Deep Learning in the Automotive Industry: Recent Advances and Application Examples

TL;DR

Deep learning has rapidly advanced and is increasingly applied in the automotive industry, enabling innovations like autonomous vehicles, safety systems, and vehicle monitoring through recent technological progress.

Contribution

This paper provides an overview of recent deep learning advances and challenges specifically in automotive applications, highlighting new developments and potential impacts.

Findings

Deep learning models are now integral to automotive safety and autonomous systems.

Applications include self-driving cars, safety functions, and vehicle health monitoring.

Progress driven by data scale, computational power, and algorithmic innovations.

Abstract

One of the most exciting technology breakthroughs in the last few years has been the rise of deep learning. State-of-the-art deep learning models are being widely deployed in academia and industry, across a variety of areas, from image analysis to natural language processing. These models have grown from fledgling research subjects to mature techniques in real-world use. The increasing scale of data, computational power and the associated algorithmic innovations are the main drivers for the progress we see in this field. These developments also have a huge potential for the automotive industry and therefore the interest in deep learning-based technology is growing. A lot of the product innovations, such as self-driving cars, parking and lane-change assist or safety functions, such as autonomous emergency braking, are powered by deep learning algorithms. Deep learning is poised to offer gains in performance and functionality for most ADAS (Advanced Driver Assistance System) solutions. Virtual sensing for vehicle dynamics application, vehicle inspection/heath monitoring, automated driving and data-driven product development are key areas that are expected to get the most attention. This article provides an overview of the recent advances and some associated challenges in deep learning techniques in the context of automotive applications.