Deep Learning in Autonomous Vehicles: A Review

Abstract

Autonomous vehicles (AVs) represent a transformative technology that has the potential to revolutionize transportation systems worldwide. Deep learning, a subset of machine learning, plays a crucial role in the development and operation of AVs by enabling perception, decision-making, and control. This paper provides a comprehensive review of the role of deep learning in AVs, covering topics such as object detection, lane detection, traffic sign recognition, localization, mapping, behaviour planning, and control systems. The paper also discusses the challenges and limitations of deep learning in AVs, including data quality and quantity, safety and security concerns, environmental factors, and regulatory and legal challenges. Furthermore, the paper examines recent advancements in deep learning models for AVs, integration with other technologies, potential impacts on society and industry, and future research directions. By providing a detailed overview of deep learning applications in AVs, this review aims to contribute to the ongoing discourse surrounding the development and deployment of autonomous vehicles.