unload target, computing & Whether to uninstall & & Intelligent allocation of \\  & resource information, available & target and computing & & edge computing \\  & bandwidth and vehicle location & resources and available & & resources and \\  & information. & bandwidth allocation. & & channel resources. \\ \hline \multirow{3}{*}{[PERSON] [89]} & Communication rate, energy & & \multirow{3}{*}{} & Implement \\  & consumption, driving direction, & & & blockchain-enabling \\  & content delivery delay and & & & content cache security \\  & storage capacity. & & & and privacy protection. \\ \hline \multirow{3}{*}{[PERSON] [90]} & Preloaded video size, cache & \multirow{3}{*}{} & Quality change, packet & Millimeter wave-based \\  & occupancy, average video & & & loss and video \\  & quality and location information. & & interruption. & \\ \hline \multirow{3}{*}{[PERSON] [91]} & Computing and & \multirow{3}{*}{} & Calculate the uninstall & Task processing delay & Optimization of joint \\  & caching capabilities. & & and service & and energy \\  & caching capabilities. & caching policy. & consumption. & service cache. \\ \hline \multirow{3}{*}{[PERSON] [92]} & Location information, & \multirow{3}{*}{} & Whether to unload, & Quality enhancement, & Video streaming \\  & transmission queue status, & & unload quantity and & avoiding packet loss & transmission scheme \\  & receiving area status, number of & & block quality. & and video freeze. & for mobile-aware \\  & blocks and average quality. & & & vehicle network. \\ \hline \multirow{3}{*}{[PERSON] [93]} & Video data transmission status, & \multirow{3}{*}{} & A combination of the & Download cost, data & Active caching of \\  & local view set status and vehicle & & view set and & loss and video & multi-view 3D video in \\  & association status with RSU. & memory allocation. & freeze cost. & 5G network. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Video application methods over CVIS based on DDPG.  
applying image processing algorithms to recorded traffic flow videos, [PERSON] et al. [38] construct a trajectory database of 18 variables related to vehicle state, and test the performance of its proposed deep learning architecture for predicting future traffic scenarios.  
The COCO dataset contains 80 different object categories, and this dataset is used mainly for object detection, segmentation, etc. [PERSON] et al. [50] used 2 CNN architectures to classify data: YOLOv3.11 (for 11 classes related to traffic congestion) for traffic object recognition; and YOLOv3.6 (for 6 classes related to traffic events).  
[PERSON] et al. [43] tested its intrusion detection methods on its own carefully created automotive Ethernet intrusion dataset. The dataset was recorded in the format of a PCAP file so that it can be viewed using popular programming libraries and packet analyzers.  
[PERSON] et al. [40] tested the performance of the proposed deep learning-assisted ETAN segmentation on the MIO-TCD dataset. The MIO-TCD dataset is a standard benchmark dataset for vehicle localization and identification. The dataset has about 500,000 images of vehicles taken from US roads at different times of the day. From this dataset, we selected 100 images to test the performance of the proposed work.  
[PERSON] et al. [41] used VOC2007, VOC2012 and videos captured in real driving scenarios, which contain 16,551 vehicles, buses, pedestrians, motorcycles and bicycles, to train the model.