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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
by Robotbeat 5y ago
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.)
- avs733 5y agoMost fatal car crashes are investigated, but by the police not engineering experts. The invetigational motivation is legal and liability focused not improvement focused. 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?
- eddanger 5y agoI would love to see re-certification of "professional" drivers. Almost daily I encounter taxi or semi-truck operators driving at the limit of what is acceptable.
- osipov 5y ago> 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. That's not how neural networks work. You start by training them with a radar, then you deploy them without the radar. Neural nets make the radar irrelevant post-training. This is the entire point of Karpathy's pitch.
- nelsonfavedra 5y ago> but it seems like training actually uses radar data to help calibrate vision 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.
- jowday 5y agoThey use radar for way more than that given that Autopilot has it's capabilities limited in the new cars without radar vs. the old ones with radar.
- Robotbeat 5y agoI don't think it's silly at all, although there are probably other ways to do it (license plates are a known size, so they could be used for calibration... to some extent). In fact, I think going to a sort of mapping solution where some portion of the Teslas on the road provide mapping data (using just calibrated vision) may be appropriate. If the reason you aren't using maps as an extra layer of capability is just because of the cost of mapping, then using Teslas to provide mapping capability "for free" is a good solution. 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.