Google explores these techniques as well. They're critical to improving accuracy in urban areas where you don't have line of sight to satellites (this is why your GNSS (AKA GPS) location is terrible whenever you're in a city). Here's [0] a cool gif visualizing the principle of GNSS ray tracing taken from one of Google's blog posts [1]. Here's an article with more information about Google's techniques [2].

[0] https://1.bp.blogspot.com/-FskvGHx-IOc/X8qtZi1GUNI/AAAAAAAAP...

[1] https://android-developers.googleblog.com/2020/12/improving-...

[2] https://insidegnss.com/end-game-for-urban-gnss-googles-use-o...

Cool, so it looks like they take the “raw” GPS location (after the GPS module does processing to spit out a lat/long) and applies statistical methods with a model of the actual 3D geometry. Seems like a smart way to improve the accuracy from “userspace”.

This reminds me of the Comma.ai project, Laika [0]. Since they have access to the raw GPS module data (Ie the actual timings of each received satellite transmitting), they can do things such as correcting for troposphere/ionosphere delays by looking up the relevant astro data as recorded by NASA and others.

[0]: https://github.com/commaai/laika

This should probably be called "use-specific gps optimization". These are ideas that improve accuracy for specific use cases (cars on roads) but may introduce errors for other users (people in buildings). We see this with googles driver-focused navigation whereby it always pulls you to a road, even though you may be well off the road.

Ive seen this come up in courts. A gps record will say that a person is traveling on highway X instead of parrallel street Y based on speed or direction of travel. When someone is doing something unussual (high speed in wrong direction) mapping software optimizations may go haywire. This is fine, so long as we all understand the difference between the underlying GPS track and a software-optimized track that incorporates logic beyond simple gps position.

Take the unussual situation of a large traffic accident. We dont want the EMT position to be reported as being on the sidewalk just because they are walking. Or after an earthquake. We dont want trapped survivor positions to change based on expected interfearance from tall buildings, buildings which might not be there anymore.

"2018" post.

L5 GPS will address the multipath-in-urban-canyon problem that Uber worked on.

What happened to the next gen gps technology, I thought it was supposed to be out by now. Centimeter level precision, much better urban (blocked by buildings) accuracy, etc?

Urban canyons will be properly solved by using small phased array antennas for GPS reception. Then one can detect which signals are coming from the wrong direction and therefore must be reflections.

Phased array antennas also allow GPS-based compasses which should be far better than magnetic compasses which can be rather inaccurate.

Such phased array antennas are already coming into use for more throughput for 5G and WiFi use, and antenna sharing is feasible although not currently done commonly.

https://news.ycombinator.com/item?id=16887276 Old HN thread

Needs (2018) in the title.

Surprised I don't see Miles (https://www.linkedin.com/in/miles-detrixhe-32a979122/) on here - I remember the initial prototypes for this were his.

Most of the Uber cabs work in urban settings where there are bunch of Wi-Fi enabled. Could they use the Wi-Fi to pinpoint the location instead of relying on GPS? Probably they can setup some relay nodes on their own for coverage.

No mention of iOS in the video or article. Anyone knows if Uber does something similar on iOS, and if Apple has done any similar efforts in system-level APIs like Google has?

Are there open data for this type of research? If someone would like to improve their algorithms.

I'm curious if they'll make use of the L5 GPS band now that popular phones are getting it.

(2018)

Their internal app has ads for other Uber services? Oof.

This is an old article. Please label it likewise.

How about make your drivers use semi decent phones that don’t lag GPS?

Was just in an Uber in Miami and the driver was telling me how the service stopped working for 3 hours a week ago. It seemed to be local to the area around Miami only, though. Anyone know if this is true?

It has a shorter wavelength so the correlation peaks are more narrow and hence more easily distinguishable. See this illustration [0] taken from this article [1]

[0] https://insidegnss-com.exactdn.com/wp-content/uploads/2021/0...

[1] https://insidegnss.com/end-game-for-urban-gnss-googles-use-o...

To oversimplify: It's on a different frequency and so will be subject to different forms of error. You can average the results of L1 and L5 to get a more accurate position.