How Parallel Processing Is Key To AI Learning To Drive

How parallel processing is key to AI learning to drive

- Ian Evenden

The day looms closer when autonomous vehicles will be let loose on our roads, as the legal implications, safety concerns, and technical considerations get ironed out. Movement on the last of those has been swift, with progress from companies such as Google, Tesla, General Motors, and MIT’s Computer Science and Artificial Intelligence Laboratory. Some vehicles, though, are powered by a name familiar to anyone who’s played a game on PC in the past 20 years.

Nvidia’s entry into the autonomous vehicle market might seem to be an odd choice for a company more commonly associated with texture fill rates and polygons-per-second, but it’s the massive parallelism of its processors, as well as their relatively low power requirements, that makes them suitable.

Autonomous vehicle technology is divided into levels, with Level One being something as simple as cruise control, and Level Two taking that a step further with automatic parking or adaptive cruise control that stays a set distance from the car in front. Level Five is a fully robotic car that may not even have a steering wheel, and which can drive itself in areas that don’t have hyperdetailed mapping or any kind of geofencing to help it out. Cars currently available are around Level Two, with anything higher being the domain of Tesla, high-end Audi, or Mercedes, and those used purely for testing.