[idavidrein]

I agree with a significant amount of your point, but with regard to object permanence, I would guess that they have prediction algorithms that don't only rely on the current-time perception, so if something blips out of sight for a second the system will still infer/predict it's existence (for a time - obviously if something is hidden for a long time it won't continue to not trust perception).

[lolc]

I'd be very interested to know how that works. But I don't think they have it.

The boxes they draw are very wobbly and dimensions expand and contract directly with sensor input. Maybe they only show fused output (in itself an achievement) and there is a later step they don't show. That would be weird though because if they want to brag about their model they would definitely show it if it was any good.

[idavidrein]

that's a fair point, but it seems reasonable to me that they would separate the sensory input and the predictive/higher-level aspects of their modeling. For example, we know for a fact that they must be doing tons of prediction for both cars and people, so I think it's likely that different models (not sensory) have the info that a person is probably still there.

[lolc]

Yes it's true they must have some form of persistence when they do predictions. But expected trajectory of other vehicles and pedestrians was missing from their video. A lot of other interesting feeds were missing too, so I don't know what to read into it. I tend to think that that stuff would look much worse. But maybe they just didn't want to clutter the video or show how advanced they are already.

[idavidrein]

yeah, it's possible that the stuff doesn't look very good, but my guess (maybe my hope?) is that it's too cluttering or through careful analysis could reveal IP about their predictive algos