# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### Perception
#### 3.1.4 Lane Detection and Tracking
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Method** & **Outcomes** & **Advantages** & **Limitations** \\ \hline
[60] & DNN & 35.85 on the average miss rate. & \
\begin{tabular}{c} Processed in large field images, continuous detection in complex scenes. \\ \end{tabular} &
\begin{tabular}{c} Higher missing rate with comparatively \\ slowly detection \\ \end{tabular} \\ \hline
[61] & YOLOv3-TINY and DeepSort & 80.3\% accuracy in complex environment. & \
\begin{tabular}{c} Designed faster warning area \\ bounding by each pedestrian \\ with direction labelling. \\ \end{tabular} &
\begin{tabular}{c} Only considered daytime \\ scenarios and \\ lower accuracy. \\ \end{tabular} \\ \hline
[63] & CNN & \begin{tabular}{c} 26\% and 14\% missing \\ rates on two \\ datasets, respectively. \\ \end{tabular} & \
\begin{tabular}{c} Did not require explicit \\ detection in crowded scenario. \\ \end{tabular} &
\begin{tabular}{c} Did not apply for motion \\ images or \\ real-time problems. \\ \end{tabular} \\ \hline
[64] & \begin{tabular}{c} Part-level FCN \\ and CNN \\ \end{tabular} & \begin{tabular}{c} 6.83\% improved \\ outcomes compared \\ with CifarNet. \\ \end{tabular} & \
\begin{tabular}{c} Estimated accurate distances \\ of pedestrians generating the \\ confidence map using FCN. \\ \end{tabular} &
\begin{tabular}{c} High missing rate for \\ practical implementation. \\ \end{tabular} \\ \hline
[65] & \begin{tabular}{c} SSD-based \\ late-fusion CNN \\ \end{tabular} & \begin{tabular}{c} Decreased by more than \\ 10\% of log average \\ miss rate. \\ \end{tabular} & \
\begin{tabular}{c} Most applicable in \\ nighttime implementation. \\ \end{tabular} &
\begin{tabular}{c} Slower detection and \\ complex parameter tuning. \\ \end{tabular} \\ \hline
[66] & \begin{tabular}{c} Resolution \\ aware-based CNN \\ \end{tabular} & \begin{tabular}{c} 3.3\% lower log-average \\ miss rate than CNN. \\ \end{tabular} & \
\begin{tabular}{c} Learnt feature extraction from \\ low-level image with \\ resolution information. \\ \end{tabular} &
\begin{tabular}{c} Was not applied in complex \\ traffic or \\ crowded environment. \\ \end{tabular} \\ \hline
[67] & Faster R-CNN & 86.6\% average precision \\ in 0.104 s. & \
\begin{tabular}{c} Outperformed on distorted \\ and blurry frames. \\ \end{tabular} &
\begin{tabular}{c} Did not consider low-light or \\ traffic scenarios. \\ \end{tabular} \\ \hline
[68] & R-CNN with \\ ACF model & \
\begin{tabular}{c} 14.1\%, 15.3\%, 45.6\% \\ miss-rate on \\ three datasets. \\ \end{tabular} &
\begin{tabular}{c} Reduced the number of region \\ proposals, and costs less time. \\ \end{tabular} & \multicolumn{1}{c}{—} \\ \hline
[72] & \begin{tabular}{c} Dark \\ channel-based SVM \\ \end{tabular} & \begin{tabular}{c} 81.63\% precision and \\ 67.88\% recall. \\ \end{tabular} & \
\begin{tabular}{c} Pedestrian detection and \\ position estimation from \\ haze condition. \\ \end{tabular} &
\begin{tabular}{c} Presumed constant depths in \\ input images. \\ \end{tabular} \\ \hline
[73] & \begin{tabular}{c} Laplacian \\ distribution model \\ \end{tabular} & \begin{tabular}{c} 4.1 mAP and 3.98 mAP \\ on expert and ordinary \\ view, respectively. \\ \end{tabular} & \
\begin{tabular}{c} Pedestrian detection in \\ complex dim-light condition. \\ \end{tabular} &
\begin{tabular}{c} Was not applied in real-time \\ driving scenes. \\ \end{tabular} \\ \hline
[75] & \begin{tabular}{c} Multiple \\ YOLO methods \\ \end{tabular} & \begin{tabular}{c} Highest average \\ precision 80.5\% \\ in 81.7 ms. \\ \end{tabular} & \
\begin{tabular}{c} Minimized number of \\ parameters and outperformed \\ state-of-art methods. \\ \end{tabular} &
\begin{tabular}{c} Early dependency on \\ preliminary boxes during \\ detection process. \\ \end{tabular} \\ \hline
[76] & GPCANet & \begin{tabular}{c} 96.2\% recall in 320 ms on \\ SCUT dataset. \\ \end{tabular} & \
\begin{tabular}{c} Improved outcomes in both \\ day and night including far \\ FoV and crowded traffic. \\ \end{tabular} &
\begin{tabular}{c} Higher log average missing \\ rate for occluded \\ on-road objects. \\ \end{tabular} \\ \hline
[77] & CNN & Showed 5.5\% miss rate. & \
\begin{tabular}{c} Localized distracting \\ pedestrian and improved \\ detection annotations. \\ \end{tabular} &
\begin{tabular}{c} Did not test in cases for \\ crowded or complex scenes. \\ \end{tabular} \\ \hline
[78] & \begin{tabular}{c} CNN cascaded \\ with AdaBoost \\ \end{tabular} & \begin{tabular}{c} Generated the maximum \\ 9\% log-average miss rate. \\ \end{tabular} & \
\begin{tabular}{c} Combined thermal images for \\ nighttime detection. \\ \end{tabular} &
\begin{tabular}{c} Might fail in complex urban \\ traffic scenarios. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}