The common sentiment has been that self-driving cars are just around the corner. The reality is quite different. The remaining problems to solve such as naviga

The common sentiment has been that self-driving cars are just around the corner. The reality is quite different. The remaining problems to solve such as navigating the elements or obeying construction signs or interacting properly with pedestrians or police are orders of magnitude more difficult than the problems that have been solved thus far. These problems are fundamentally different in that they can't be solved by current AI techniques and vision algorithms. The progress made so far has been quick, but it relies on technology that Google has already mastered. We'd be mistaken to think that the remaining challenges will be solved as easily. It's a repeat of the classic mistake that has plagued the field of AI since the beginning: we underestimate the difficulty of problems that humans solve easily. We simply aren't aware of the incredible complexity involved in our simplest decisions, such as pulling over to allow an ambulance to pass. This is simple right? Just slow down and move off to the side of the road. But when is it OK to move off to the side -- what if there is something in the way, what if a pedestrian didn't expect you to move there, what if the car behind you suddenly gets in the way while it's pulling over, what if you're on a bridge, what if the ambulance behind you turned already and no one expects your car to suddenly pull over? Similar or more difficult problems arise when there's debris or potholes in the road, other poor drivers, bad weather, jaywalkers, policemen giving orders, road work, detours, etc. What you find is that the last 5 or 10 percent of the capability required to make self-driving cars feasible represents a category of problems which we don't know how to solve, requiring a level of sophistication far beyond the current state of the art and perhaps approaching general intelligence in some cases (such as interpreting signs). Better approaches involve shooting for more modest goals instead of full autonomy. Car companies are making investments in these more practical, incremental improvements, like automated parking and advanced cruise control. But unlike car companies, Google isn't in this game because it thinks it can make a profitable and successful product. Instead, it's obvious that the main function of developing self-driving cars is as a PR tool (and the same goes for the rest of the Google-X projects). Google has gotten a lot of positive press for their self-driving cars, and they even use it to attract new employees. However, I predict this positive press won't last (this article being an early example) because people's expectations are way too high. As years and years go by without much progress, Google's self-driving cars will increasingly become a PR liability and will be compared to the promised flying cars of yesteryear.