There's a probabilistic nature to how the image sensor data is interpreted.
The neural network processes data to classify objects and predict what each object is. It doesn't always have a perfect static representation of each object. It's constantly updating its understanding based on the image sensor data. That jiggly jitter you see is basically the system refining its prediction in real-time.
Other companies have put quite a lot of effort into perception stability because it has a large effect on the downstream quality. It's hard to estimate higher order derivatives like velocity and acceleration well if your position estimates are unstable.
> That jiggly jitter you see is basically the system refining its prediction in real-time.
GP is saying that a "car that jumps in and out" and "things that randomly vanish" do not look very refined. Just like missing a freaking moving train doesn't look very refined.
The neural network processes data to classify objects and predict what each object is. It doesn't always have a perfect static representation of each object. It's constantly updating its understanding based on the image sensor data. That jiggly jitter you see is basically the system refining its prediction in real-time.
Or at least that's how I understand it...