You sparked a happy delusion in my mind...training drivers to the same level we train pilots. Can you imagine drivers having regular check rides?
They seem to use radar solely to automatically label data for training.
In the given example though where according to the vision system smog interrupted the persistence of the label for the leading car, I wonder if the use of radar data to persist the label is strictly necessary.
A car disappeared then reappeared in the data, why not just tween the bounding box over time and assume the car had always been there, like, if it looked the same when it reappears or something. An extra sensor just for labelling data seems silly.
The argument may be that humans do fine without mapping, but that's not strictly true. Humans remember and anticipate the road based on past memory. When there's a change in the road due to construction, there's often a sign placed to warn drivers of the change in traffic pattern, showing that humans definitely at least partially rely on "mapping," so it could help even a pure vision system.
Also, the sensor resolution issue he mentioned could be addressed by using a higher resolution radar sensor.
I find the list of 221 triggers to be interesting. In principle, the NHTSA or NTSB could help contribute lists of triggers to companies to validate their training sets on.
Every time there is a fatal airliner accident, the NTSB does a safety investigation and airliners get a little bit safer each time. In the same way, each fatal accident in a vehicle with this kind of autonomy could end up being captured by these triggers, improving safety over time in a sort of mixture between expert human analysis and ML.
(Nobody does this for all regular car crashes because fatal car crashes happen every day! And you’re not going to retrain human drivers about some new edge case every day, although you can for vehicles like this.)