# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### Ar-Hud
This tracking method on AR-HUD showed great efficiency and easy adoption capability for vehicle displaying systems. To reduce the accident cases, [PERSON] et al. proposed augmented reality-based head-up display providing more essential surrounding traffic data as well as increasing interactions between drivers and vehicles to enhance drivers' focus on the road [183]. A custom deep CNN architecture was implemented to identify obstacles and final outputs will be projected in the AR head-up display. For AR-based projection in HUD, firstly, pose prediction of targeted ROIs were carried out and obtained 3D coordinates with points after achieving camera projection matrix to recognize AR 3D registration. This step created a 6-DOF pose of translation and rotation parameters which will be helpful for motion estimation calculation with planar homograph. Afterwards, the RANSAC method was applied to compute the homograph matrix, and OpenGL real camera was synchronized with a virtual camera that showed a projection matrix to map 2D points utilizing 3D surface points and developed a marker-less approach. [PERSON] et al. demonstrated an augmented reality-based windshield display system for autonomous vehicle with a view to assisting driving situation awareness in city areas and increase automated driving level from level 4 to 5 [184]. This AR-based windshield display UI was developed based on deep learning-applied object detection to enhance situation awareness, aiming at both clear and lower-visibility conditions where they obtained very different situation awareness scores in low-visibility conditions in disabled windshield display but failed to obtain a good score when windshield UI was enabled. Nevertheless, it worked significantly better in clear weather conditions. [PERSON] et al. presented a 2D histogram of oriented gradient (HOG) tracker and an online support vector machine (SVM) re-detector based on training of the TLD (tracking-learning-detector) functional vehicle tracking system for AR-HUD using equi-height mosaicking image (EHMI) [185]. The system initially performed tracking on the pre-computed 2D HOG EHMI, when the vehicle was identified in the last frame. If the tracking failed, the system started re-detection using an online learning-based SVM classification. The tracking system conducted online learning frequently after the vehicle had been registered and minimized the further calculation necessary for tracking as the HOG descriptor for EHMI was already determined in the detection phase. The technique was perfect for deploying in various lighting and occlusion scenes since it adopted online learning. Refining the algorithm to make optimized hardware or embedded device and to identify other dangerous obstacles effectively in road scenes, this lightweight architecture-based proposed work could be a more acceptable approach for faster tracking and visualizing in HUD. To represent driving situation awareness data, [PERSON] et al. introduced a vehicle augmented-reality system that deducts drivers' distractions with an AR-based windshield of the Genesis DH model from Hyundai motors [186]. The system presented driving conditions and warned a driver using a head-up monitor via the augmented reality. The system included a range of sub-modules, including vehicle and pedestrian recognition based on the deep learning model of [179], vehicle state data, driving data, time to collision (TTC), hazard evaluation, alert policy and display modules. During most experiments, on the basis of TTC values and driver priority, the threat levels and application of augmented EHMI was already determined in the detection phase. In this section, a combination of deep learning algorithms and their outcomes were visualized as the final task of AVS showing them in an AR-based HUD for better driving assistance. AR-HUD was adopted due to visualization in front display for early warning, navigation, object marking by overlapping, ensuring safety and better tracking.