# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### Perception
#### 3.1.5 Traffic Scene Analysis
to estimate the incidence of a crash in real time where the dataset comprised historical accident information and combined current traffic and weather information from Attica Tollway, Greece [116]. The method achieved accuracy, precision, recall and AUC of 68.95%, 52.1%, 77% and 64.1%, respectively. The limitation was the transferability while returning the parameters and the absence of good interplay during comparison and insufficiently clarified unexpected heterogeneity. The possible solution offered by the authors was to apply a sensitivity analysis that was not used when applying a binary logistic model in their work to determine risk of crashes. Moreover, [PERSON] et al. proposed a Graph-Q and DeepScene-Q off-policy reinforcement learning-based approach for traffic scene analysis and understanding applied to a custom dataset [117]. The proposed method used dynamic awareness-based scene understanding for AVS, although it was not tested in a real driving environment and was unable to track lanes while moving quickly. With a view to understanding hazardous or damaged roads in a driving situation for a smooth autonomous driving experience, deep learning approaches can also provide a solution. [PERSON] et al. used CNN architecture to identify damages and cracks in the road that reduced false detection and without pre-processing, which helped to decrease computational time [118]. On the other hand, authors adopted a principal component analysis (PCA) method and CNN to classify and sense damaged roads with their own dataset. Another Deep CNN-based approach with discriminative features for understanding road crack identification was developed by [PERSON] et al., which also could be a pathway to implement in AVS [119]. The core advantage of the framework was self-learning features that did not rely on manual labelling and geometrical pavement predictions. An alternative method for autonomous road cracks alongside pothole detection was demonstrated by [PERSON] et al. as part of an analysis of traffic scene [120]. SegNet was applied with texture that relied on features to separate roads from traffic scene in order to build a first division of mask and concatenated with the second masking, which was created with a 2-canny edge algorithm and dilation. Further, SqueezeNet was applied to the GAPS dataset along with being prepared for deployment in a self-driving vehicle. Compared with a similar approach of [PERSON] [119], it achieved higher precision, recall and F1 score, leaving one drawback where it failed to recognize cracked road that was misinterpreted as under-construction surface texture. For this outcome, the method of [PERSON] et al. [120] was a more suitable approach for identifying damage road surface. In summary, deep learning approaches such as fine-resolution CNN and coarse-resolution CNN, 2D-LSTM model RNN, HRNet, Deep CNN, Contextual Deconvolution Network, DNN and CNN with pyramid pooling were analyzed which demonstrated high-accuracy traffic-scene understanding from a crowded movable platform, showed less model complexity, being applicable in different scales, avoiding confusion of ambiguous labels by increasing the contrast among pixels, in some cases developing more expressive spatial features and predicting risk of accident. However, some approaches were limited for implementation because of the requirement of re-weighting, which was inapplicable in uncertain environments, slower computational time, low accuracy and inability to focus on objects in dim light and foggy vision. The overall summary is presented in Table 9.