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.
