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That's a reasonable basic overview.

I'm surprised that rotating scanners are still used. It's been twenty years since Velodyne built their first one. They work OK, but cost too much. I was expecting flash LIDAR or MEMS mirrors to take over. Continental, the auto parts company, bought the leading flash LIDAR company over a decade ago, but the volume market a big parts company needs never appeared.

Waymo is still using rotating LIDARs even for the little ones at the vehicle corners. Those need less range. There needs to be a cheap, flush-mounted replacement for those things. The location is too vulnerable. Maybe millimeter phased array radar mounted behind Fiberglas body panels. Waymo needs to solve that problem before they do New York.

The LIDAR on top may not be a problem. Insisting that it has to go away to "look like a car" is like insisting that cars had to have the form factor of horse-propelled buggies. Early cars looked like buggies, but that didn't last.

One big advantage of pulsed LIDAR over continuous is that the interference problem between identical units is much less. The duty cycle is tiny. Data from one pulse round trip is collected in less than a microsecond. Just put some randomization in the pulse timing and getting multiple conflicts in a row goes away.

Here's an interesting "lidar gem" from Hacker News a few years ago:

https://news.ycombinator.com/item?id=33554679

Lidar obstacle detection algorithm from a Git repo leaked onto Tor

This is a drivable region mapping (obstacle detection) algorithm found in what appears to be a git repo leaked from an autonomous vehicle company in 2017. The repo was available through one or more Tor hidden services for several years.

The lidar code appears to be written for the Velodyne HDL-32E. It operates in a series of stages, each stage refining the output of the previous stage. This algorithm is in the second stage. It is the primary obstacle detection method, with the other methods making only small improvements.

The leaked code uses a column-major matrix of points and it explicitly handles NaNs (the no-return points). We've rewritten it to use a much more cache-efficient row-major matrix layout and a conditional that will ignore the NaN points without explicit testing.

This is an amazingly effective method of obstacle detection, considering its simplicity.

I worked on an automotive FMCW LiDAR that didn't quite make it to market. Cool technology but it was difficult to scale the cost down, which is pretty important for automotive. Margins are very low in that market

Are LIDAR dangerous to the eyes of other drivers or pedestrians?

"Its particular superpower is that it can generate high resolution images of its surroundings much better than radar can."

Is this true tough? Car radars are fixed. I guess a comparable lidar would be fixed too and have n points for n lasers.

A rovolving radar would have continuous resolution around while a lidar samples?

I thought the advantage of lidars were accuracy and being better at measuring heights of objects, where as radars flatten the view.

Fantastic tech that Musk hates

Is there a lidar unit I can take home and scan my house at high resolution (than iphone)?

Related: https://www.viksnewsletter.com/p/teslas-big-bet-cameras-over...

Why is lidar so expensive? And still needs to be miniaturized. But time should solve those problems, there's enough engineering effort.

LIDARs can be blinded by consumer grade laser pointers, I wonder if there are systems that protect LIDARs against adversarial attacks or DOS attacks