Abstract

Keywords

۱٫ Introduction

۲٫ Evolution of self-driving cars

۳٫ Big data and big-sensed data for self-driving cars

۴٫ Deep learning: A subset of artificial intelligence and machine learning

۵٫ Deep reinforcement learning for computer vision in self-driving vehicles

۶٫ Conclusion and future directions

Declaration of competing interest

References

Abstract

This article presents a comprehensive survey of deep learning applications for object detection and scene perception in autonomous vehicles. Unlike existing review papers, we examine the theory underlying self-driving vehicles from deep learning perspective and current implementations, followed by their critical evaluations. Deep learning is one potential solution for object detection and scene perception problems, which can enable algorithm-driven and data-driven cars. In this article, we aim to bridge the gap between deep learning and self-driving cars through a comprehensive survey. We begin with an introduction to self-driving cars, deep learning, and computer vision followed by an overview of artificial general intelligence. Then, we classify existing powerful deep learning libraries and their role and significance in the growth of deep learning. Finally, we discuss several techniques that address the image perception issues in real-time driving, and critically evaluate recent implementations and tests conducted on self-driving cars. The findings and practices at various stages are summarized to correlate prevalent and futuristic techniques, and the applicability, scalability and feasibility of deep learning to self-driving cars for achieving safe driving without human intervention. Based on the current survey, several recommendations for further research are discussed at the end of this article.

۱٫ Introduction

With recent advances in artificial intelligence (AI), machine learning (ML) and deep learning (DL), various applications of these techniques have gained prominence and come to fore. One such application is self-driving cars, which is anticipated to have a profound and revolutionary impact on society and the way people commute [1]. Although, the acceptance and domestication of technology can face initial or prolonged reluctance, yet these cars will mark the first far reaching integration of personal robots into the human society [2]. The last decade has witnessed growing research interest in applying AI to drive cars [3]. Due to rapid advances in AI and associated technologies, cars are eventually poised to evolve into autonomous robots entrusted with human lives, and bring about a diverse socio-economic impact [4]. However, for these cars to become a functional reality, they need to be equipped with perception and cognition to tackle high-pressure real-life scenarios, arrive at suitable decisions, and take appropriate and safest action at all times [5].

Embedded in the self-driving vehicles’ AI are visual recognition systems (VRS) that encompass image classification, object detection, segmentation, and localization for basic ocular performance [6]. Object detection is emerging as a subdomain of computer vision (CV) that benefits from DL, especially convolutional neural networks (CNNs) [7]. This article discusses the self-driving cars’ vision systems, role of DL to interpret complex vision, enhance perception, and actuate kinematic manoeuvres in self-driving cars [8].