3 ms·
what if a driver does a mistake, like running a red, and doesn't get a ticket? who validates all this data? attaching a dnn to a driver as a training set is a
by LoSboccacc 11y ago
what if a driver does a mistake, like running a red, and doesn't get a ticket?
who validates all this data?
attaching a dnn to a driver as a training set is a pipe dream, for now. maybe after we understand how our brain perceives time and build models of future outcomes, we could apply it to build better nn. For now, nn are just best used as classifiers in a controlled environment, not from an environment with unpredictable states.
and especially not in an environment with adversaries http://spectrum.ieee.org/cars-that-think/transportation/self-driving/researcher-hacks-selfdriving-car-sensors http://spectrum.ieee.org/cars-that-think/transportation/self...
- darkmighty 11y agoThe vulnerability to sensor error (adversarial or not) is certainly not exclusive to nn based approaches. I commented on the validation problem in the comment above and in another below, and one way to deal with it is simply manual validation (mainly for false positive elimination). Indeed this approach with dnns is already being employed by Mobileye, so I don't think it's a pipe dream. Sensor failure or well characterized adversarial inputs are actually really easy to deal with -- they are very easy to simulate with a given dataset and self-validate using traditional techniques -- simply make one or more cameras fail (or receive spurious sigs) and verify the output. It's a good point that probably all autonomous cars will need a contingency plan (probably human intervention and/or blind emergency stops) with non-zero probability -- even if you have a redundant network of cameras around your vehicle a critical number can and will occasionally fail (when you look at the fleet sizes that will be dealt with).