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And it's even harder to automate systems that have to derive data from environmental cues. A line-following robot is something simple enough that undergrad stud
by alxlaz 8y ago
And it's even harder to automate systems that have to derive data from environmental cues. A line-following robot is something simple enough that undergrad students can build, and making it stop if it detects obstacles is a good choice for extra credit. A road-following car that stops when detecting obstacles is way, way, way more complicated than that.
- adrianN 8y agoI don't think stopping for obstacles is the hard part of autonomous driving.
- reitanqild 8y agoIt gets harder when you are: * Out of the lab * On roads that aren't level * When the obstacles move * etc
- rsynnott 8y agoIt's one surprisingly hard part of it. Note how Teslas enjoy driving into stationary objects with certain properties, say.
- adrianN 8y agoIt is my understanding that this is due to having the wrong kinds of sensors.
- kwhitefoot 8y agoThere is a road (Norway, Drammen-Svelvik) that I travel once or twice a week in my 2015 Tesla S 70D that often has a car parked on a particular corner. The car is directly ahead of my car and side on; my car reliably interprets this stationary car as being on the road and that we are on a collision course and applies the brakes even though the road actually bends to the left and the stationary car is not in the way. If I didn't know the road I might well have reacted in the same way. I'm not sure that improving the sensors would necessarily change things, the software that interprets the sensor output must be improved too.
- alxlaz 8y agoIt's very complicated. Not bumping into stationary objects on the road is fairly simple with the right kind of sensors, but most obstacles you encounter in real life are neither permanently stationary nor permanently in motion, and are not always on the road.