How the LIDAR tech GM just bought probably works

What about a beam collimator at the detector? It would reject a lot of noise but would cost some power.

Despite the name, they aren't related to old-school photomultipliers - they're basically large arrays of very tiny Geiger-mode avalanche photodiodes on a single chip. This solves the low dynamic range issues from traditional large-area avalanche photodiodes. Traditional APDs have a long recovery time, whereas an array of small APDs has both a shorter recovery time per cell as well as a greater overall dynamic range due to the ability of multiple cells to be struck at once by incident photons.

SensL actually has a bunch of videos of their products in use for a LIDAR application: http://sensl.com/applications/lidar1/

Continuous ToF calculation from a specially coded continuous signal is already used in Chinese military rangefinders (coded continuous signal is hard to spot unlike repetitive pulses)

In a ToF camera (at least some of them - see lock-in pixels), each pixel is sampled four times per cycle to detect the phase offset from the outgoing illumination. The SwissRanger was one of the first time of flight cameras. The reason it suffers short range is because of phase ambiguity - the lasers are modulated at around 30MHz which gives a wavelength of 10 m or so. The ambiguity distance is half this (5 m). LIDAR systems historically got round this by using multiple modulation frequencies for different distance scales.

This tech is now everywhere thanks to Microsoft buying Canesta.

It's not that better and better solutions are bad but there are probably a lot more gains in mitigating failure cases. I think our brains may even have dedicated systems for potential failure detection leading to timidness. Failure is currently mostly defined as lack of ability to get the right answer, it is negatively defined. I wonder is there's any merit in positively defining failure.

Another way to think of it is if timidness/shyness, typically "negative" traits (especially in American culture) are actually features rather than bugs.

> Consequently, the deflection is typically limited to tens of milliradians.

This is the big drawback with AOMs. They're very precise, but the angle through which they steer beams is small. You would need hundreds of scanners to scan a full circle under the best conditions.

AOMs are interesting though in that they provide a way to modulate the frequency of the light they deflect. This is how I've seen them used before: as a very precise way to introduce small frequency shifts into a laser beam for physics research.

To give you an example, this[1] commercially available sensor head can give great resolution stereo depth at around 10m but the included LIDAR unit is good out to around 30m.

LIDAR also has the advantage of operating on a different portion of the EM spectrum. It can sometimes be more well behaved in situations where there might be interference on the visible light wavelengths but not on the LIDAR wavelengths which is typically IR (e.g. extremely bright sunny days).

I think that there's a place for both in vehicles as a sort of redundancy. While they both mostly do the same things, they do them in different ways, and if LIDAR can be made cost effective then having both is a huge gain.

[1]: https://carnegierobotics.com/multisense-sl/

Even if AI gets it (mostly) right, getting a second input that verifies your path is clear seems like a good idea.

How much does a chauffeur cost for 100,000 miles?

- Frequency Division: each detector uses a different frequency/wavelength. This approach is very simple to implement, but the usable range of wavelengths for LIDAR is fairly narrow. So we'd run out of choices pretty quickly.

- Time Division: each detector is allocated a different operating time slot, so that only one is active at a time. For cell phones, this is relatively easy to implement because they all connect to a central system that can coordinate the timing. But for cars, this would be more difficult since there's no central system that links the detectors on different cars together (although they could still communicate with each other through other means).

- Code Division: each detector's output is pulsed in a unique pattern so that the reflected signals will return that same pattern, then the processor can reject any patterns that it didn't send out. This approach is much more complex, but doesn't suffer from many of the drawbacks of the other solutions.

Code division is by far the most widely used approach here, but frequency and time division also play a small role as well since different manufacturers/detectors use different wavelengths and not all detectors are being operated at exactly the same time (the time needed to send and receive a single signal is extremely short).

I worry that these modern cars will be basically requiring fighter jet storage in order to work. I need something that works in a snowstorm when the car is covered in ice.

My "parking sensors" (the cheap little distance sensing thingys mounted around most new cars) are screaming every time there is a little surface ice over them. I work around this by simply ignoring it. But if a car would require these sensors to function, which would be the case if it was autonomous, I'd be starting my morning drive by somehow getting the ice off them.

State-of-the art 79 GHz automotive radar: 0.2 m range uncertainty, 5 degree azimuth angular uncertainty, dozens of points per second.

Spinning lidar: <0.05 m range uncertainty, <0.1 degree azimuth angular uncertainty, a million points per second.

Radar definitely has its uses as it can see through weather conditions and tell velocity, but lidar is the vastly superior sensor otherwise.

I can imagine something similar to what you're proposing being a part of the overall system, I suspect there will always be the need for something vehicle-based that "sees" the environment.