# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### Perception
#### 3.1.3 Pedestrian Detection
Moreover, it learnt feature extraction from a low-level image with resolution information and achieved 3.3% lower log-average miss rate than CNN which made the architecture more acceptable for AVS. In another work, [PERSON] et al. implemented an optimized multiclass pedestrian identification system, using a Faster RCNN-based neural network [67]. The analysis indicated that the framework for pedestrian detection in blurred fields of view were ableto increase speed with average precision of 86.6%. This approach could be suitable for distorted images for pedestrian detection tracking. [PERSON] et al. proposed a region proposal framework for pedestrian detection implementing R-CNN combined with an ACF model, where the ACF model was applied to produce only pedestrian class-bounding region, which was a very useful application for autonomous vehicle systems [68]. Moreover, the proposed framework cost less execution time during training and testing. Though most of the studies showed pedestrian detection in the daytime or in clear weather, this task becomes more complex in low-light condition, haze or fog because these create vision difficulties and this kind of condition causes a higher number of accidents [69; 70] and increases the possibility of traffic accidents by 13% [71]. Correspondingly, de-hazing algorithms were one of the solutions to fix vision problems which can be implemented in detection of pedestrians in haze conditions. For instance, a related approach for pedestrian detection in haze conditions was proposed by [PERSON] et al., implementing a synthesized haze version of the INRIA dataset using dark channel prior-based linear SVM and HOG algorithm [72]. Nonetheless, the approach received poor recall value, scoring 67.88% in predicting constant depths of input images. Although the method is a good approach for pedestrian detection, the limitations could be solved taking into account the pre-trained pedestrians' depths in multiple haze environments. Moreover, [PERSON] et al. provided a Laplacian distribution model that featured a combined HTE (haze thickness estimation) and IVR (image visibility restoration) for solving problems of pedestrians [73]. Implementation of this algorithm could enhance the performance for detecting pedestrians in haze conditions, but the most difficult haze conditions occur in dim light conditions, where they achieved 4.1 mAP and 3.98 mAP based on expert and ordinary view, respectively. For alleviating the haze problem while detecting pedestrians, [PERSON] et al. [74] proposed three approaches named Simple-YOLO, VggPrioriBoxes-YOLO and MNPrioriBoxes for pedestrian detection based on YOLO [75]. Deep separable convolution and linear bottleneck capabilities were implemented to minimize parameters and enhanced processing speed, making the network far more usable. The average precision of their three methods was 78.0%, 80.5% and 80.5%, respectively, where precisions were 89.4%, 90.8% and 89.3%. The lowest 22.2 FPS were 22.2, 81.7 and 151.9 applied to the combined data of their HazePerson dataset and INRIA person dataset after dataset augmentation. Although this approach was one of the preferable approaches for AVS to detect and localize pedestrians in day and night haze conditions, the higher missing rate in complex scenarios was an issue which could be resolved by adopting key point detection methods. [PERSON] et al. proposed a ground plane context aggregation network (GPCANet) for detecting pedestrians in ground plane areas of Caltech, SCUT and EuroCity datasets, where the best result was achieved for the SCUT dataset with 96.2% recall value, and obtained 25.29% and 25.10% log average miss rate for the rest of the dataset serially [76]. However, it might have slightly higher log average miss rate, but the outcomes were in crowded traffic complex scenarios, which made the approach more practicable for AVS. Moreover, CNN-based work was demonstrated with only 5.5% miss rate to localize distracting pedestrians [77].