Humans suck at these classification tasks things too, though, if you set your expectations high. Is it a cat or a bag, or a weird shadow? Doesn't matter, I ran it over before I could figure it out and react. Or maybe it turned out to be a boulder? Now I'm one of 35,000 traffic fatalities this year in this country.
Computer vision has been improving rapidly in the last 10 years, I think it's too soon to rule out the viability of a camera-based solution entirely. Though I do hope improved lidar technology can improve on humans.
Humans are way, way better than current CNNs on this field. We can talk about cats, shadows, and boulders when CNN-based methods stops crashing into concrete barriers, parked fire-trucks, and 18-wheelers making a left turn.
I don't want to dismiss the work of Deep Learning / Machine Learning specialists. I just want to point out that the problem is incredibly difficult. It is very far away from being a solved problem.
> Traffic-Aware Cruise Control cannot detect all objects and may not brake/decelerate for stationary vehicles, especially in situations when you are driving over 50 mph (80 km/h) and a vehicle you are following moves out of your driving path and a stationary vehicle or object is in front of you instead
This is a known issue, a known pattern and has happened multiple times this year. Its repeatable. CNNs today are not working in this case, and fixing it will require a research effort of mammoth proportions.
You're talking about what the tech does. I'm talking about what it can do, with reasonable-sounding non-magical improvements to tech. Just because there are limitations now does not mean those limitations will still exist in ten years.
Computer vision has been improving rapidly in the last 10 years, I think it's too soon to rule out the viability of a camera-based solution entirely. Though I do hope improved lidar technology can improve on humans.