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.

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 and 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 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.

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

smart devices that can recognize and predict how much power is going to be produced in

the near future.

So let's look at an example of smart devices.

So let's say you have a fridge or a washing machine.

You can program them in a way that they are rather flexible at the point in time when

they consume the energy because you can start the washing also maybe overnight or in one

hour when the price is lower.

So if you had smart devices that could predict the current prices then you would be able

to essentially balance the node of the energy system and at the same time remove the peak

conditions.

Let's say there is a lot of wind blowing then it would make sense that a lot of people cool

down the refrigerators or wash the dishes or wash the clothes.

So this can be done of course with recurrent neural networks and then predict how much