They make it sound like it's not about driving finesse.
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That’s because self-driving technology is a huge power drain. Some of today’s prototypes for fully autonomous systems consume two to four kilowatts of electricity -- the equivalent of having 50 to 100 laptops continuously running in the trunk, according to BorgWarner Inc. The supplier of vehicle propulsion systems expects the first autonomous cars -- likely robotaxis that are constantly on the road -- will be too energy-hungry to run on battery power alone.
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I call shenanigans.
https://www.nvidia.com/en-us/self-dr...cars/drive-px/
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NVIDIA DRIVE™ PX is the AI car computer that enables automakers, truck makers, tier 1 suppliers, and startups to accelerate production of automated and autonomous vehicles. It scales from a single processor configuration delivering AutoCruise capabilities, to a combination of multiple processors and discrete GPUs designed to drive fully autonomous robotaxis.
The architecture is available in a variety of configurations. These range from one passively cooled mobile processor operating at 10 watts, to a multi-chip configuration with four high performance AI processors — delivering 320 trillion deep learning operations per second (TOPS) — that enable Level 5 autonomous driving.
....
DRIVE PX Xavier will deliver 30 TOPS of performance, while consuming only 30 watts of power. Packed with 7 billion transistors, a single Xavier AI processor will be able to replace today’s DRIVE PX configured with dual mobile SoCs and dual discrete GPUs — at a fraction of the power consumption. Available Q1 of 2018.
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I would expect LIDAR, for instance, also following a development curve. I know the cost has been cut by an order of magnitude recently.
edit:
That's in the short term. Down the road (
) we can apparently blow right past Moore's law with neuromorphic (brainlike) silver wire mesh single-atomic-transistor brainz:
https://www.wired.com/story/brain-built-on-switches/
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Gimzewski believes that the silver wire network or devices like it might be better than traditional computers at making predictions about complex processes. Traditional computers model the world with equations that often only approximate complex phenomena. Neuromorphic atomic switch networks align their own innate structural complexity with that of the phenomenon they are modeling. They are also inherently fast—the state of the network can fluctuate at upward of tens of thousands of changes per second. “We are using a complex system to understand complex phenomena,” Gimzewski said.
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Do you remember Isaac Asimov's Positronic Brain? This is that, in a vegetative state. What happens when it wakes up?