# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### Ar-Hud
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Method** & **Outcomes** & **Advantages** & **Limitations** \\ \hline
[164] & DIRL & \begin{tabular}{c} 99\% policy recovery \\ within less data length. \\ \end{tabular} & \
\begin{tabular}{c} Avoid cost function and \\ manual labelling. \\ \end{tabular} &
\begin{tabular}{c} Insufficient training data for \\ stochastic \\ behavior representation. \\ \end{tabular} \\ \hline
[165] & DRL with TTC & \begin{tabular}{c} 28\% faster than \\ TTC method. \\ \end{tabular} & \
\begin{tabular}{c} Solved limitation of \\ rule-based \\ intersection problems. \\ \end{tabular} &
\begin{tabular}{c} Performance decreased three \\ times during \\ challenging situation. \\ \end{tabular} \\ \hline
[166] & IMM with MPC & \begin{tabular}{c} Score 3.128 RMSE after \\ 5 s during \\ motion prediction. \\ \end{tabular} & \
\begin{tabular}{c} Considered motions of \\ surrounding vehicles. \\ \end{tabular} &
\begin{tabular}{c} Lower accuracy in far \\ predicted horizon. \\ \end{tabular} \\ \hline
[167] & Dijkstra and TEB & \begin{tabular}{c} Obtained efficient \\ Euclidean \\ distance 1.41 m. \\ \end{tabular} & \
\begin{tabular}{c} Reach goal with \\ modest error. \\ \end{tabular} &
\begin{tabular}{c} Applicable only if vehicle was \\ out of track in \\ simple scenarios. \\ \end{tabular} \\ \hline
[168] & DNN & \begin{tabular}{c} 21\% safety \\ enhancement adding \\ hazard-avoiding \\ method. \\ \end{tabular} & \
\begin{tabular}{c} Safe navigation adding \\ hazard detection and \\ segmentation method. \\ \end{tabular} &
\begin{tabular}{c} Tested only on simple \\ open highway. \\ \end{tabular} \\ \hline
[169] & Advance RRT & \begin{tabular}{c} Took 5 ms and 48 ms \\ for path selection and \\ path generation. \\ \end{tabular} & \
\begin{tabular}{c} Novel cost function to select \\ path and \\ obstacle-avoiding feature. \\ \end{tabular} &
\begin{tabular}{c} Limited to \\ non-rule-based approach. \\ \end{tabular} \\ \hline
[170,171] & Artificial potential field & \begin{tabular}{c} Visualized potential \\ field in nine \\ different scenarios. \\ \end{tabular} & \
\begin{tabular}{c} Reduce computational cost \\ by estimating \\ convex function. \\ \end{tabular} &
\begin{tabular}{c} Effects of local minimum issue \\ that led AV to be stuck in \\ a position. \\ \end{tabular} \\ \hline
[172] & CNN + LSTM + \begin{tabular}{c} State method \\ \end{tabular} & \begin{tabular}{c} Lower error medium \\ and more centralized \\ error distribution. \\ \end{tabular} & \
\begin{tabular}{c} Vehicle motion planning \\ predicted in multilane \\ turn-based intersection. \\ \end{tabular} &
\begin{tabular}{c} Did not consider traffic light \\ and weather condition for \\ performance evaluation. \\ \end{tabular} \\ \hline
[173] & ADW with MPC & \
\begin{tabular}{c} Reached destination \\ location ignoring the \\ desired orientation. \\ \end{tabular} & Did not require any \\ weighted objective function. &
\begin{tabular}{c} Limitation occurred with \\ constrained kinematics. \\ \end{tabular} \\ \hline
[174] & 3D-CNN & \begin{tabular}{c} Almost 98.5\% average \\ accuracy and \\ stable outcome. \\ \end{tabular} & \
\begin{tabular}{c} Able to learn time-serial \\ features from \\ traffic environment. \\ \end{tabular} &
\begin{tabular}{c} Minimized state after \\ generating overfitting on \\ time-series data. \\ \end{tabular} \\ \hline
[175] & Fuzzy logic & \begin{tabular}{c} 45.6 m path planning \\ with 50.2 s. \\ \end{tabular} & \
\begin{tabular}{c} Human-like control thinking \\ in ambiguous circumstances. \\ \end{tabular} &
\begin{tabular}{c} Difficult to establish a rule \\ base to tackle \\ unstructured conditions. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Summary of multiple deep learning methods for path and motion planning. tification [176]. The limitation of the work was noticed when the observed object moved rapidly and the car suddenly turned; it was visualized with delay.