# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### Perception
#### 3.1.5 Traffic Scene Analysis
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref.** & **Method** & **Outcomes** & **Advantages** & **Limitations** \\ \hline
[79] & Modified CNN & 95.5\% accuracy for single frame. & Reduced dependency of manual labelling and processing time in single frame. & Deployment for testing did not consider urban or crowded scenarios. \\ \hline
[80] & U-Net & 95.57\% max F-measurement in 0.15 s. & Smooth segmentation of road surface with multiple objects as obstacles. & Huge dependency on manual pre-labeling. \\ \hline
[81] & Multitask CNN and RNN & Max AUC value of 99\%. & Recognized region in complex traffic and visualized spatially distributed cues. & Higher computational cost and inefficient for large field of view. \\ \hline
[82] & Bayesian Model & 91.83\% true positive rate. & Automated curve detection in rural and challenging roads with lower error rate. & Lighting conditions were not considered and slow processing. \\ \hline
[83] & UNet and Kalman Filter & 2.5\% and 9.75\% lateral error generated in 10 ms. & Obtained less lateral error and overcame slow feature extraction. & Limited to a simple close-loop circle in TORCS simulator. \\ \hline
[88] & CondLaneNet & 86.10\% F1 score. & Solved the lane detection in fork and dense scenarios. & Contextual features need to be refined. \\ \hline
[90] & RANSAC & 99.9\% precision and 99.4\% F-measurement. & Prevent misdetection by estimating parameters when illumination changes. & Slower execution, slightly high FPR and did not consider urban traffic road. \\ \hline
[91] & CNN & Obtained highest accuracy (97.85\%). & Outperformed in shadow and roads with obstacles. & Created errors during shifting the ground to predict disappearing point \\ \hline
[92] & Modified CNN & Average 99.25\% accuracy. & Most suitable deployment in two datasets with faster runtime for nine classes & Did not test in real-time driving senses. \\ \hline
[93] & CNN-LSTM with SegNet & 96.78\% accuracy in 46 ms. & Raised performance in occlusion scenarios. & Combined method of CNN and RNN were resource hungry and slow. \\ \hline
[94] & Modified Decision forest and DNN & 98.80\% and 97.45\% accuracy, respectively. & High accuracy in surface and road detection. & High computational cost. \\ \hline
[95] & CNN & 65.2\% mIoU, 79.6\% class average accuracy. & Modified CNN to achieve low complexity and maintained similar accuracy. & Was not tested in crowded traffic environment. \\ \hline
[96] & EL-GAN & 96.39\% accuracy. & The loss of embedding in detector boosted the performance closest to the mark. & Required tuning of huge number of parameters. \\ \hline
[98] & LaneNet and HNet & 96.4\% accuracy within 19 ms. & Did not require post processing, pixel-wise segmentation or fix lane number. & Faced challenges in long field of view while detecting curves. \\ \hline
[99] & EL-CNN & 98.9\% accuracy. & Reduced training time 1/50 and 1/20 in KNU and Caltech datasets, respectively. & Required matrix inversion for better execution time in high dimensional data. \\ \hline
[100] & ERFNet-DLSF & 95.80\% accuracy. & Did not need post processing to estimate line coordinates using weight map. & Was not tested in urban or complex lighting conditions. \\ \hline
[101] & CNN with SAD & 96.64\% accuracy. & Has self-learning ability and increased the efficiency of narrow-lane detection. & Complex hyperparameter adjustment for inadequate training process. \\ \hline
[44] & Lightweight YOLO Network & 90.32\% precision and 83.76\% recall in 50.6 ms. & Applicable in both day and night scenarios and multiple fields of view. & High interruption for obscured vehicles. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of multiple deep learning methods for lane detection and tracking. such as LiDAR and GPS, where most of the standard methods struggled for most of the intersections due to the lack of these attribute labels. Moreover, the method can accurately identify urban intersections with up to 90% accuracy at 113 real-world intersections.