It's really Deep Learning 101.
Furthermore, it's a non-sequitur of an argument.
Dojo is generic neural network training chip. It can be used to speed up any and all Deep Learning problems. Self driving is one such problem.
During AI Day Tesla also presented, in depth, the architecture of the Deep Learning network they use.
If you have a critique of their approach or can show how a competitor's approach is better, then do share with the class. If you don't it's just lazy "Tesla bad because they solved a problem that no-one else solved"
They're not just going to bloat the dataset with straight driving.
The guy from comma.ai says the same thing.
2 - it's not true that "no-one else solved" this problem. If we define this problem as: have a self-driving vehicle which safely navigates its environment and does not endanger its passengers or the people outside. See the Waymo whitepapers: waymo.com/safety/performance-data
Ultimately, even if we agree that what Tesla is working on is orders of magnitude better than what's currently available from them, that might not cut it:
Let's assume that the current obstacle detection is 99% accurate... if the newer versions/improved models are 99.9%, 99.99%, etc. accurate...
we'll still have a 0.1, 0.01, 0.001%, etc of chance of an obstacle not being recognized. Tesla cars are a small fraction of those on the road, and there've been several deaths already. If million of Teslas will be sold, those small percentages will still mean a significant number of accidents and deaths caused by Tesla. If instead a Lidar reliably detects all obstacles, well in advance of the vehicle approaching it (and the car will stop/disable self driving if the Lidar becomes unoperable, e.g. due to bad weather)... it would be irresponsible to persuade huge swaths of the population to let a machine drive the vehicle, without providing the extra safety that a Lidar enables.
To date, Tesla has sold about 2 million cars.
Not sure how many of those support HW3 "FSD capable" computer, but I'd guess 1,5 million.
The FSD software package so far has been rolled out as a beta only, to a few selected users. It is based on vision entriely and aims to properly identify everything on the road. The solid obstacles have all been hit by the previous auto pilot version. In the new cars currently sold, the radar has been replaced by a pure vision implementation close to the FSD software, so it remains to be seen which level of safety can be achieved with that.
Nobody uses radar for lane keeping. It can't see the lane lines.
Pretty much every manufacture now has one and they always use forward facing camera and radar in combination. Of course the radar can't see lanes, but its essential for seen if there are cars ahead and how fast they are.
All of these solution suffer from the same problem of the radar not being good to detect a stationary object. That is where you have to do more with the camera data or add some form of lidar.
I think some 1 car (Audi maybe) use solid state lidar. Tesla has switched to vision only for the most part. No company yet uses active lidar as far as I know.