Graphics processing for AI in security, monitoring and more: Page 5 of 6

December 13, 2019 // By Andrew Grant
Graphics processing
We have seen a revolution in the field of GPUs (graphics processing units) driven not by making prettier pixels but by artificial intelligence (AI), specifically relating to computer vision and data-driven decision-making.

Automotive: autonomous vehicles

An autonomous vehicle has multiple cameras for computer vision, object recognition, lane warning and driver monitoring, as well as other sensors (e.g. thermal imaging, RADAR and LiDAR) for sensor fusion. Processing at the edge minimises the bandwidth required to move data to and from the vehicle and avoids delays to that analysis. In connectivity blackspots or when latency is critical (e.g. travelling at 70 mph+) then edge processing could literally be the difference between saving a life or not.

In addition, AI and path planning could identify and predict that a child that may walk into the road, thus enabling the vehicle to adapt and slow down, ready to take evasive action. At a simpler level, the automated valet parking will remove from drivers the burden of finding a parking space.

In addition, edge sensors will track water, waste, energy and environmental pollution (redirecting traffic to lessen pollution in specific areas), as well as making homes and workplaces safer and more intelligent.

In the smart city the AIoT will enable ever smarter edge devices to not only be data generators (sensors) but data aggregators, data exchanges and data-driven decision making “brains”. For cars in the city this means spreading the traffic jam or eliminating the jam entirely by enabling cars to be constantly updated by street infrastructure (V2X) and by other vehicles (V2V), with sharing of data allowing better decision-making for routing and safety as well as clearing the path for emergency vehicles to get through.

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