# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### Perception  
#### 3.1.1 Vehicle Detection  
The identification and detection of an on-road vehicle for AVS together form one of the predominant and most challenging issues due to versions, combined fast multitasking and visual difficulties. For fast and more accurate vehicle detection and recognition in different and uncertain driving conditions, deep learning algorithms are analyzed in this section. For instance, an online network framework for detecting and tracking vehicles was proposed by [PERSON] et al., who predicted full 3D vehicle bounding box mapping from a monocular camera using both the environment and camera coordinates by reprojecting [34]. Eventually, the framework tracked the movement of instances in a global coordinate system and revised 3D poses with a trajectory approximation of LSTM, implementing on a KITTI dataset, where the outcomes surpassed the outcomes of LiDAR in long range [187]. In a 30 m range, LiDAR obtained 350.50 false negative and the method scored 395.33, while vehicle detection was 11.3% higher, indicating the limitation of the framework. However, it performed better in 50 m and 10 m, where the false negative scores were 857.08 and 1572.33 when the LiDAR-based method obtained false negative values of 1308.25 and 2445.30, respectively. The decreased false negative in 10 and 50 m showed that the method was able to overcome the performance of LiDAR using only camera and deep learning despite reduced accuracy in some real-time implementations. To tackle accuracy-based issues, improve slow detection and recognition speed, and address the lack of categorization ability, [PERSON] et al. introduced a novel YOLOv2_Vehicle architecture [35]. For multiple scales of vehicles that influenced the detection framework, normalization had been used for the improvement of the method of measuring losses for boundary box length and width after clustering and bounding boxes applying k-mean  
Figure 5: Clustering of dataset for subdomains of perception. \(++\) algorithm to the training dataset [36] along with applying multilayer feature fusion to boost the network extraction capabilities and repeatedly eliminating convolutional layers in high layers. The method implementing the BIT dataset could obtain a mean average precision (mAP) exceeding 94.78% in 0.038 s, which was found to be much faster and more accurate than compared existing methods. In another work, an AdaBoost combined with a pixel look-up features-based approach was demonstrated by [PERSON] et al., where the methods included mining orientation, object geometry and occlusion pattern by clustering, and 81.94%, 66.32%, 51.10% accuracy in easy, moderate and hard scenarios, respectively, was obtained for vehicle detection. However, performance decreased when a 70% overlap evaluation threshold was used instead of 50% and during heavy occlusion [37]. Nonetheless, the method was inappropriate for rough conditions as performance decreased when 70% overlap evaluation threshold was used instead of 50%, and it showed poor accuracy in heavy occlusion. Further, [PERSON] et al. presented a new method to identify five distinct vehicle classes, that is, car, van, bus, truck and tractor, using the AdaBoost and CNN algorithms applied to CompCars and their custom dataset containing rear views of vehicles [38]. They employed CNN as a function extractor with a Support Vector Machine (SVM) for training the features separately, and further AdaBoost algorithms were applied for integration. They obtained optimum results even with faulty images and high computing costs, with average accuracy of 99.50% in 0.028 s, which was 13% higher than the other mentioned fusion methods, for instance, SIFT + SVM, HoG + SVM and SURF + SVM. However, the method was only deployed and considered in simple but low-quality images and daytime scenarios. Moreover, one of the biggest issues was the low resolution of images in the real-time traffic surveillance method due to either low-vision RGB cameras or environmental features such as low light condition or foggy weather.