Recent Advances in Machine Learning Based Advanced Driver Assistance System Applications

Abstract

In recent years, the rise of traffic in modern cities has demanded novel technology to support the drivers and protect the passengers and other third parties involved in transportation. Thanks to rapid technological progress and innovations, many Advanced Driver Assistance Systems (A/DAS) based on Machine Learning (ML) algorithms have emerged to address the increasing demand for practical A/DAS applications. Fast and accurate execution of A/DAS algorithms is essential for preventing loss of life and property. High-speed hardware accelerators are vital for processing the high volume of data captured by increasingly sophisticated sensors and complex mathematical models’ execution of modern deep learning (DL) algorithms. One of the fundamental challenges in this new era is to design energy-efficient and portable ML-enabled platforms for vehicles to provide driver assistance and safety. This article presents recent progress in ML-driven A/DAS technology to offer new insights for researchers. We covered standard ML models and optimization approaches based on widely accepted open-source frameworks extensively used in A/DAS applications. We have also highlighted related articles on ML and its sub-branches, neural networks (NNs), and DL. We have also reported the implementation issues, bench-marking problems, and potential challenges for future research. Popular embedded hardware platforms such as Field Programmable Gate Arrays (FPGAs), central processing units (CPUs), Graphical Processing Units (GPUs), and Application Specific Integrated Circuits (ASICs) used to implement A/DAS applications are also compared concerning their performance and resource utilization. We have examined the hardware and software development environments used in implementing A/DAS applications and reported their advantages and disadvantages. We provided performance comparisons of usual A/DAS tasks such as traffic sign recognition, road and lane detection, vehicle and pedestrian detection, driver behavior, and multiple tasking. Considering the current research dynamics, A/DAS will remain one of the most popular application fields for vehicular transportation shortly.