3 ms·

Seems like it would make more sense to model the inertia. Cars don't randomly accelerate at 100,000m/s/s in some direction they aren't pointed. Though they shou

Seems like it would make more sense to model the inertia. Cars don't randomly accelerate at 100,000m/s/s in some direction they aren't pointed. Though they should have a model for detecting obstacles in the view regardless of inertia, because sometimes something really does appear in front of you in a thirteenth of a second. You could probably model inertia with n prior frames of probability fields.

eru 7y ago
Modelling inertia seems like a special case of a low pass filter? A very useful and physically plausible special case, of course.
modeless 7y ago
> Cars don't randomly accelerate at 100,000m/s/s in some direction they aren't pointed What if they are hit by a truck? Maybe not 100,000 m/s^2 but if you assume that cars can't accelerate in directions they aren't pointed, you will be wrong at the worst possible time.
- microcolonel 7y ago
  That's why I elaborate, and why I chose that number. The only way something actually accelerates like that is an error, or it's an error.
  - modeless 7y ago
    A threshold that high will be useless as it will miss most errors. A threshold low enough to catch most errors will reject some valid data. A naive approach like that will not work. A better approach would be to include temporal data in the inputs to the neural net so it can learn how to do the prediction and filtering itself using all the context available in the input imagery, instead of processing each frame completely independently and feeding low-dimensional symbolic results into some other system. But you'd need a very large dataset and a very large neural net.
mannykannot 7y ago
Would you not be implicitly assuming that the prior position was more accurate than the current one? If you have a sequence of consistent prior positions, then perhaps something like a Kalman filter would be appropriate, but I would guess that with something suddenly being revealed by a change in either party's position or that of a third party, you don't always have that.

eru 7y ago

Modelling inertia seems like a special case of a low pass filter? A very useful and physically plausible special case, of course.

modeless 7y ago

> Cars don't randomly accelerate at 100,000m/s/s in some direction they aren't pointed What if they are hit by a truck? Maybe not 100,000 m/s^2 but if you assume that cars can't accelerate in directions they aren't pointed, you will be wrong at the worst possible time.

microcolonel 7y ago
That's why I elaborate, and why I chose that number. The only way something actually accelerates like that is an error, or it's an error.
- modeless 7y ago
  A threshold that high will be useless as it will miss most errors. A threshold low enough to catch most errors will reject some valid data. A naive approach like that will not work. A better approach would be to include temporal data in the inputs to the neural net so it can learn how to do the prediction and filtering itself using all the context available in the input imagery, instead of processing each frame completely independently and feeding low-dimensional symbolic results into some other system. But you'd need a very large dataset and a very large neural net.

mannykannot 7y ago

Would you not be implicitly assuming that the prior position was more accurate than the current one? If you have a sequence of consistent prior positions, then perhaps something like a Kalman filter would be appropriate, but I would guess that with something suddenly being revealed by a change in either party's position or that of a third party, you don't always have that.