Automated and Connected Driving Challenges

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

1. Learn how to develop software for automated and connected driving
2. Learn AI fundamentals and how to train neural networks
3. Integrate your software into the Robot Operating System