[MarcelGerber]

Yes, this seems really complex to me, too. I'm also not quite sure on why exactly it is so complex to distinguish between objects (including vehicles) on the road and ones above/next to it. If they have radar images (I imagine them as images with depth information, which might be fundamentally wrong), they should be able to tell both where the road is going and, with that information, which of these objects are of relevance.

But for the learning part, they probably combine the camera that they used to date as their primary device in combination with the radar (at least in daylight scenarios) to identify objects. They may even be able to learn about the special material properties, like the reflective coating of a traffic sign.

[toomuchtodo]

> I'm also not quite sure on why exactly it is so complex to distinguish between objects (including vehicles) on the road and ones above/next to it.

Because radar does not have the same resolution as LIDAR.

EDIT: Phased array radar and cheap stationary LIDAR should get under $100 in ~5 years, at which point this whole argument will be moot. Hacks in the meantime!

[Pyxl101]

What does the world look like in radar? Are there any visualizations available?

Could multiple radar emitters and receivers be used to create a phased array and improve resolution?

[danielmorozoff]

Here is the resolution calculation for aircraft radar (with respective example of signature - aka image )

http://www.radartutorial.eu/01.basics/Range%20Resolution.en....

For angular resolution: http://www.radartutorial.eu/01.basics/Angular%20Resolution.e...

[wyager]

> What does the world look like in radar?

Depends on the radar system. Some are distance only without direction. Some are 1D (a line, usually horizontal) and some are 2D. Many objects are partially opaque, which is confusing. Resolution is very poor compared an optical device of the same size.

> Could multiple radar emitters and receivers be used to create a phased array and improve resolution?

Yes. However, this is currently bulky and expensive (in dollars and in compute power). Thankfully, it looks like capitalism is coming in to the rescue here and miniaturizing the everloving shit out of complex radar arrays for human interface tech. This should be usable for vehicles as well.

[Animats]

All the automotive radars are phased array devices, and have been since the Eaton VORAD of the late 1990s. No moving parts. They're usually 1D scan (horizontal) only, although 2-axis scanned automotive devices exist.

[kevin_thibedeau]

If you're cresting a hill or rounding a curve, passing under a bridge, etc., static objects in the visual field are translating in front of the vehicle and are not easy to distinguish from slow moving vehicles. With simple sensors, there isn't enough information to decide that these blips are on a collision course or not.

The Florida situation was the easiest set of circumstances for machine vision to handle and it still failed. More complex, dense road systems with real terrain are much harder to handle. This radar system will still get someone killed.

[toomuchtodo]

The driver is still the final authority. Pay attention and in most cases, if you die, it won't be your fault.

[bluthru]

Is LiDAR worthwhile despite rain and snow detection? Can that be ignored in software?

[caf]

I'm also not quite sure on why exactly it is so complex to distinguish between objects (including vehicles) on the road and ones above/next to it.

In the case they're talking about here, it's because to do so you need to predict where the non-visible road surface is going to be.

Consider travelling up a continous, slight incline. Precisely at the crest of the incline is an overhead gantry sign, positioned such that for an observer travelling up the incline it is located directly in line with their current direction of travel.

The only way that you "know" that the road actually dips under the sign rather than the sign being on the road surface is experience.

[byebyetech]

You can also see other cars "disappearing" under the sign. And the sign going up a bit as you move closer.

[Fricken]

That's the kind of higher level reasoning that's easy for people and hard for machines.