Artificial Intelligence in Autonomous Driving

September 07, 2016 // By Joachim Langenwalter, NVIDIA
Artificial intelligence (AI) based on deep learning architectures, such as deep neural networks (DNNs), is being applied worldwide in the automotive market to fields like computer vision, natural language processing, sensor fusion, object recognition and autonomous driving projects. Autonomous driving startups, Internet companies and established OEMs are exploring the use of graphical processing units (GPUs) for neural networks to ultimately make cars drive autonomously.

The development of the most advanced driver assistance systems (ADAS) in the industry should be based on integrated and open platforms. A complete solution is required for development, simulation, prototyping, and implementation to enable smarter, more sophisticated ADAS, and to pave the way for the autonomous car. This article summarizes the current status of DNN-based deep learning architectures built on top of a supercomputer on wheels, which are integrated in platforms to drive the future of autonomous vehicles.

What is deep learning?

Deep learning is the most popular approach to develop AI. It is a way to enable machines to recognize and understand the world they are intended to operate in. Neural networks are a collection of simple, trainable mathematical units, which collectively learn complex functions like driving.[3]

Deep learning is the process of turning data into decisions of a computer program. The significant difference to algorithm-based systems is that once the basic model is established, the deep learning system learns on its own how to fulfill the intended tasks.[4] These tasks range from tagging images and understanding spoken languages, to enabling drones to carry out independent missions and empowering cars to drive themselves. Deep learning emulates the way the human brain learns about the world, recognizing patterns and relationships, understanding language and coping with ambiguity.[5]

Neural networks are inherently parallel models. Therefore, they fit very well to multicore GPUs, which can be found across industries such as PCs, robotics and automotive. GPUs take full advantage of this parallelism and are perfectly suited for the definition, training, optimization and deployment of deep learning systems. According to Popular Science, “the GPU is the workhorse of modern A.I.”[6]

ImageNet

A simple example of the progress of deep learning is the ImageNet Large Scale Visual Recognition Challenge. This challenge evaluates algorithms for object detection and image or scene classification from thousands of images and videos at large scale.[7] Until 2012, the rate of

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