# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
###### Abstract  
In the past decade, autonomous vehicle systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. However, the AVS is still far away from mass production because of the high cost of sensor fusion and a lack of combination of top-tier solutions to tackle uncertainty on roads. To reduce sensor dependency and to increase manufacturing along with enhancing research, deep learning-based approaches could be the best alternative for developing practical AVS. With this vision, in this systematic review paper, we broadly discussed the literature of deep learning for AVS from the past decade for real-life implementation in core fields. The systematic review on AVS implementing deep learning is categorized into several modules that cover activities including perception analysis (vehicle detection, traffic signs and light identification, pedestrian detection, lane and curve detection, road object localization, traffic scene analysis), decision making, end-to-end controlling and prediction, path and motion planning and augmented reality-based HUD, analyzing research works from 2011 to 2021 that focus on RGB camera vision. The literature is also analyzed for final representative outcomes as visualization in augmented reality-based head-up display (AR-HUD) with categories such as early warning, road markings for improved navigation and enhanced safety with overlapping on vehicles and pedestrians in extreme visual conditions to reduce collisions. The contribution of the literature review includes detailed analysis of current state-of-the-art deep learning methods that only rely on RGB camera vision rather than complex sensor fusion. It is expected to offer a pathway for the rapid development of cost-efficient and more secure practical autonomous vehicle systems. autonomous controlling; deep learning; decision making; intelligent vehicle; perception; self-driving +
Footnote †: journal: Applied Sciences  
2019, the manufacturing of level 1 to 3 vehicle systems has been achieved but level 4 vehicle systems are in the testing phase [6]. Moreover, it is highly anticipated that autonomous vehicles will be employed to support people in need of mobility as well as reduce the costs and times of transport systems and provide assistance to people who cannot drive [7; 8]. In the past couple of years, not only the autonomous driving academic institutions but also giant tech companies like Google, Baidu, Uber and Nvidia have shown great interest [9; 10; 11] and vehicle manufacturing companies such as Toyota, BMW and Tesla are already working on launching AVSes within the first half of this decade [12]. Although different sensors such as radar, lidar, geodimetric, computer views, Kinect and GPS are used by conventional AVS to perceive the environment [13; 14; 15; 16; 17], it is indeed expensive to equip vehicles with these sensors and the high costs of these sensors are often limited to on-road vehicles [18]. Table 1 shows a comparison of three major vision sensors based on a total of nine factors. While the concept of driverless vehicles has existed for decades, the exorbitant costs have inhibited development for large-scale deployment [19]. To resolve this issue and build a system that is cost efficient with high accuracy, deep learning applied vision-based systems are becoming more popular where RGB vision is used as the only camera sensor. The recent developments in this field of deep learning have accelerated the potential of profound learning applications for the solution of complex real-world challenges [20].