I agree (article submitter here). I submitted the article mostly not to complain about lack of progress but because the article covered a lot of interesting details about how the Google technology worked in discussing the limits of the current system. I have little doubt such systems will continue to rapidly improve.
I was involved briefly on a project for self-driving cars in the late 1980s at Princeton involving neural network ideas for image processing, and I suggested we could just train the cars to drive specific routes. However, that suggestion was scoffed at (and I did not try hard to push it). My argument was that most driving is stuff like daily commutes or runs to well known stores, and so pretty much the car could drive exactly the same way every time, seeing the exact same sights. That might make it feasible to train the neural networks from just a few video recordings of drives over the same stretch of roadway. Granted, lighting conditions, weather, other cars, pedestrians, and possible lane changes make that harder -- but is seemed like a good place to start, rather than try to create a car driving system that could drive in arbitrary new circumstances where it has never seen the road before. Solar panels have succeeded much the same way -- the early ones were niche (like in calculators or satellites), but sales drove more R&D that lead to better and cheaper panels in more and more applications. A self-driving car that could only drive me from home to a few local towns and back on fixed routes (safely, while, say, I surfed the web) would still be tremendously valuable to me. Think of how many people commute the same routes every day for years and could use that commuting time more productively in other ways via the internet. If people with an hour commute could use that time to answer email, then maybe they could work one hour less in the office? Also, a car that just knew how to park itself in a standard location and come back to pick you up in front of some building you work at or apartment you live in would be very useful in cities.
Another idea I had several years ago is that we could have an open source software effort to drive cars in various simulated racing games like "Gran Turismo" or other free play driving games like "Driver: San Francisco" or various off-road sims. That would be a inexpensive and safe challenge for college students. Those driving simulators go to great lengths to make realistic looking images (including things like dust clouds and vehicle dynamics), and they continue to improve. You just feed the first-person video generated the game into the car-driving visual processing algorithms, and you have the software control the game via USB outputs. As the software gets better, then you can fuzz up the image more and more by adding more white noise to it, or whatever other distortions you wanted (like bug white blotches over parts of the image) to challenge the algorithms. Or you could introduce delays and noise in how commands for steering were processed. Such an approach makes writing such software feasible for the average software developer without a special car. Granted, the software would have to focus on processing 2D images instead of 3D laser ranging data. Even Google has talked about testing their software in simulations regarding certification. Ideally, the simulations used for testing would be open source too, like Rigs of Rods (or even more realistic) and if so, things like 3D ranging data could probably be extracted too: http://www.rigsofrods.com/