# 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

Table 7: Summary of multiple deep learning methods for pedestrian detection. A road scene sectioning framework was adopted by [PERSON] et al. using a CNN-based algorithm to retrieve the 3D scene layout of the street image from noisy labels combining online and offline learning [79]. The proposed method built with color plane fusion and CNN was able to achieve 95.5% to extract a single image of a lane without manual labelling. This CNN-based approach could be considered as the most efficient method for deploying in unknown environments for AVS road-feature extraction. However, for each pixel of the image, which was a path or a lane, authors [PERSON] et al. considered the visual road-detection challenge applying a U-Net-prior network with the DAM (Domain Adaptation Model) to reduce the disparity between the training images and the test image [80]. The proposed model was compared to other state-of-art methods such as RBNet [191], StixeNet II and MultiNet [192], where the max-F measures were 94.97%, 94.88% and 94.88%, respectively, in 0.18 s, 1.2 s and 1.7 s. Their methodology obtained 95.57% max F-measurement in 0.15 s faster and more accurately than others, which indicates that their monocular-vision-based systems achieve high precision for a lower running time. Another kind of approach for storing processes of previous stages, a method based on a combination of CNN and recurrent neural network (RNN), was proposed by [PERSON] et al., which was able to identify lane markers using geometry feedback with maximum 99% AUC value [81]. However, since no image pre-processing was conducted, this process took a lot of time in sorting unrelated image areas. In addition, these methods were either time-consuming or inefficient in a true, dynamic world, which does not fulfil the maximum efficiency restriction of a critical function. The Bayesian method for estimating multihyperbola parameters splitting frames in multiple patches was demonstrated by [PERSON] et al. to recognize curved lanes under difficult conditions using [82]. The lane line was represented on each component by a hyperbola which was determined using the proposed Bayesian hierarchical model with an average of 91.83% true positive rate (TPR) on the ROMA dataset. To sum up the theory, it could be made more practical by adopting sampling techniques such as Hamiltonian schemes to enhance the model's performance. [PERSON] et al. suggested a substitution of image pre-processing to reduce the uncertainty about lane state [83]. Their approach uses profound lane detection based on deep learning as a substitute for practical lane detection with UNet encoder including high-grade GPU processing. The paper also states that the CNN-based UNet with Progressive Probabilistic Hough Transformation, UNet, Kalman filter were far more inefficient in terms of identification than the feature-based approaches, such as Hough Transformation (HOG) for lane tracking in real time [84; 85; 86]. For predicting lane line under the most challenging conditions, a spatiotemporal-based hybrid architecture after encoding-decoding SCNN and ConvLSTM [87]. This is the very first approach which improves temporal correlation with spatial relation of feature extraction with 98.19% accuracy and 91.8% F1 score. However, although this is one of the strongest approaches, the authors did not apply it to complex weather and nighttime scenarios. Furthermore, to resolve instance level and complex fork and dense line-detection issue, a novel approach was implemented, CondLaneNet, using recurrent instance module applied to a CULane dataset [88]. The approach obtained an 86.10% F1 score while detecting curve lane in complex scenarios despite the lack of proper refining of contextual features. Multiple deep learning methods were studied regarding the lane curve tracking system. For instance, [PERSON] et al. deployed circle equation models and parabola equations to redesign the Kalman filter for curved lane tracking with a view to calculating curving parameters in far field view [89].