[ACCount37]

"If."

So far, every self-driving accident where the self-driving car was found to be at fault follows the same pattern: the car had all the sensory data it needed to make the right call, and it didn't make the right call. The bottleneck isn't in sensors.

[rootusrootus]

In that case we're probably even further from self-driving cars than I'd have guessed. Adding more sensors is a lot cheaper than putting a sufficient amount of compute in a car.

[BuckRogers]

He didn't really cite a source on his huge claim that it had "everything it needed" in all cases. A camera can't see in the dark, or through fog.

I want lidar. But the minimum setup without lidar would be cameras, radar, ultrasonic, GPS/GNSS + IMU.

As I told him, two things can be true at once. AI may not be good enough AND you want more sensors and data to work with.

Everyone tries to present choices like that all the time to try to make a false dichotomy. Yes I concede that it's most likely that most autonomous accidents are probably in crowded spaces or areas with a lot of cars. Simply because that's the most likely place for an accident to occur.

But who doesn't want beyond superhuman sensory data for their vehicle? Why limit to just human eyeball equivalent sensory data? It makes no sense. Unless you're just trying to defend Tesla's decision. I'm sure it can work that way. It would be smarter if the idea was to cost cut, to remove sensors AFTER AI was trained on more data and perfected. Not train a fleet on minimum input and then just beat down the strawman you created that you "don't need more". The task (training the AI) isn't done yet, so you can't really make such a sweeping claim until you succeed.

But why do I want this anyway? Give me two parachutes please, rather than one. I'm so worried about the cost that I won't spare it.