Sure, 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 th

Sure, 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).