# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### Ar-Hud
The proposed system yet needed to optimize efficiency and acceleration which in diverse vehicle conditions responds robustly to different and high speeds. An analysis showed personal navigation with AR navigation assist equipped for use with a volumetric 3D-HUD and utilizing its parameters. An interface was developed for assisting to turn faster by locating turn points quicker than during regular navigation [177]. The interface also helped to maintain user eyes and to fix them more precisely on the driving environment after analyzing traffic scenes with deep learning algorithm with proper registration of applications via spatial orientation of AR views on interface. On the basis of the results, however, the inadequate perception of the depth of a specified 2D HUD distance is obvious and the navigation system's AR interface was ineffective without a 3D HUD. An automatic AR based on a road tracking information method registration was introduced by [PERSON] et al., with a SIFT matching function and homography measurement method, which defined matching between camera and HUD providing the driver's view was positioned to the front, which detected vehicle and pedestrians and converted them into AR contents after projective transformation [178]. This solution was good enough for daytime performance but had limitations at nighttime. Nevertheless, the procedure had the ability to automate the matching without user interference, but it is inconvenient while projecting outcomes which occurred due to misreading local correspondence. [PERSON] et al. demonstrated an AR-HUD-based driving safety instruction by identifying vehicle and pedestrians using the INRIA dataset [179]. The identification method was built using SVM and HOG with 72% and 74% in fps accuracy and detected partial obstacles, respectively, applying a billboard sweep stereo (BSS) algorithm. The detected vehicles and pedestrians were overlapped on the HUD with the AR technique. Despite detecting obstacles in sunny and rainy scenarios, it was not deployed for nighttime scenarios. In order to integrate outcomes with AR, the system was divided into two parts by [PERSON] et al., 3D object detection and 3D surface reconstruction, to develop object-level 3D reconstruction using Gaussian Process Latent Variable Model (GPLVM) with SegNet and VPNet for in-vehicle augmented reality UI and parking system [180]. Their AR-based visualization system was built with monocular 3D shaping, which was a very cost-efficient model and needed only a single frame in the input layer. Furthermore, a new traffic sign-recognition framework based on AR was constructed by [PERSON] and [PERSON] to overlay traffic signs with more recognizable icons overlapped in an AR-HUD to increase the visualization of a driver aiming to improve safety [181]. The Haar Cascade detector and the verification of the theory using BoVW were combined with the relative spatial data between visual words, which had proven to be a reasonable balance between resource efficiency and overall results. A classifier with an ROI and allocated 3D traffic sign was subsequently developed using a linear support vector machine that required less training and computation time. During the decision-making process, this state-of-the-art methodology influenced the distribution of visual attention and could be more consistent with the improved approach of deep learning recognition relying on the GPU. Addressing the challenge of overtaking an on-road slow vehicle, a see-through effect-based marker-less real-time driving system had been demonstrated by [PERSON] et al., applying AR [182]. To overcome the occlusion and produce a seamless see-through effect, a 3D map of the surroundings was created using an upper-mounted camera and implementing an in-vehicle pose predictor system. With up to 15 FPS, they presented a faster novel real-time 2D-3D tracking strategy for localization of rear in a 3D map. For the purpose of decreasing bandwidth usage, the ROI was switched to the rear car impacted by an occlusion conflict.