# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### Perception
#### 3.1.3 Pedestrian Detection  
Effectively detecting and localizing pedestrians on roads in various scenarios was one of the major vision-based problems for autonomous driving systems. A study shows that only in the USA has the fatality rate for road crossing increased up to 30% in seven years from 2009. In 2016, a total of 6000 pedestrians were killed, which is a record in the last three decades [58]. Moreover, based on vulnerable road users in the ASEN region, 13% of the deaths on roads are related to pedestrians [59]. In order to prevent deaths, the detection and localization of pedestrians have become a major focus of the study of autonomous vehicles. Several studies have been successfully conducted on reducing accident cases and creating a sustainable and more accurate approach to autonomous driving systems. For instance, [PERSON] et al. proposed a deep network named large-field-of-view (LFOV) to perform complex image processing continuously for pedestrian detection and localization [60]. The purpose of the proposed Large-Field-of-View deep network was to understand, simultaneously and effectively, as well as to make classification decisions in many places. The LFOV network processes vast regions at much higher speeds than traditional deep networks and therefore can re-use calculations implicitly. With 280 ms per image on the GPU and 35.85 on the average miss rate on the Caltech Pedestrian Detection Benchmark, the pedestrian detection system showed a promising performance for real-world deployment. A vision based pedestrian detection and pedestrian behavior classification technique was proposed by [PERSON] et al. [61], where YOLOv3-TINY was used for quick segmentation and multitarget tracking of detected pedestrians with the DeepSort algorithm [62]. Finally, to identify the behavior of pedestrians, an improved and customized AlexNet algorithm was adopted. The proposed model performed efficiently in real time at a rate of 20 frames per second along with a designed warning area binding each pedestrian. Convolutional neural network is one of the most popular deep learning models and has been adopted in several studies for pedestrian detection. [PERSON] et al. used a novel CNN architecture model for pedestrian detection [63]. To train the model, they applied transfer learning as well as synthetic images using an uncovered region proposal of a bounding box to avoid the annotation of pedestrians' positions. It obtained a 26% missing rate in CUHK08 and a 14% missing rate in the Caltech pedestrian dataset, where crowded scenes were considered. The biggest advantage was that it required no explicit detection while training and did not need any region proposal algorithm. A similar concept was used by [PERSON] et al., who combined part-level fully convolutional networks (FCN) and CNN to generate a confidence map and pedestrian location based on the aligned bounded box concept [64]. The proposed framework was compared with CifarNet and achieved 6.83% improved outcomes. A novel single shot detector method based on late-fusion CNN architecture was introduced by [PERSON] to analyze data of a multispectral system that performed with higher accuracy at nighttime [65]. The combined architecture was applied to a KAIST multispectral pedestrian benchmark where the late-fusion CNN architectures worked efficiently. In terms of log average miss rate, it decreased by more than 10% and developed for suitable deployment during both day and nighttime. As a result, it became one of the best practical CNN-based pedestrian detectors of all the accepted AVS methods. For identifying pedestrians in low resolution learning from low-level image features, a single image-based novel resolution-aware CNN-based framework was proposed by [PERSON] et al. [66]. The authors also developed a multiresolution image pyramid and obtained the original input image to identify pedestrian size.