Whether it's well-known car manufacturer like Tesla or new technology companies like Google: many different companies are working on the development of autonomous vehicles. This emerging field provides a lot of potential and if you are interested in the basics of autonomous driving and what is necessary to bring an autonomous car to live, then this course is for you.
But don't be worried about that this is another theory-only lecture series: In this course we will work hands-on and lab centered for everyone who is interested in the field of artificial intelligence, motion planning, control theory, and applied machine learning. From the very beginning we will introduce you to both a simulation environment and real hardware: Our 1/10th-scale autonomous race car.
We will not only teach you about the theory, you will also learn how to program the algorithms. We will also show you how to bring your autonomous driving algorithms on the real F1TENTH hardware.
In the 4 week course we will teach you about the basics of autonomous driving and give you a glimpse of what is necessary to implement an autonomous driving function. We will show you different methods for creating a map and localizing the car afterwards on that map. In addition we will try out different path planners to make the car more agile on the racetrack.
After attending the course you will have a comprehensive overview of the perception, planning and control methods used in autonomous driving. You will be able to select the appropriate method and algorithm for various autonomous driving problems and then implement it with appropriate code. In addition you are able to program the F1TENTH Autonomous Race car to drive fast and safe around a racetrack. In addition you will learn:
This course is a beginners course in the field of autonomous driving. This course is focusing on undergraduate students with a basic knowledge in the field of math and good experience in the field of programming.
Rahul Mangharam is an Associate Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He is a founding member of the PRECISE Center and directs the Safe Autonomous Systems Lab at Penn. His research is at the intersection of formal methods, machine learning and controls for medical devices, energy efficient buildings and autonomous systems.
Johannes is a postdoctoral researcher at the University of Pennslyvania where he is working at the mLab: Real-Time and Embedded Systems Lab. In his current research he is focusing on the development of algorithms for autonomous vehicles – mostly for vehicles the operate on the limit of handling. This research also includes the integration of ethics for Level-5 autonomous vehicles.
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The car is an additional add-on and provides you the important insight in the deployment of the software on real hardware.