Lots of hills, and will have plenty of ice and snow in a few months. It seems to me like a logical place to test the resilience of a self-driving system against some predictable corner cases.
> The company has been creating extremely detailed maps that include not just roads and lane markings, but also buildings, potholes, parked cars, fire hydrants, traffic lights, trees, and anything else on Pittsburgh's streets.
If one city requires that much work, they're not scalable. Even if we assume the LIDAR on every Uber car collects the new data, the car will trip sooner or later if it can't detect potholes.
Concerning the bridges, can't they do like the Kinect: Match the image with depth-sensing lasers, so they can detect the boundaries of the bridge, stray away from pedestrians, filter out fire hydrants and trees but slow down when encountering other objects?