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I guess what I meant was, surely there are weird surfaces that have multiple bounces. Or light being emitted could bounce between 2 cars and back to the sensor
by daenz 15y ago
I guess what I meant was, surely there are weird surfaces that have multiple bounces. Or light being emitted could bounce between 2 cars and back to the sensor...or off a shop window (at a high angle, fresnel reflection), back onto something else, and back into the sensor. This data would come back into the sensor and it wouldn't be expected.
So surely the automated car, when it sees data it does not expect, does not stop, because it must see data it does not expect often through multiple bounces, right?
- xxbondsxx 15y agoSure, the situation you described certainly happens and is just considered a general noisy measurement. The car could detect empty road one second and all of the sudden some small object _right_ in front of the grill at the next frame. To avoid this "freakout" situation where the car slams on the brakes every time a noisy measurement comes in, all the data is passed through a particle filter (or Kalman filter) first before being processed by the AI. The transition model of the cars environment is known, so it can reason that "there is a very small chance this reading represents a real object and is not noise, because i did not detect anything near this position over the last 20 frames, so I'm going to assign a very low probability to it." Hence you can clean up the data really well because you're measuring an outdoor environment, not a meteor shower (or anything else where objects could appear and disappear every frame due to high velocity).