Artificial Intelligence in Autonomous Driving

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.

By Joachim Langenwalter, NVIDIA, EETimes , Sept. 07, 2016 – 

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]

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