More generally, machine learning is a broad area and there's no reason to believe that different applications of it will all succeed or all fail for similar reasons. It seems more likely there will be more winners along with many failed attempts.
More generally, machine learning is a broad area and there's no reason to believe that different applications of it will all succeed or all fail for similar reasons. It seems more likely there will be more winners along with many failed attempts.
Yes. I've been saying this for a while. Waymo's approach is about 80% geometry, 20% AI. Profile the terrain, and only drive where it's flat. The AI part is for trying to identify other road users and guess what they will do. When in doubt, assume worst case and stay far away from them.
I was amazed that anyone would try self-driving without profiling the road. Everybody in the DARPA Grand Challenge had to do that, including us, because it was off-road driving and you were not guaranteed a flat road. The Google/Waymo people understood this. Some of the others just tried dumping the raw sensor data into a deep learning system and getting out a steering wheel angle. Not good.
As for Tesla - Tesla isn't even trying to make proper self-driving cars. Tesla's goal has always been assisted driving. However you feel about that, it's really not relevant to the success or failure of self-driving cars.
OP can't possibly have been ignorant of the fact that Waymo is the clear leader here with a substantial head start, and a proven record (and an actual fleet of self driving cars now on the road), and yet he chose not to mention it. That really undermines his credibility for me - he seems clearly more interested in making his point than in accurately engaging with reality.