Google Using Self-Driving Car Data To Make Cars Smarter

cartechboy (2660665) writes "One thing Google has perfected is using massive data sets generated from users to improve user experience. Google's self-driving cars may be subject to the same cycle of improvement, as they have racked up considerable mileage on public roads, and each mile generates data that Google engineers can use to 'teach' vehicle. Meet Pricilla — a Google test driver on the self-driving car project as she does a video walk through of some of the improvements created so far. Some are fairly simplistic, for example: 'The car does move to avoid large obstacles." That said, the car can also detect a bicyclist signaling and stay clear — oddly, even when that cyclist changes his mind and zig zags a little." Google is now testing cars on the city streets of Mountain View.

Still waiting to see 3 things

[NewWorldDan]

1. Cop directing traffic
2. A more complicated construction zone with a badly marked detour
3. A snow storm

Things are coming along nicely, but I still imagine these are a decade away. Still, they should be common and affordable by the time I'm ready to plow through a farmer's market.

In the future...

[Ichijo]

...our children and grandchildren would wonder why we ever allowed humans to operate motor vehicles on public roads.

Re:Fucking Cyclists are ruining the future.

[Ichijo]

What if there's no bike lane, or it's filled with debris, or the bicyclist needs to make a left turn?

Re:Fucking Cyclists are ruining the future.

[PvtVoid]

If grid lock, anger, douch baggery, and killing automated cars is you goal, then sure. Otherwise, we will just have the self righteous assholes going 15 MPH in a 25 MPH zone becasue they are too precious to use the bike lane.

And the car will have the simple common sense to wait ten or fifteen seconds until it's safe to go around the cyclist, who will inevitably catch up at the next traffic light anyway. This behavior seems to elude humans.

Re:Still waiting to see 3 things

[Missing.Matter]

I know what they use. I use the same sensors. Rain is a problem. Dust is a problem. Snow is a HUGE problem. Of the sensors on the car, the Velodyne HDL-64E on top is by far the most important, and provides the most critical data used for localization and detecting dynamic objects. With rain in particular laser beams can get refracted or reflected by the raindrops, which incredibly fuck up your distance measurements. The typical solution is to do a lot of filtering (i.e. take the median of two measurements) but this cuts down your effective frame rate, which already isn't that high to begin with.

Snow pretty much guarantees that "manual operation mode" is going to be a primary interface in autonomous cars for a long time to come, as we wait for not only sensors to get up to speed, but also machine learning in general.