That is what the comment is saying. Of course the vision stuff is done with machine learning - that is after all the state of the art. But that is a tiny part of the self-driving problem. So you can recognize pedestrians, other cars, lanes, signs, maybe even infer velocity and direction from samples over time. But then the high-level planning phase isn't typically a machine learning model, and so if you record all the state (Uber better do or that's a billion dollar lawsuit right there) you can go back and determine if the high-level logic was faulty, the environment was incomplete etc.

I was responding specifically to "Instead, LIDAR should exactly identify potential obstacles to the self-driving car on the road." - LIDAR isn't economically viable in many self driving car applications (for example: Tesla, TuSimple) right now.

Then your comment is off-topic, because the realm of discussion was explicitly "self-driving cars equipped with LIDAR". Uber's self-driving vehicles are all equipped with LIDAR, as are basically all other prototype fully-autonomous vehicles.

How is it off topic when we're discussing "current-generation self-driving" vehicles?

It's a point of clarification that the originally listed study doesn't take into account, but which could be important to the broader discussion. Especially considering that while this vehicle had LIDAR, the other autonomous vehicle fatality case did not.

> as are basically all other prototype fully-autonomous vehicles

As I pointed out with examples above, no, they are not.

The vehicle involved in the accident has an HDL64 on the roof.

Is it true?

You can get depth sensing (time of flight) 2D camera Orbecc Astra for $150 or 1D laser scanner RPLIDAR for $300. Of course they are probably not suited for automotive, but for me even extra $2000 for self-driving car sensors isn't that much.