# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### End-to-End Controlling and Prediction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Method** & **Outcomes** & **Advantages** & **Limitations** \\ \hline
[144] & Hybrid weighted KNN & \begin{tabular}{c} Gained 85.5\% accuracy in \\ 0.223 s in best case. \\ \end{tabular} & \
\begin{tabular}{c} Performed safe control during \\ lane changing in \\ uncertain environment. \\ \end{tabular} &
\begin{tabular}{c} Unsafe driving behavior and \\ did not consider \\ complex conditions. \\ \end{tabular} \\ \hline
[146] & \begin{tabular}{c} CNN + LSTM + \\ State method \\ \end{tabular} & \begin{tabular}{c} Successfully learnt to \\ navigate the track at \\ speeds of up to 7.5 m/s. \\ \end{tabular} & \
\begin{tabular}{c} High-speed driving control \\ and robustness to \\ compound errors. \\ \end{tabular} &
\begin{tabular}{c} Trained only for elliptical \\ racetracks with no \\ other vehicles. \\ \end{tabular} \\ \hline
[147] & CNN with comma.ai & \begin{tabular}{c} Obtained appropriate \\ steering angles with 2.42 \\ mean absolute error. \\ \end{tabular} & \
\begin{tabular}{c} Able to overcome slow drifting \\ from human driving data. \\ \end{tabular} &
\begin{tabular}{c} Improper dataset for \\ practical implementation. \\ \end{tabular} \\ \hline
[148] & \begin{tabular}{c} Multimodal-based \\ CNN \\ \end{tabular} & \begin{tabular}{c} Scored 1.26\({}^{\circ}\) MAE for \\ angles and 0.45\({}^{\circ}\) MAE \\ for velocity. \\ \end{tabular} & \
\begin{tabular}{c} Accurate estimation \\ continuous steering angles \\ and velocity. \\ \end{tabular} &
\begin{tabular}{c} Noise of human factor for \\ speed changes caused \\ lower accuracy. \\ \end{tabular} \\ \hline
[149] & CNN and LSTM & \begin{tabular}{c} Almost similar steering \\ prediction value as \\ ground truth. \\ \end{tabular} & \
\begin{tabular}{c} Resolved the limitation of \\ CNN and blind-spot problem. \\ \end{tabular} &
\begin{tabular}{c} Lack of driving data collection \\ from vehicle. \\ \end{tabular} \\ \hline
[150] & ConvNet & \begin{tabular}{c} 90\% autonomy value and \\ 98\% autonomous \\ approximately. \\ \end{tabular} & \
\begin{tabular}{c} Required fewer training data \\ with no manual \\ decomposition. \\ \end{tabular} &
\begin{tabular}{c} Robustness were not successful \\ in internal processing phase. \\ \end{tabular} \\ \hline
[151] & Deep ConvNet & \
\begin{tabular}{c} 0.033\({}^{\circ}\) and 0.025\({}^{\circ}\) steering \\ angle MAE on GIST and \\ Caltech baseline. \\ \end{tabular} &
\begin{tabular}{c} Considered lane gap and \\ records of previous \\ car driving. \\ \end{tabular} & Only tested on simple cases. \\ \hline
[152] & DDQN & \begin{tabular}{c} 271.13\% better than DQL \\ for speed control. \\ \end{tabular} & \
\begin{tabular}{c} Make optimal control \\ increasing precision and \\ policy efficiency. \\ \end{tabular} &
\begin{tabular}{c} Measured few errors on \\ uneven roads. \\ \end{tabular} \\ \hline
[153] & ST-LSTM Network & \begin{tabular}{c} Obtained 0.0637 RMSE \\ value on Udacity with \\ model weight. \\ \end{tabular} & \
\begin{tabular}{c} Implemented in challenging \\ lighting conditions. \\ \end{tabular} &
\begin{tabular}{c} Only focused on the ground \\ truth of steering angle. \\ \end{tabular} \\ \hline
[154] & Spatiotemporal \\ Conv-LSTM & \begin{tabular}{c} Showed lowest RMSE \\ value 0.0491 to predict \\ steering angles. \\ \end{tabular} & \
\begin{tabular}{c} Overcome the limitation \\ of [153]. \\ \end{tabular} &
\begin{tabular}{c} Did not test in \\ busy environment. \\ \end{tabular} \\ \hline
[156] & DNN & \begin{tabular}{c} Showed on average 40\% \\ less RMSE retrieving \\ steering angles. \\ \end{tabular} & \
\begin{tabular}{c} Predicted steering angles in \\ multiple conditions. \\ \end{tabular} &
\begin{tabular}{c} High developing cost for \\ training individual \\ DNN model. \\ \end{tabular} \\ \hline
[157] & GAN & \begin{tabular}{c} 8\% improvement for \\ Argoverse validation \\ dataset baseline. \\ \end{tabular} & \
\begin{tabular}{c} Better trajectory building \\ layers while motion prediction. \\ \end{tabular} &
\begin{tabular}{c} Had not tested in real time and \\ only used simple semantics. \\ \end{tabular} \\ \hline
[158] & \begin{tabular}{c} CNN and \\ state-transitive LSTM \\ \end{tabular} & \begin{tabular}{c} Predicted 2.58\({}^{\circ}\) and 3.16\({}^{\circ}\) \\ MAE for steering angle. \\ \end{tabular} & \
\begin{tabular}{c} Used current position and \\ subgoal angle for steering \\ angle prediction. \\ \end{tabular} &
\begin{tabular}{c} Slow prediction rate in \\ unknown environment. \\ \end{tabular} \\ \hline
[159] & CNN & \begin{tabular}{c} Achieved 99.5\% and 98.7\% \\ accuracy in urban areas \\ and \\ highways, respectively. \\ \end{tabular} & \
\begin{tabular}{c} Solved lateral controlling using \\ fisheye camera. \\ \end{tabular} &
\begin{tabular}{c} Autonomy dropped while \\ sharp turning. \\ \end{tabular} \\ \hline
[160] & CNN & Achieved 78.5\% accuracy. & \
\begin{tabular}{c} Reduced parameters of CNN \\ to avoid overfitting on \\ data sequence. \\ \end{tabular} &
\begin{tabular}{c} Noticeable performance drop. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}