# 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
Although the algorithm had an independent threshold mechanism to compensate for various light conditions, such as low light, further research was needed to identify lane reflections and shadows. The limitation of [PERSON] et al. was solved in [90], where the authors applied a local adaptive threshold and RANSAC feedback algorithm to prevent misdetection of the lane by estimating two-lane parameter-based issues. Nevertheless, the algorithm did not allow a close-loop lane to maintain lane control while following the road lane, showing a slightly higher false positive rate (FPR) and slow execution for processing in the CPU only. However, it achieved 99.9% precision, 98.9% accuracy and 99.4% F-measurement in 0.677 fps complex visual and lighting conditions. Similarly, for overcoming lane detection in complex shadow and lighting conditions full of obstacles, a CNN-based method was presented by [PERSON] et al. [91]. From an inverse perspective, the application of a fixed transformation matrix generated errors as changes occurred, allowing the predicted exhaust point to infinitely shift upward or downward. The authors trained a neural network with a custom loss function that predicted the transformable matrix parameter valued dynamically. The method was implemented on the TuSimple dataset and obtained high accuracy for insufficient light, shadow, missing lane and normal road compared to other deep learning methods, such as Spatial CNN, CNN-FCN and UNet. As an approach to preventing lighting condition problems for lane detection and tracking, a novel CNN-based model was proposed by [PERSON] et al. [92]. In the pre-processing stage they adopted Yam angle prediction and filtering, followed by segmenting ROIs implementing waveform generation that generated on average 99.25% accuracy in the BIT dataset considering nine cases where day and nighttime accuracies were 99.34% and 98.66%, respectively, as well as a 1.78% average error rate for the Caltech dataset. However, this methodology could be the most suitable candidate only if it is performed with similar outcomes in real-life experiments of level 4 or 5 AVS. A similar CNN-based approach combined with CNN-LSTM, SegNet and UNet was applied by [PERSON] et al. for lane detection from occlusion scenarios [93]. The method obtained 96.78% accuracy for SegNet and 96.46% for UNet within 46 ms, which was much faster than the average of the other methods. With faster processing and high accuracy, this approach could be considered as one of the most acceptable methods for AVS lane detection. [PERSON] et al. proposed a lane-detection algorithm, which calculated the semantic road lane by using the extra tree-based decision forest and DNN from a street scene where hue, saturation, depth (HSD) combined with a deconvolutional network were fine-tuned [94]. In the final stage, a separate extra tree regressor was trained within each lane applying the depths and the manually annotated lane marker locations on the image. The methodology was applied to the TTI and TMD datasets, where it achieved 98.80% and 97.45% accuracy, respectively, for lane detection. Further, encoder-decoder dilated convolution and finely tuned improvements were implemented by [PERSON] et al. to create a modified CNN road lane detection system called Lane Mark Detector (LMD) which increased the accuracy of the CamVid dataset to 65.2%, obtained 79.6% class average accuracy and increased the test speed to 34.4 fps as well as improved the inference time (29.1 ms) and smaller model size of 66 mb [95]. Moreover, [PERSON] et al. used Embedding Loss-Driven Generative Adversarial Networks (EL-GAN) for detecting road lanes [96]. This led to even more secure training with stronger discrimination and stabilized the mechanism of adverse preparation. This significantly stabilized the process of opposing training. EL-GAN was also applied to the TuSimple dataset and achieved 96.39% accuracy despite requiring the tuning of a suitable number of parameters.