Another bad article.
As I keep pointing out, the way you start to do automatic driving is by first profiling the terrain to see where you can go. If it's not flat road, you don't go there. Doesn't matter why it's not flat. Then try to classify other road objects and predict their behavior. This only matters for moving objects.
Waymo gets this, as we know from Urmson's talk at SXSW a few years back.
Most of the DARPA Grand Challege vehicles got this, because they had to drive off-road, where you have to profile terrain or else.
Tesla does not get this. Cruise may or may not get this. Uber - well, Uber's system detected the pedestrian and ran into her anyway, which should end with someone in jail.
There's this mindset that you just throw deep learning at camera images and automatic driving comes out. Musk claimed that. It didn't work. We don't hear much from Tesla about self-driving any more. Udacity's self-driving course is also deep learning based.
As for how much testing is required, read the California DMV accident reports.[1] This gives you a sense of what the real-world problems are. 25 minor accidents so far this year. Mostly Cruise. The most common problem, especially with Waymo, is being rear-ended while cautiously entering an intersection with limited visibility. Their system will start forward to get a better view, then detect cross traffic and stop. What may help there is some convention such as rapidly flashing the brake lights when a sudden stop is likely and there's a vehicle close behind.
On the LIDAR front, Continental's flash LIDAR is already working well enough that drone makers are buying it. Continental is ready to produce that thing in volume, but they need volume orders from automakers before the price comes down.
[1] https://www.dmv.ca.gov/portal/dmv/detail/vr/autonomous/auton...