These are the lecture notes for FAU’s YouTube Lecture “Deep Learning”. This is a full transcript of the lecture video & matching slides. We hope, you enjoy this as much as the videos. Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. If you spot mistakes, please let us know!
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Previous Lecture** / Watch this Video / Top Level / **Next LectureSo, 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 Germany.
Today’s cars are huge sensor systems. Image under CC BY 4.0 from the Deep Learning Lecture.
One first example that I want to highlight is a cooperation with Audi and here we are working with assisted and automated driving. We are working on smart sensors in the car. You can see that the Audi A8 today is essentially a huge sensor system that has cameras and different sensors attached. Data is processed in real-time in order to figure out things about the environment. It has functionalities like parking assistance. There are also functionalities to support you during driving and traffic jams and this is all done using sensor systems. So of course, there’s a lot of detection and segmentation tasks involved.
Online road scene analysis. Full video can be found here. Image generated using gifify.
What you can see here is an example, 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 have to detect cars. You also 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’s a huge challenge because we need to test the algorithms. Often this is done in simulated environments and then a lot of data is produced 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 and different daylight conditions. This makes it all extremely hard. What you’ve seen in research is that many of the detection results are working with nice day scenes and the sun is shining. Everything is nice, so the algorithms work really well.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 and landmarks. Then you analyze the scenes around you such that you have a reliable prediction towards your autonomous driver system.
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