0% if you're referring to complete autonomy in all situations.
I work in the broader AI field, admittedly in the NLP/NLU/NLC space rather than image processing, but autonomy (which is what we're really talking about) is an extraordinarily difficult problem to solve, where the inputs are not finite. There are literally an infinite number of "ifs" with a finite number of "thens" to consider, and so this is both a computational problem as well as a learning problem and a recall (storage/latency) problem.
I certainly think that something close to self-driving will be available for "sunny day" scenarios, like driving around relatively calm and quiet grid-based cities with good weather and predictable traffic. But negotiating poorly signed road-works on smashed up highways around New York or Detroit, or dealing with heavy rain, snow, fog, erratic drivers, cyclists, traffic cops telling drivers to do the opposite of what the signs say, dealing with emergency vehicles, navigating parking lots, doing all of that in the dark, doing any of that when there's no wireless signal and not enough information in the onboard database, etc, all mean that "full" autonomy is probably 3 or 4 generations away (10 - 15 years IMO).
In technology people talking about 80/20 where 20% of the effort gets you 80% of the result, and the remaining 80% effort gets you the final 20% result. In the development of autonomous driving, those "edge cases" which might only be 1 - 2% of driving situations, will consume more than 95% of the effort.
I would so love to be wrong BTW.
