# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 6 Discussion  
Deep learning is fast becoming a successful alternative approach for perception-based AVS as it reduces both cost and dependency on sensor fusion. With this aim in mind, total categories of primary domains of AVS were reviewed in this paper to identify efficient methods and algorithms, their contributions and limitations. From the study, it was found that recent deep learning algorithms obtained high accuracy while detecting and identifying road vehicle types, and in some cases, the results surpassed LiDAR's outcome in both short and long range for 3D bounding vehicles [34]. Moreover, some recent methods such as YOLO V2 [35], deep CNN [38], SINET [41] and Faster R-CNN [42] achieved high accuracy within a very short period of time from low-quality training images to challenging nighttime scenarios. However, there were several limitations, for example, in certain lighting conditions and higher execution costs. Following that, a massive contribution to lane and curve detection along with tracking was presented by studies where 95.5% road scene extraction was demonstrated, for example, in [79], for lane edge segmentation without manual labelling using a modified CNN architecture. As discussed in previous sections, challenges such as higher computational cost [81], insufficient for far field of view [82], not testing in complex scenarios [79] and poor luminance made some proposals tough for practical implementation in present AVS. In addition, a good amount of attention was given to developing safe AVS systems for pedestrian detection. Multiple deep learning approaches such as DNN, CNN, YOLO V3-Tiny, DeepSort R-CNN, single-shot late-fusion CNN, Faster R-CNN, R-CNN combined ACF model, dark channel prior-based SVM, attention-guided encoder-decoder CNN outperformed the baseline of applied datasets that presented a faster warning area by bounding each pedestrian in real time [61], detection in crowded environments, and dim lighting or haze scenarios [62; 72] for position estimation [72], minimizing computational cost and outperforming state-of-the-art methods [120]. The approaches offer an ideal pedestrian method once their technical challenges have been overcome, for example, dependency on preliminary boxing during detection, presumption of constant depths in input image and improvement to avoid missing rate when dealing with a complex environment. Moreover, to estimate steering angles, velocity alongside controlling for lane keeping or changing, overcome slow drifting, take action on a human's weak zone such as a blind spot and decreasing manual labelling for data training, multiple methods, such as multimodal multitask-based CNN [148], CNN with LSTM [149] and ST-LSTM [153], were studied in this literature review for AVS's end-to-end control system. Furthermore, one of the most predominant segments of AVS, traffic scene analysis, was covered to understand scenes from a challenging and crowded movable environment [102], improve performance by making more expensive spatial-feature risk prediction [112] and on-road damage detection [120]. For this purpose, HRNet + contrastive loss [104], Multi-Stage Deep CNN [106], 2D-LSTM with RNN [108], DNN with Hadamard layer [110], Spatial CNN [112], OP-DNN [113] and the methods mentioned in Table 9 were reviewed. However, there are still some limitations, for instance, data dependency or relying on pre-labelled data, decreased accuracy in challenging traffic or at nighttime. Taking into account all taxonomies as features, the decision-making process for AVS was broadly analyzed where driving decisions such as overtaking, emergency braking, lane shifting with collision and driving safety in intersections adopting methods such as deep recurrent reinforcement learning [127], actor-critic-based DRL with DDPG [123], double DQN, TD3, SAC [124], dueling DQN [126], gradient boosting decision tree [133], deep RL using Q-masking and automatically generated curriculum-based DRL [139].