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.

Genetic Optimization for Self-Driving Vehicles Based on Automated Behavior Cloning Convolutional Neural Network

“…His methodology demonstrates that, after several rounds, the virtually produced vehicle creates collisionfree travel and exhibits human-like driving behavior. The systematic review on AVS [15] using deep learning is divided into several modules, including tasks like decision-making, endto-end controlling and prediction, path and motion planning, and augmented reality-based -head-up display while analyzing research works from 2011 to 2021, concentrating on RGB camera vision. M. R. Bachute [16]…”

Research on sensor dirt recognition of autonomous vehicle camera based on deep learning

“…In recent years, intelligent driving vehicles have become one of the critical research areas, focusing on the realization of driverless intelligent transportation to improve the safety and reliability of road traffic [1] . One of the main drivers driving the boom in smart car technology is its ability to help humans overcome driving errors.…”

Research on Path Planning and Path Tracking Control of Autonomous Vehicles Based on Improved APF and SMC

“…Global path planning is carried out throughout the entire map range, planning a rough route for vehicles from the starting point to the endpoint. The classification of common global path planning methods is as follows, graph search-based algorithms such as A* [ 15 ] and Dijkstra [ 16 ], intelligent algorithms such as the genetic algorithm [ 17 ] and particle swarm optimization [ 18 ], and machine learning-based methods such as reinforcement learning [ 19 ] and deep learning [ 20 ]. Local path planning is carried out in the environment surrounding the vehicle’s current location.…”