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bxsephjo t1_ivvjiq3 wrote

It seems like Waymo exclusive operates in Phoenix. If their models don't currently allow them to handle other cities well, would this be a case of their machine learning over-fitting the training set? And if so how do they overcome that in order to expand?


Leburgerking t1_ivwosvn wrote

They have about ~10 training cities around the US. Waymo also operates in San Francisco, and their automated trucking division is starting to operate in Texas. Some of their training cites include Kirkland, Washington and Novi, Michigan.


Ambiwlans t1_ivwrk3t wrote

Overfitting if they tried to just release this software everywhere i guess. But they're not doing that.

Their strat seems to be to learn each location vs tesla which is trying to learn to drive generally.


2001zhaozhao t1_iwccuik wrote

It's just a small pilot test, I would think they would start by testing in one city regardless of how advanced their tech is.


LeagueReplays2 t1_ivwkhef wrote

It's not an ML problem. Waymo uses Lidar, because that's what google started with over a decade ago and they never moved past it. Lidar is pretty bad for self-driving for many reasons, they hit a brick wall with it years ago and haven't moved past since.

So it's either scrap the company, or try to make something of it. So they picked a city with the second lowest rainfall anywhere in the US, and pre-programmed in some areas of the Phoenix metropolitan area into the car. Streets, lane counts, how to make turns at each intersection, how the lanes match up, markings...etc. Once pre-programmed, they lock the cars in the area and drive the pre-programmed routes, using Lidar to try not to bump into stuff.

The gimmick here is in the strange definition of level 4 autonomy. Unlike the other levels, level 4 allows for geofencing. This makes it pretty misleading since it means if you train your car to drive around an empty parking lot without a driver, and nowhere else, that's technically level 4 autonomy and you can market it as that. Cars have been able to self drive around a parking lot since the 90's if not earlier, and self navigate entire deserts since like 2001, so it's not really an impressive feat.

They're hoping it will be good enough to start robo taxi services in areas they can get the pre-programming working well enough to not keep failing. But there really isn't a point, they have to perpetually re-program the cars by re-scanning the entire geofenced area. It stops being economical the moment any other competitor makes actual self driving cars that aren't based on gimmicks.


No-Operation3052 t1_ix6j9jz wrote

LIDAR, paired with a very high definition map, can be very good. It's quite unlike the vision sensing that Tesla is trying to do. LIDAR, in general, won't hit things because LIDAR is very good at sensing obstacles (particularly in dry clear weather). So if you are a risk averse self-driving company then LIDAR makes a lot of sense because, above all, you don't want to hit anything.

Tesla's vision system still tries to hit things, a lot. It is still confused by what is actually in front of it, a lot. If you set a Tesla loose in Phoenix without a driver it would probably hit something within an hour. Also, if I'm not mistaken Waymo is also using vision to supplement the LIDAR. I think Waymo used the kitchen sink approach throwing virtually every kind of sensor at the problem of not hitting things.