The "until we can program computers to..." is a when question, not an if question. There's nothing about driving, in any situation, that doesn't fall in the face of "assuming infinite computing power and infinitely good sensors". Driving isn't a creative act, it's a responsive one.
My main issue is that a large number of people seem to think that cameras + machine learning are enough to solve this problem. And while I'm not an expert at machine learning, what I know about it makes me a pessimist. There's just too many unsolved problems in the machine-learning community to apply machine learning to the car-driving problem.
Machine learning probably can solve weird cases people don't expect. IIRC, CNNs are better at recognizing blurred or garbled text than humans these days. So CNNs can read speed limit signs, road signs, and other texts and and at least process that.
Even figuring out if its a speed limit sign, an address, or a route-number probably can be solved by CNNs. But higher level reasoning (is that spraypaint messing up the signpost?? Which was common in some of the areas I drove through) seems like an unsolved problem.
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Anyway, Machine Learning + Cameras are IMO, at best... a partial solution to some of the problems. Anyone who thinks that cameras + radar + machine learning is sufficient is probably just a TSLA long who wants to believe in the success of their stock. Otherwise, I think most people are reasonable and recognize the importance of experimenting with a ton of different methodologies to solve this problem.
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.
https://www.wired.com/story/tesla-autopilot-why-crash-radar/
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.
https://arxiv.org/pdf/1801.02225.pdf
Just go through this recent paper I found on DDG.gg and see the amount of effort it takes to parse foreground / background data that a self-driving car needs.
You gotta figure out if there's a still-object on the road. And whether or not its a shadow (shadows don't move after-all but its safe to drive over). Like, neural networks can't do that stuff 100% reliably yet. And it may never happen.
Or some researcher next year might come out and discover a method to parse background / foreground / shadows out of pictures. But then there are a whole host of OTHER issues involved.
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I think self-driving technology has potential. But you need more than just neural networks hooked up to cameras. I really like Waymo's direction with advanced super-human sensors. Avoid the shadow / background / foreground problem entirely and just have LIDAR give you the precise coordinates of all objects within 100-feet of the car.
IMO, if self-driving technology ever happens, it will be because of advances in advanced LIDAR or other kinds of sensors. Stuff that can avoid the research-problems that the "Deep Learning" community hasn't been able to solve for the last 50 years.