# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### Perception
#### 3.1.1 Vehicle Detection
For this problem, vehicles in low-resolution images and videos were analyzed in terms of the efficiency of the CNN by [PERSON] et al. [39]. The neural network used an activation function that worked in two phases: first, the detection of high-level attributes; second, the detection of low-level attributes. It tested the compartment of the model to detect vehicles with lower input resolution at different levels, as well as the number and size of filters. Results demonstrate that CNN was remarkably successful even with low resolution in the identification and classification of vehicles with an average precision fit for real time applications. [PERSON] et al. showed a hierarchical system for detecting and tracking vehicles in an urban area at night based on taillights [40]. The system focused primarily on effective detection and pairing of taillights, considering their innate variety and observing all aspects of the layers and interrelationships in a hierarchical framework which increases the efficiency of vehicle detection and tracking in comparison with traditional methods, with recall of 78.48% and precision of 90.78%. However, performance decreased for short-distance vehicles due to headlight illumination of host vehicles. This approach could be considered as one the most suitable methods for nighttime vehicle detection. [PERSON] et al. demonstrated a novel CNN architecture called scale-insensitive convolutional neural networking (SI-Net) [41] to enhance the performance of vehicle detection for autonomous vehicles, solving the issue of limited CNN-based vehicle detection [39]. The framework improved the limitation-scale insensitive CNN, deploying context-aware region of interest (ROI) pooling to preserve the real structure of small-scale objects. The state-of-the-art method outperformed the others in terms of measuring, scoring 89.6%, 90.60% and 77.75% for accuracy in moderate, easy and complex moods, respectively, in 0.11 s execution time on the KITTI benchmark as well as a custom highway dataset with different variance of scaled objects. Thus, the method was able to maintain a good performance in multiple traffic scenarios. Targeting the runtime of previous works, [PERSON] et al. combined anchor size, receptive field and anchor generation optimization (AGO) with Fast R-CNN to ensure that an acceptable number of vehicle features could be accessed by the network in the shortest amount of time [42]. Using the anchor shape, it efficiently detects vehicles in large, medium and short fields of view with 87.2% average precision in 0.055 s. The anchor shape-based detection process is a very coherent technique for AVS for reducing computational cost by not taking the whole field of vision for processing. In another work, which combined Faster R-CNN training parameters with a region proposal network (RPN)-based approach, [PERSON] et al. implemented vehicle-type detection in a real traffic area including MIT and Caltech datasets with ZF and VGG-16 networks in multiple scenarios [43]. The research results increased the average accuracy of the detection systems and the rate of detection compared with the CNN models. The proposed architecture classified vehicles from three categories where they achieved the best accuracy: 84.4% for car, 83.8% for minibus and 78.3% for SUV using the VGG-16 model in 81 ms. The execution cost of the proposed method outperformed Fast R-CNN and Faster R-CNN applied to complex scenarios. However, a lightweight YOLO network that was built with a YOLO v3 algorithm with generalized IoU loss was combined with loss function as well as with the integration of two different focal length cameras by [PERSON] et al. to indicate less computer complexity for AVS [44]. The method was implemented on their self-made dataset where the network obtained 90.38% precision and 82.87% recall within 44.5 ms. This could be a milestone for AVS in terms of a faster and more accurate method for different field of view and day or nighttime implementation. [PERSON] et al.