Artificial Intelligence and Autonomous Vehicles

The use of artificial intelligence (AI) based machine learning technologies in autonomous vehicles is on the rise. Helping to drive this trend is the availability of a new class of embedded AI processors. A good example is NVIDIA's Jetson family, which includes small form factor system on modules (SoMs) that provide GPU-accelerated parallel processing. These high-performance, low-power devices are designed to support the deep learning and computer vision capabilities needed to build software-defined autonomous machines. They derive massive computing capabilities from the use of a parallel processing GPU device with many cores, enabling next-gen computing devices to take on many of the tasks that were historically handled by humans or multiple, traditional computers.

How AI provides navigation and obstacle avoidance for autonomous vehicles on land, air, and sea gets a lot of attention, but machine learning is also being used in other ways on unmanned vehicles. These machine learning applications are usually related to the types of sensors that are on board a particular platform. For example, a number of our customers are using AI engines in unmanned situational awareness airborne applications, taking and processing data from different types of sensors, such as cameras, radars, and lidars, which are used for surveillance and detection, and then report that data back to operators.