# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### Decision Making
\begin{table}
\begin{tabular}{p{71.1 pt} p{71.1 pt} p{71.1 pt} p{71.1 pt} p{71.1 pt}} \hline \hline
**Ref.** & **Method** & **Outcomes** & **Advantages** & **Limitations** \\ \hline
[55] & VGG-19 SegNet & \begin{tabular}{c} Highest 91\% \\ classification accuracy. \\ \end{tabular} & \
\begin{tabular}{c} Efficient in specified scene \\ understanding, reducing the person manipulation. \\ \end{tabular} &
\begin{tabular}{c} Showed false rate for not having \\ high-resolution labelled dataset. \\ \end{tabular} \\ \hline
[102] & Markov Chain & Identify intersections with \
\begin{tabular}{c} Identified intersections from \\ dallenging and crowded \\ urban scenario. \\ \end{tabular} &
\begin{tabular}{c} Independent tractlets caused \\ unpredictable collision in \\ complex scenarios. \\ \end{tabular} \\ \hline
[103] & HRNet & 81.1\% mIoU. & \
\begin{tabular}{c} Able to perform semantic \\ segmentation with high resolution. \\ \end{tabular} &
\begin{tabular}{c} Required huge memory size. \\ \end{tabular} \\ \hline
[104] & HRNet + contrastive loss & 82.2\% mIoU. & Contrastive loss with pixel-to-pixel dependencies enhanced performance. &
\begin{tabular}{c} Did not show success of \\ contrastive learning in limited \\ data-labelled cases. \\ \end{tabular} \\ \hline
[105] & DeepLabV3 and ResNet-50 & \begin{tabular}{c} 79\% mIoU with 50\% less \\ labelled dataset. \\ \end{tabular} & \
\begin{tabular}{c} Reduce dependency on huge labelled \\ data with softmax fine-tuning. \\ \end{tabular} &
\begin{tabular}{c} Dependency on labelled dataset. \\ \end{tabular} \\ \hline
[106] & Multistage Deep CNN & Highest 92.90\% accuracy. & Less model complexity and three times
\begin{tabular}{c} Did not demonstrate for \\ challenging scenes. \\ \end{tabular} \\ \hline
[107] & Fine- and \
\begin{tabular}{c} coarse-resolution CNN \\ \end{tabular} & 13.2\% error rate. & Applicable at different scale. &
\begin{tabular}{c} Multilabel classification from scene \\ was missing. \\ \end{tabular} \\ \hline
[108] & 2D-LSTM with RNN & 78.52\% accuracy. & \
\begin{tabular}{c} Able to avoid the confusion of \\ ambiguous labels by increasing \\ the contrast. \\ \end{tabular} &
\begin{tabular}{c} Sufficed scene segmentation in \\ foggy vision. \\ \end{tabular} \\ \hline
[109] & CDN & \begin{tabular}{c} Achieved 80.5\% \\ mean IoU. \\ \end{tabular} & \
\begin{tabular}{c} Fixed image semantic information and \\ outperformed expressive \\ spatial feature. \\ \end{tabular} &
\begin{tabular}{c} Unable to focus on each object in \\ low-resolution images. \\ \end{tabular} \\ \hline
[110] & DNN with \begin{tabular}{c} Hadamard layer \\ \end{tabular} & 0.65 F1 score, 0.67 precision and 0.64 recall. & \
\begin{tabular}{c} Foresaw road topology with \\ pixel-dense categorization and less \\ computing cost. \\ \end{tabular} &
\begin{tabular}{c} Restrictions by the double-loss \\ function caused difficulties in \\ optimizing the process. \\ \end{tabular} \\ \hline
[111] & \begin{tabular}{c} CNN with \\ pyramid pooling \\ \end{tabular} & \
\begin{tabular}{c} Scored 54.5 mIoU. \\ \end{tabular} &
\begin{tabular}{c} Developed novel image augmentation \\ technique from fisheye images. \\ \end{tabular} & Not applicable for far field of view. \\ \hline
[112] & Spatial CNN & 96.53\% accuracy and \begin{tabular}{c} 68.2\% mIoU. \\ \end{tabular} & \
\begin{tabular}{c} Re-architected CNN for long \\ continuous road and traffic scenarios. \\ \end{tabular} &
\begin{tabular}{c} Performance dropped significantly \\ during low-light and \\ rainy scenarios. \\ \end{tabular} \\ \hline
[113] & OP-DNN & \begin{tabular}{c} 91.1\% accuracy after \\ 7000 iterations. \\ \end{tabular} & \
\begin{tabular}{c} Decreased the issue of overfitting in \\ small-scale training set. \\ \end{tabular} &
\begin{tabular}{c} Required re-weighting for \\ improved result but inapplicable \\ in uncertain environment. \\ \end{tabular} \\ \hline
[114] & CNN and LSTM & 90\% accuracy in 3 s. & \
\begin{tabular}{c} Predict risk of accidents lane merging, \\ tollgate and unsigned intersections. \\ \end{tabular} &
\begin{tabular}{c} Slower computational time and \\ tested in similar kinds of \\ traffic scenes. \\ \end{tabular} \\ \hline
[116] & DNN & 68.95\% accuracy and \begin{tabular}{c} 77\% recall. \\ \end{tabular} & \
\begin{tabular}{c} Determined risk of class from \\ traffic scene. \\ \end{tabular} &
\begin{tabular}{c} Sensitivity analysis was not used \\ for crack detection. \\ \end{tabular} \\ \hline
[117] & \begin{tabular}{c} Graph-Q and \\ DeepScene-Q \\ \end{tabular} & \begin{tabular}{c} Obtained \(p\)-value \\ of 0.0011. \\ \end{tabular} & \
\begin{tabular}{c} Developed dynamic \\ interaction-aware-based scene \\ understanding for AVS. \\ \end{tabular} &
\begin{tabular}{c} Unable to see fast lane result and \\ slow performance of agent. \\ \end{tabular} \\ \hline
[118] & PCA with CNN & \begin{tabular}{c} High accuracy for \\ transverse classification. \\ \end{tabular} & \
\begin{tabular}{c} Identified damages and cracks in the \\ road, without pre-processing. \\ \end{tabular} &
\begin{tabular}{c} Required manual labelling which \\ was time consuming. \\ \end{tabular} \\ \hline
[119] & CNN & \begin{tabular}{c} 92.51\%, 89.65\% recall and \\ F1 score, respectively. \\ \end{tabular} & \
\begin{tabular}{c} Automatic learning feature and tested \\ in complex background. \\ \end{tabular} &
\begin{tabular}{c} Had not performed in real-time \\ driving environment. \\ \end{tabular} \\ \hline
[120] & SegNet and SqueezedNet & \begin{tabular}{c} Highest accuracy (98.93\%) \\ in GAPs dataset. \\ \end{tabular} & \
\begin{tabular}{c} Identified potholes with \\ texture-reliant approach. \\ \end{tabular} &
\begin{tabular}{c} Failed cases due to confusing with \\ texture of the restoration patches. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}