Automated and Connected Driving Challenges

Hinweis: Dieser Kurs wird ausschließlich in Englisch angeboten.

Automated and connected driving is a major topic in automotive research at the moment. The module "Automated and Connected Driving Challenges (ACDC)" introduces students to the latest research challenges and provides the possibility to develop and test automated and connected driving functions step by step. The skills acquired in this course enable students to play an active role in shaping future Intelligent Transport Systems that are safe, sustainable, and accessible to all.

This course first provides a comprehensive introduction to ROS (Robot Operating System), which is a popular software framework for automated vehicle prototypes. On this basis, students then learn how to develop and integrate modules for sensor data processing, object fusion and tracking, vehicle guidance and V2X communication. In particular, the students will:

  • Develop functions for automated and connected vehicles using Python and C++
  • Integrate their developed functions into the Robot Operating System
  • Train Neural Networks for Computer Vision tasks with Tensorflow
  • Learn how to use tools like: Linux, Terminal, ROS, RVIZ, Juypter Notebooks, Git

All course materials will be accessed on-demand through the MOOC portal. This allows self-paced learning throughout the semester. Frequent quizzes allow the learner to regularly test their comprehension of the materials.

While the course Automated Driving gives a general overview of automated and connected driving and presents more established aspects of the field, ACDC provides the possibility to get to know and work on the latest research challenges in automated and connected driving. In the subsequent and voluntary module "Automated and Connected Driving Challenges - Research Project", students may conduct their own a research resulting in a small research paper.

Previous knowledge expected
Students should be familiar with at least one high-level programming language in advance (Java, Python, C++, ...).


Learning Outcomes

  • Students are able to contribute to current research challenges in automated and connected driving
  • Students can train neural networks and program functions for automated and connected vehicles, and are able to evaluate their developed software.
  • Students can integrate their developed software into the Robot Operating System (ROS).


Bastian Lampe M.Sc.


Till Beemelmanns M.Sc.
+49 241 80 26533


Maschinenbau M.Sc. Wirtschafts-Ingenieur M.Sc. Fahrzeugtechnik und Transport M.Sc. Automotive Enginieering M.Sc.






Vernetzung K.I. Intelligente Mobilität


Blended Learning


We also offer this lecture as a certified MOOC on making it available for everyone who is not enrolled at RWTH Aachen University.

This course replaces the previously offered courses "Self-Driving Lab I - Software Framework" and "Self-Driving Lab II - Algorithms" and incorporates their content.

The creation of this module was made possible through the support of the Rectorate, Division 6.2 - Teaching and Learning and Media for Learning of RWTH Aachen University.


Institut für Kraftfahrzeuge
RWTH Aachen University
Steinbachstraße 7
52074 Aachen · Deutschland
+49 241 80 25600

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