So thank you very much for tuning in again and welcome to our second part of the deep

learning lecture and in particular the introduction.

So in the second part of the introduction I want to show you some research that we are

doing here in the pattern recognition lab here at FAU and let's switch to the presentation

mode.

Okay one first example that I want to highlight is a cooperation with Audi and here we are

working with assisted and automated driving and we are working on smart sensors in the

car.

You can see that an Audi A8 today is essentially a huge sensor system which has cameras and

different sensors attached that are processed in real time in order to figure out things

about the environment.

In the future any car that does not have autonomy would be about as useful as a horse.

So it has functionalities like parking assistance, there is also functionalities to support you

during driving and traffic jams and this is all done using sensor systems.

So of course there is a lot of detection and segmentation tasks involved.

What you can see here is an example view where we show some output of what is recorded by

the car.

This is a frontal view where we are actually looking around the surroundings and there

you have to detect cars, you have to detect here shown in green the free space where you

can actually drive to and all of this has to be detected and many of these things are

done with deep learning today.

Of course there is a huge challenge because we need to test the algorithms, often this

is done in simulated environments and then produce a lot of data in order to make them

reliable but in the very end this has to run on the road which means that you have to consider

different periods of the year, different daylight conditions and this makes it all extremely

hard.

What you have seen in research and many of the detection results is that they are working

with like nice day scenes and the sun is shining, everything is nice so the algorithms work

really well here.

Very, very quickly, maybe even towards the end of this year but I would say I would be

shocked if it is not next year, at the latest, that having a human intervene will decrease

safety.

But the true challenge is actually to go towards rainy weather conditions, night, winter, snow

and you still want to be able to detect of course not just cars but traffic signs, landmarks

and then analyze the scenes around you such that you have a reliable prediction towards

your autonomous driver system.

I can imagine if you are in an elevator.

It used to be that there were elevator operators and you couldn't go in an elevator by yourself

and work the lever to move between floors and now nobody wants an elevator operator

because the automated elevator that stops the floors is much safer than the elevator

operator and in fact it would be quite dangerous to have someone with a lever that can move

the elevator between floors.

We also look into smart devices and of course some very interesting topic here is renewable

energy and power.

One problem that we typically face is in under production when there is not enough wind blowing

or when not enough sun is shining you have to fire up backup power plants and of course

you don't want to do over production because that would produce energy that cannot be stored

very well.

So the storing of energy is not very efficient right now.

So there are some ideas to go towards this like real time prices but what you need are