I’m looking at track record. GM promised Super Cruise, they delivered it works well in the limited settings it is allowed.
So I do believe that when GM officially launches ultra cruise which is similar to Tesla’s FSD, they would have tested the technology and it will work well.
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As for Google or Apple, they can throw more $$$ at it than anyone else, and, that’s as important a metric as any to me as a non-techie. And if someone beats them to it, they can just buy them.
You are (in)correct on all counts. That is why GM has such excellent Battery Management Systems, and why the Bolt has never had a recall, nor had they ever introduced the V8.6.4 and so many more. That is why Boeing produced such superb reusable rockets, narrow body state of the art safety and outstanding mature technology on the 787. If spending more resources on the problem could be useful GM would indeed be leading, Boeing would ahem more orders than Airbus and SpaceX would have failed, not to mention Tesla.
Thoughtful criticism is both responsible and informative. Everyone who's been watching autonomous driving development is acutely aware that there is no practical solution today, and there is no assurance there may be any soon. Nobody really knows when it will be solved.
Things we do know today: 1. sensor incompatibility is a giant problem; 2.Update lags render radar ineffective. 3.. Lidar has exceedingly high sensitivity to precise and permanent labeling, making it both expensive and complex to use without both highly precise and highly accurate environmental data. Both rdar and lidar have some excellent applications but general over the road use is not one of them.
We do not know the limitations of vision based systems nor if the recent advances in utility and affordability will continue to evolve enough to cope with difficult atmospheric problems such as rain, snow, smoke and other pollution. We do not yet know how these systems can be made impervious to major lighting changes,
We do know that vision systems now can use electron microscopes, other visual systems that manage accuracy within a few microns, and rapid improvement in cost effectiveness of visual measurements that benefit from numerous variants of AI-based decision making. These developments demonstrate that eventhe surface of such approaches is far from defined.
I do not represent myself and authoritative in any way. I do claim to have a well defined sense of the obvious. The obvious, in my opinion, is that success will only come from computational advances and sensor improvements. Thus, I would bet on Andrej Karpathy and others like them who understand how to describe their process in terms that common people think they understand. They call these things like "Deep Learning" and "Computer Vision" that help get the funding needed to make faster and faster labeling of more and more obscure event descriptions so that they can train their tools to recognize undesired events faster and more reliability while reducing the risk of making a mistake and not recognizing something correctly. In that ancient statistical terminology, type one and type two errors.
The people who sell lidar and radar is preferred solutions haven't really understood the problem they are trying to solve.
Hence, they can easily make airport shuttle run on tracks and be perfectly Fien with no human driver. That approach can and does land a space vehicle quite perfectly on a space station. They can map the ocean floor. They cannot drive a car, unless it is in a tunnel.
Dojo to the rescue!!
