# 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
As the loss of embedding into classification boosted the maximum efficiency of the lane marking method, it was one of the best and most appropriate approaches for continuous lane detection and tracking. Tracking lane during nighttime was one of the most difficult tasks of AVS. [PERSON] et al. solved the issue by developing a vision-based lane detection system, where they pre-processed with a Gabor filter, continuing adaptive splay ROI and Hough transformation to detect the lane marker [97]. Despite lacking an appropriate self-switching system for defining lanes in all circumstances in pre-processing, the detection rates were 97.31% and 98.15% using two clips of Guangzhou where frame numbers were 3274 and 2231. However, the method faced difficulties when tackling bright light reflection, missing lane marks and lane cracks as well. [PERSON] et al. formulated a solution using LaneNet and HNet for the problem of lane detection with an instance segmentation problem in which each lane constituted its own instance to be end-to-end trained [98]. In addition, to a set \"bird's-eye view\", they introduced a learning transfer to the perspective, which was contingent on the image and achieved 96.4% accuracy within 50 fps (frames per second) for the TuSimple dataset. The method was robust enough to adjust the pitch of the ground plane by adapting the transition parameters accordingly, which was the main reason for accurate visualization, and detected lane and lane curves. Moreover, [PERSON] et al. proposed a fast-learning environment using extreme learning CNN (EL-CNN) combining extreme learning machine (ELM) calculating weights among output and hidden layers in one iteration with CNN for lane marking extraction in complex scenarios to overcome computing of large dataset [99]. It reduced training time 1/50 for the KNU dataset, and 1/200 for the Caltech dataset compared to CNN. Experimental results demonstrate that it obtained maximum weights effectively while maintaining performance of 98.9% accuracy applied to the Caltech dataset. In another work, [PERSON] et al. implemented ERFNet with differentiable least-squares fitting (DLSF) for end-to-end lane detection [100]. The approach used dynamic backpropagation to perform an experiment on a lane detection task that demonstrated that, despite the poor supervision signal, the end-to-end approach exceeded a two-step procedure, scoring 95.80% accuracy in 70 fps applied to the TuSimple dataset. The accuracy was not the maximum, but the weight map did not require post processing for accurate lane estimation. [PERSON] et al. proposed a lane detection CNN by self-attention distillation (SAD) which had self-learning ability in the training phase and boosted the visual attention of multiple layers in different networks and increased the efficiency of narrow-lane detection systems [101]. The method obtained 96.64% accuracy in the CULane, BDD100 K and TuSimple datasets, although the hyperparameter adjustment was complicated by an insufficient training process and loss functions. In another work, [PERSON] et al. used a lightweight YOLO network for lane curve detection and tracking with 90.32% precision and 83.76% recall in 50.6 ms [44]. The method was applied to a custom dataset which was evaluated for day and night scenarios. However, the efficiency could be better suited to proper AVS if it solved the interruption of vehicles during lane detection. In conclusion, most of the approaches have performed well enough to be adopted for practical implementation of AVS. However, modified CNN [92] was able to detect lanes with highest accuracy for both day and nighttime, and the CNN-LSTM-based SegNet and UNet combined approach was [93] able to segment roads within the fastest runtime.