soooo... you're agreeeing that non-vision isn't necessary since the control domain is so much simpler?
I personally think they should use as much data inputs as possible: radar, IR, LIDAR, mesh networks, fixed route information.
Where tesla went particularly wrong IMO is ignoring some sort of route-based chunk information which is how humans navigate. IIRC Elon said something to the effect of just having an algorithm to work everywhere.
Humans use the basic algorithm "stay in lane, drive forward" and then decorate with signs, knowledge of curves, locations of potholes, dangerous low-viz corners, likelihood of surprise stopped traffic, obscured driveways, general character of neighborhoods, road purpose. Weather. Windy sections, icy sections, light availability anomalies. What type of vehicle. Repair state of vehicle.
A general AI algorithm will never be able to properly account for flavors/tags/chunk info on routes. Especially since cloud precomputation is so available these days.
Anyway, while recognizing that Tesla's "Fully Self Driving" is not as advertised, and we are a ways from self driving for any statistical measure of superiority to a healthy aware adult, it is still damn impressive what FSD vids show.
Do AI driving systems try to make "subsystems" of AI networks to reduce inputs to various higher-level inputs, or do other just throw a ton of inputs at a big ass network and just let the entire system rise from the soup of information?
Hell, even in the northeast US (particularly the cities) this isn't true. Self-driving cars today seem to have a dogmatic focus on California-style driving.
I personally think they should use as much data inputs as possible: radar, IR, LIDAR, mesh networks, fixed route information.
Where tesla went particularly wrong IMO is ignoring some sort of route-based chunk information which is how humans navigate. IIRC Elon said something to the effect of just having an algorithm to work everywhere.
Humans use the basic algorithm "stay in lane, drive forward" and then decorate with signs, knowledge of curves, locations of potholes, dangerous low-viz corners, likelihood of surprise stopped traffic, obscured driveways, general character of neighborhoods, road purpose. Weather. Windy sections, icy sections, light availability anomalies. What type of vehicle. Repair state of vehicle.
A general AI algorithm will never be able to properly account for flavors/tags/chunk info on routes. Especially since cloud precomputation is so available these days.
Anyway, while recognizing that Tesla's "Fully Self Driving" is not as advertised, and we are a ways from self driving for any statistical measure of superiority to a healthy aware adult, it is still damn impressive what FSD vids show.
Do AI driving systems try to make "subsystems" of AI networks to reduce inputs to various higher-level inputs, or do other just throw a ton of inputs at a big ass network and just let the entire system rise from the soup of information?