# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### Perception
#### 3.1.5 Traffic Scene Analysis
Another scene semantic segmentation approach is the High-Resolution Network (HRNet) proposed by [PERSON] et al. [103], where the method obtained 81.1% mIoU. HRNet linked the high-to-low resolution convolution streams in parallel and transferred data across repeatedly. The advantage of the method was that the resulting representation was richer semantically and spatially. However, it required huge memory size due to high resolution-wise segmentation. Additionally, the same author improved their previous work applying contrastive loss to previous architecture (HRNet), which explored pairwise pixel-to-pixel dependencies applied to the Cityscape dataset and obtained 1.1% higher mIoU [104]. Although the proposed method demonstrated effective performance, which is applicable for top-tier AVS, it was unable to achieve success during contrastive learning in few parts of the labelled dataset. To tackle this issue, [PERSON] et al. [105] presented a contrastive approach following previous research [103; 104] and proposing SoftMax tuning rather than applying contrastive loss and cross-entropy at once. The authors demonstrated three variants of label and pixel-wise contrastive losses by adopting DeepLabV3 with ResNet-50 with 256 channels of convolution layers and bilinear resizing for input resolution for semantic segmentation. This approach showed 79% and 74.6 mIoU, respectively, for Cityscape and PASCAL VOC 2012 datasets but using 50% less labelled dataset. Thus, powerful semantic segmentation with a fine-tuned pretrained method can be a major pathway for higher level AVS for scene analysis. Furthermore, to develop a scene recognition framework, [PERSON] et al. demonstrated GoogleNet for multi-stage feature fusion, named G-MS2F, segmented into three layers to feature extractions and scoring scene understanding, that can be efficiently employed for autonomous driving systems [106]. The framework obtained 92.90%, 79.63% and 64.06% accuracy, respectively, when applied to the Scenel5, MIT67 and SUN397 datasets for image scene recognition. Similarly, a multiresolution convolutional neural network architecture was proposed by [PERSON] et al. for driving scene understanding in different scales where they used two categories of resolution images in the input layer [107]. A combination of fine-resolution CNN and coarse-resolution CNN was included for recording small and comparatively large-scale visual frameworks. To obtain visual information with more accurate resolution and enhanced spatial information, on an inception layer, three convolutional layers were added. They implemented the architecture on the Place365 dataset where the lowest error rate was 13.2%. Moreover, a 2D-LSTM model was proposed to learn information from surrounding context data of scene labels as well as spatial dependencies in [108] within a single model that generated each image's class probabilities. They obtained 78.52% accuracy when deploying on the Standford background dataset. [PERSON] et al. introduced an integrated channel contextual framework and spatial contextual framework as a contextual deconvolution network (CDN) that used both local and global features [109]. In an attempt to optimize the visualization of the semantic data, the decoder network utilized hierarchical supervision for multilevel feature maps in the Cityscapes dataset and achieved 80.5% mean IoU. Following the work, an optimized model of a deep neural network was proposed with two distinct output directions by [PERSON] et al. Their method foresaw road topology along with pixel-dense categorization of images at the same time, and lower computing costs were offered in real-time autonomous applications via a proposed architecture combined with a novel Hadamard layer with element-wise weights using Caffe and achieved 0.65 F1, 0.67 precision and recall 0.64 after fine-tuning the architecture with 10,000 iterations [110].