Professional Course

Automated and Connected Driving Challenges

edX, Online
16 weeks
16 weeks
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Course description

Automated and Connected Driving Challenges

This course first provides a comprehensive introduction to the Robot Operating System (ROS), which is a popular software framework for automated vehicle prototypes. On this basis, participants then learn how to develop and integrate modules for sensor data processing, object fusion tracking, vehicle guidance, and connected driving. In particular, this MOOC allows participants to

  • develop functions for automated and connected vehicles using Python and C++;
  • integrate their developed functions into the Robot Operating System (ROS);
  • train neural networks for environment perception tasks using TensorFlow;
  • learn how to use tools like: Linux, Terminal, Docker, ROS, RVIZ, Juypter Notebooks, Git.

At the end of the course, you may optionally choose from a provided list of open research challenges and start working on your own contribution to automated and connected driving.

Who should attend?


  • Basic linear algebra and calculus skills are advantageous
  • Basic programming skills with Python and C++ are advantageous
  • Basic skills in using Linux and command line interfaces are advantageous
  • A Linux operating system as a native operating system or, e.g., as a virtual machine, is necessary to execute programming exercises and to work with ROS. We provide instructions to set up the coding environment on your computer.
  • If possible, you should use a computer with an x86 CPU architecture. There is only limited support for machines with an ARM CPU architecture (Mac M1, M2 etc).

Course delivery details

This course is offered through RWTH Aachen University, a partner institute of EdX.

3-8 hours per week


  • Verified Track -$50
  • Audit Track - Free

Certification / Credits

What you'll learn

After completing the course, you will be able to

  • contribute to current research challenges in automated and connected driving;
  • program functions for automated and connected driving using Python C++;
  • integrate your developed functions into the Robot Operating System;
  • train neural networks, e.g. with TensorFlow;
  • evaluate your developed functions.

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