## 6 Discussion  
Up to now, there is little research on solving traditional problems based on deep learning, and the field of video application over CVIS is still in its infancy. Compared with the traditional methods, the video application method based on deep learning has lower average delay, higher QOE and more accurate detection and is more robust to the complex CVIS environment. Therefore, the use of deep learning algorithms to solve video business problems over CVIS is worthy of extensive research. The development trend of this research in the future is discussed as follows:  
1. In order to restore the real environment and reflect more real data characteristics when building a model based on deep learning, it is a problem worthy of in-depth study to integrate the traffic characteristics such as the high-speed mobility of vehicles and the short life of channel links;
2. Due to the access environment of workshop communication being open, it will face the risk of malicious attacks in the CVIS environment [98; 99]. Therefore, the security of vehicle communication needs to be paid enough attention, and it is necessary to design algorithms based on deep learning to solve network security problems and avoid video content transmission errors, identity authentication failures and network intrusion; \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Accuracy** & **Recall** & **Precision** & **F-Measure** & **Method** \\ \hline [PERSON] [36] & 0.8 & - & - & - & DL \\ [PERSON] [37] & 0.98 & - & - & 0.87 & R-CNN \\ [PERSON] [47] & 0.964 & - & - & - & R-CNN \\ [PERSON] [55] & 0.8 & - & - & - & YOLO \\ [PERSON] [38] & 0.9 & - & - & - & CNN and LSTM \\ [PERSON] [39] & - & - & - & 0.95 & MvRF-CNN \\ [PERSON] [41] & - & - & 0.7364 & - & CNN \\ [PERSON] [43] & - & 0.9949 & - & 0.9704 & CNN \\ [PERSON] [50] & - & - & 0.515 & - & YOLOv3 \\ [PERSON] [48] & - & - & 0.6279 & - & R-CNN \\ [PERSON] [54] & - & - & 0.71 & - & YOLOv2 \\ [PERSON] [52] & - & - & 0.579 & - & YOLOv3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation and characteristics of representative methods based on deep learning. Let ‘-’ indicate that the data was not available. 3. When facing large-scale traffic datasets, the model based on deep learning is slightly insufficient in terms of training speed and precision, so how to improve the training speed on the premise of ensuring precision is a problem that needs to be paid attention, so as to improve the training efficiency of the deep learning model;
4. Because of the single training environment, the deep learning algorithm has poor model transfer in the complex and changeable CVIS environment. Therefore, improving the generalization ability of the deep learning model is also the direction of optimizing the deep learning algorithm. To sum up, firstly, we summarized the current video application methods based on deep learning over CVIS, and then described and analyzed the shortcomings and related problems in the existing research; finally, we discussed the possible development trends of this research in the future. From the simulation results of most studies at present, the deep learning algorithm performs very well compared with the traditional algorithm, which still has the potential for optimization. Therefore, it can be predicted that solving traditional problems through deep learning algorithms will become the mainstream trend in video applications over CVIS. Conceptualization, L.D. ; methodology, B.S. ; validation, L.D, Y.J. and B.S. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Key Research and Development Program of China (2021 YFB2601401), and the Natural Science Foundation of Shaanxi Province, China (2019Q-264). Not applicable. The datasets used were obtained from public open-source datasets from: 1.