Tesla Autopilot

Post by falcolas » Sat, Jun 17, 2017, 2:13 PM UTC

jacquesm wrote: post #217

Animats wrote: post #64

Watch this at 0.25x speed or slower to see what's going on. This is a carefully chosen environment. Every place it drives has very clear highway centerline markings. It seems to be highly dependent on those for guidance. Sometimes it can't quite identify the road edge, but the centerline provides a position reference. The inputs seem to be road line recognition, optical flow for the road, and solid object recognition…

One day some joker with two pots of paint (one white, one gray) is going to have a field day causing cars to run off the road.

Post by falcolas » Sat, Jun 17, 2017, 2:17 PM UTC

ErikHuisman wrote: post #154

smitherfield wrote: post #91

Earlier quoted context omitted.

When you buy a car, the title is signed over to you, and you become its owner. Microsoft Office is information that Microsoft agrees to share with you subject to certain restrictions on your use of it. To actually purchase Microsoft Office the intellectual property, as opposed to a license for it, would cost you about 9 orders of magnitude more. They're also restricting their customers' actions in meatspace (you can'…

tesla is restricting the autopilot software not the car itself

Post by myko » Sat, Jun 17, 2017, 2:45 PM UTC

dingo_bat wrote: post #232

TeMPOraL wrote: post #180

Earlier quoted context omitted.

The thing is, human "machine vision" has graceful failure modes. You don't switch from seeing cars to seeing nothing to seeing an elephant just because the input got a little noisy. The same cannot be said about current ML demonstrations - because they operate on just vision. Humans continuously reconcile visual input with their model of the world and with other inputs, to the point of overriding visual data if neede…

Another issue is detecting issues and fixing them. For example, if dirt gets into my eyes while driving, I immediately know and do what is necessary to clean my eyes before continuing on my drive. On a car, what if there's a small spec of dust on a camera that blocks some part of the view. Is the car smart enough to figure out something is wrong and refuse to go?

Post by andybak » Sat, Jun 17, 2017, 2:54 PM UTC

factsaresacred wrote: post #155

prostoalex wrote: post #126

Earlier quoted context omitted.

The dash cam videos would be correct, outside of high-occupancy lane (2+ people) there are no lane speed restrictions, and if someone feels like getting into the left-most lane and driving 5 miles below the speed limit, they most certainly act on that impulse. As you can imagine, anybody intending to use the left-most lane for high-speed driving is now forced to use the brakes, execute a [rarely safe] lane merge to t…

I only recently discovered that in the US many people driving in the left lane will not move to the middle lane to allow cars approaching from behind overtake them. That's incredibly selfish, not least because it's incredibly dangerous for the driver that has to weave into the middle lane and back again. Seems bizarre. Then again, you Yanks also don't have kettles, so nothing surprises me ;)

Post by dsfyu404ed » Sat, Jun 17, 2017, 2:57 PM UTC

Retric wrote: post #230

obastani wrote: post #203

Earlier quoted context omitted.

Humans know when they can't see properly. The problem with adversarial examples is that the neural net thinks it's doing great. You have to manually specify every possible failure mode for the neural net before hand -- if you miss one, you might have an accident.

The real world is not filled with adversarial examples. Further, NN often does not think it's doing great when providing off the wall examples. Further this is a car, with GPS, where simply stopping is a completely viable option 99% of the time. The truth is people really really stuck a driving, but it's easy enough that the vast majority of mistakes don't result in any problems.