# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### Perception
#### 3.1.2 Traffic Sign and Light Recognition
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Method** & **Outcomes** & **Advantages** & **Limitations** \\ \hline
[34] & 3D vehicle bounding box mapping & \begin{tabular}{c} 65.52\% more \\ enhancement in 50 m \\ than LiDAR. \\ \end{tabular} & \
\begin{tabular}{c} Exceeded the outcomes of \\ LiDAR in long range. \\ \end{tabular} &
\begin{tabular}{c} Unstable accuracy for certain \\ error properties. \\ \end{tabular} \\ \hline
[35, 36] & \begin{tabular}{c} YOLO V2 and \\ K-mean ++ \\ \end{tabular} & \begin{tabular}{c} mAP 94.78\% in 0.038 s. \\ \end{tabular} & \
\begin{tabular}{c} Faster detection \\ and recognition. \\ \end{tabular} &
\begin{tabular}{c} Trained with vehicle types \\ of data. \\ \end{tabular} \\ \hline
[37] & \begin{tabular}{c} AdaBoost with pixel \\ lookup features \\ \end{tabular} & \begin{tabular}{c} 81.94\% accuracy in \\ best-case scenarios. \\ \end{tabular} & \
\begin{tabular}{c} Improved result with \\ occlusion pattern \\ by clustering. \\ \end{tabular} &
\begin{tabular}{c} Performance decreased \\ when 70\% overlap and \\ heavy occlusion. \\ \end{tabular} \\ \hline
[38] & \begin{tabular}{c} Deep CNN and \\ AdaBoost \\ \end{tabular} & \begin{tabular}{c} 99.50\% accuracy \\ in 0.028 s. \\ \end{tabular} & \
\begin{tabular}{c} Highest accuracy in daytime \\ and trained with \\ low-quality frames. \\ \end{tabular} &
\begin{tabular}{c} Not applicable in low-light \\ or complex scenarios. \\ \end{tabular} \\ \hline
[39] & \begin{tabular}{c} CNN with \\ Caffe framework \\ \end{tabular} & \begin{tabular}{c} 96.47\% accuracy in \\ 51.28 ms. \\ \end{tabular} & \
\begin{tabular}{c} Fast classification in low \\ resolution input. \\ \end{tabular} &
\begin{tabular}{c} Higher execution time \\ compared to other methods. \\ \end{tabular} \\ \hline
[40] & \begin{tabular}{c} ANN and \\ Kalman Filter \\ \end{tabular} & \begin{tabular}{c} Recall 78.48\% and \\ precision 90.78\% in \\ urban scenario. \\ \end{tabular} & \
\begin{tabular}{c} Nighttime taillight-based \\ detection in urban area. \\ \end{tabular} &
\begin{tabular}{c} Decreased in performance \\ for headlight illumination in \\ short distance. \\ \end{tabular} \\ \hline
[41] & SI-NET & \begin{tabular}{c} 90.60\% accuracy in \\ best-case scenarios. \\ \end{tabular} & \
\begin{tabular}{c} Improved the \\ limitation-scale insensitivity \\ of CNN. \\ \end{tabular} &
\begin{tabular}{c} Not applicable in poor \\ lighting, nighttime or \\ complex weather scenarios. \\ \end{tabular} \\ \hline
[42] & \begin{tabular}{c} Faster-RCNN \\ with AGO \\ \end{tabular} & \begin{tabular}{c} 87.2\% average \\ precision in 0.055 s. \\ \end{tabular} & \
\begin{tabular}{c} Enable to detect vehicle in \\ large, medium and \\ short field. \\ \end{tabular} &
\begin{tabular}{c} Not applicable in urban or \\ challenging \\ lighting conditions. \\ \end{tabular} \\ \hline
[43] & \begin{tabular}{c} Faster R-CNN \\ with RPN \\ \end{tabular} & \begin{tabular}{c} Highest 84.4\% accuracy \\ for car detection in \\ 81 ms. \\ \end{tabular} & \
\begin{tabular}{c} Outperformed Faster R-CNN \\ in terms of execution time. \\ \end{tabular} &
\begin{tabular}{c} Unsatisfactory accuracy \\ compared with the obtained \\ execution time. \\ \end{tabular} \\ \hline
[44] & \begin{tabular}{c} Lightweight \\ YOLO Network \\ \end{tabular} & \begin{tabular}{c} 86.81\% and 78.91\% \\ precision and recall \\ within 44.5 ms, \\ respectively. \\ \end{tabular} & \
\begin{tabular}{c} Applicable in both day and \\ night scenarios in \\ multiple FoV. \\ \end{tabular} &
\begin{tabular}{c} Was not tested in urban or \\ crowded environment. \\ \end{tabular} \\ \hline
[45] & \begin{tabular}{c} Quicker \\ region-based CNN \\ \end{tabular} & \begin{tabular}{c} 84.97\% mAP \\ nighttime scenarios. \\ \end{tabular} & \
\begin{tabular}{c} Able to detect in low-light \\ and almost \\ no-light conditions. \\ \end{tabular} &
\begin{tabular}{c} Required huge manual \\ data labelling. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}