  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.
