It is definitely a clever marketing pitch, as there is plenty of evidence to back up that LIDAR makes self-driving cars significantly safer. However, despite the hype, Teslas aren't really self driving cars at the moment, so it seems an acceptable commercial decision wrapped up in a clever sales pitch.

That's also true for high resolution maps. The question is whether you're solving for self-driving on highways or a handful of mapped city centers or whether you want to solve for the real thing. Tesla is all-in on FULL self driving, and most other companies are betting on driver assistance or gps-locked self-driving. If Tesla can get FSD to work in the next couple of years then they're vindicated. If FSD requires a weak form of generalized intelligence (plausible) then FSD isn't happening anytime soon and investing in more sensors and GPS maps is correct.

High resolution maps do not give you an accurate 3D representation of nearby objects.

Our brains do an amazing job interpreting high resolution visual data and analyzing it both spatially and temporally. Our brains then take that first analysis and apply a secondary, experiential, analysis to further interpret it into various categories relevant to the current activity.

What I’ve seen from Tesla so far indicates to me that FSD shouldn’t be enabled regardless of what sensor package they’re using, let alone based on camera data only. They need to solve their ability to accurately observes their surroundings first, especially temporally. Things shouldn’t be flashing in and out that have been clearly visible to the human eye the entire time. Additionally, this all ignores the experiential portion of driving. When most people approach something like a blind driveway or crosswalk obscured by a vehicle (a dynamic, unmapped, situation), they pay special attention and sometimes change their driving behavior.

Actually that's a fair point. My goal was only for simplification and not intended to malign.

> because it's often true that you can deliver better by focusing on a smaller number of things.

This is true / dogma in linear / non-linear regression world, but of no real import in deep learning or Bayesian methods.

There seem to be 2 points of view here. One technical (sensors and the algorithms), one organisational (people and teams working on the problem).

My understanding is that by focusing on fewer things (vision only), they bet to make progress faster because of the simplified organisational aspect.

I think they’re talking about number of different systems doing the same thing. Have one system doing it that is sufficiently abstracted away from a common set of hardware vs various systems competing for various aspects of control.

Sorry, it's your opinion that researchers and/or engineers working on DL or Bayesian methods work better when they're distracted by many diverse tasks? What?

No, it's my opinion that in linear regression an inordinate amount of time is spent with feature selection and ensure there's no correlations among the features. When data is cheap in both X and Y, winnowing down X is a lot of work.