# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
## 3 Artificial Intelligence
### Deep Learning
#### 3.2.10 Deep Reinforcement Learning (DRL)
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**DL Types** & **Advantages** & **Uses Areas in VANET** & **Issues** \\ \hline \multirow{3}{*}{CNN} & Capable, simple, and highly effective & Accident forecasting involves analyzing videos and multimedia data; managing resources using 5G/6G technology & Implementing CNNs in resource-limited vehicles for assisting vehicular networks is tough \\ \cline{2-4}  & & Utilizing collaboration edge, fog, and cloud technology to exchange information; and improves the identification of barriers and forecasting of mobility & Occurrence of abrupt and disruptive changes in gradients \\ \hline \multirow{3}{*}{LSTM} & Recognize long-term dependencies; capacity to acquire and retain knowledge of extended relationships or connections & Forecasting vehicular traffic for route selection & Complex training increases computational overhead [103] \\ \hline \multirow{3}{*}{GRUs} & Retain knowledge over extended periods and discard useless data & Effectiveness in traffic prediction and intrusion detection; utilizing temporal-based prediction properties to detect prospective threats. & The presence of intricate geographical and temporal correlations in traffic flow data poses a difficult challenge \\ \hline \multirow{3}{*}{Deep AE} & High level of accuracy for identifying malicious activity, i.e., spoofing, flooding, and replay attacks. & Traffic prediction; proficient in conducting attack prevention and identification; able to calculate the velocity of vehicles & Higher processing duration and utilization of resources [104; 105] \\ \hline \multirow{3}{*}{RBMs} & Efficiently encode intricate datasets; removing noise from data & Prediction of network traffic; recognize unauthorized breaches; forecast the duration and area of road accident & Not efficient for modeling natural images \\ \hline \multirow{3}{*}{Deep Belief Encoder} & \multirow{3}{*}{Iterative visualization of features} & Ensuring the security of 6G VANETs; capable of accurately predicting operator sentiments, journey timing & High processing duration and exploitation of resources \\ \hline \multirow{3}{*}{GANs} & Predetermined total iteration defines sample & Minimizes the delay in communication; ability to anticipate the path of a vehicle; enhances the efficiency of infotainment programs & Stability problems during training, interpretability issues \\ \hline \multirow{3}{*}{EDLN} & Facilitates the inclusion of diverse elements and allows for broad application of the model & Capability to identify Distributed Denial of Service (DDoS) attacks and Blockchain attacks; forecast the movement of transportation and offer precise GPS locations to enhance the flow of data & Unable to solve complicated issues related to real-life vehicle situations \\ \hline \multirow{3}{*}{DRL} & address intricate, acquiring knowledge through expe-rience, managing uncer-tainty and noise of network & Traffic management encompasses routing, congestion mitigation, traffic signal optimization, and autonomous vehicle navigation; efficacy and security & Predicated on numerous assumptions that are challenging to realize in practice \\ \hline \hline \end{tabular}
\end{table} 
Table 6: DL methods: primary advantages, issues, and uses in the VANET domain.