5 ms·

It doesn’t have to be all or nothing. For instance, the network might only be used to decide among a series of actions, and those actions can still have limits

It doesn’t have to be all or nothing. For instance, the network might only be used to decide among a series of actions, and those actions can still have limits (such as “car cannot travel faster than X” or “Y cannot change more than 3 times per minute”, or whatever). There is still an abundance of attention put into safety, as usual for the auto industry. It isn’t just a brain hooked up to an engine that is allowed to run rampant.

apk17 10y ago
Depending on the context, 'turn the wheel left two degrees' can be a non-rampant, a very rampant, or a rampage-compensating action.
- marcosdumay 10y ago
  "Keep the car in a straight line" or "turn right, 10°" are the kinds of operation that will always be more performant, take less engineering, and more reliable to create as an old-fashioned simple math function than by using machine learning. One would expect the output of the neural nets to be encoded in those.
  - Hydraulix989 10y ago
    It's the exceptional cases that get you: 1. "Keep the car in a straight line" => The road is closed or there is a detour, or the lane markers aren't visible (or incorrectly marked like on the Palo Alto stretch of the 101 where there are two sets of lane markers; one new (the "actual" markers) and another old but barely faded and still very visible. 2. "Turn right, 10 degree" => With what steering radius? What if the road is bumpy or sloped and the driver has to compensate with the wheel? etc. These corner cases are everyday occurrences and lend a necessity for something far more sophisticated than a mere ad hoc model to represent and control human-level driving behaviors with a finite set of rudimentary actions. Actuator inputs like steering and pedals are almost always represented using continuous values rather than discrete sets for these very reasons.

apk17 10y ago

Depending on the context, 'turn the wheel left two degrees' can be a non-rampant, a very rampant, or a rampage-compensating action.

marcosdumay 10y ago
"Keep the car in a straight line" or "turn right, 10°" are the kinds of operation that will always be more performant, take less engineering, and more reliable to create as an old-fashioned simple math function than by using machine learning. One would expect the output of the neural nets to be encoded in those.
- Hydraulix989 10y ago
  It's the exceptional cases that get you: 1. "Keep the car in a straight line" => The road is closed or there is a detour, or the lane markers aren't visible (or incorrectly marked like on the Palo Alto stretch of the 101 where there are two sets of lane markers; one new (the "actual" markers) and another old but barely faded and still very visible. 2. "Turn right, 10 degree" => With what steering radius? What if the road is bumpy or sloped and the driver has to compensate with the wheel? etc. These corner cases are everyday occurrences and lend a necessity for something far more sophisticated than a mere ad hoc model to represent and control human-level driving behaviors with a finite set of rudimentary actions. Actuator inputs like steering and pedals are almost always represented using continuous values rather than discrete sets for these very reasons.