He made a very good argument for vision-only, but it seems like training actually uses radar data to help calibrate vision measurements, so it seems to me there’s value in making some vehicles still contain radar (say, one out of 10) even if it’s not used for controlling the vehicle directly at drive time.
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.)
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.)