The MOOC "Automated and Connected Driving Challenges (ACDC)" introduces participants to some of the latest research challenges and provides the possibility to develop and test automated and connected driving functions step by step.
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Automated and connected driving is a major topic in automotive research and industry at the moment. The MOOC "Automated and Connected Driving Challenges (ACDC)" introduces participants to some of the latest research challenges and provides the possibility to develop and test automated and connected driving functions step by step.
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.
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.
Syllabus
Week 1-3: Introduction & Tools
Introduction to current challenges in automated and connected driving
Introduction to the course tools and setup
Introduction to the Robot Operating System (ROS1 & ROS2 Outlook)
Week 4-7: Sensor Data Processing
Introduction to Sensor Data Processing
Semantic Camera Image Segmentation
Semantic Point Cloud Segmentation
Object Detection in Point Clouds
Occupancy Grid Mapping using Point Clouds
Camera-based Semantic Grid Mapping
Vehicle Localization
Week 8-9: Object Fusion and Tracking
Introduction to Object Fusion and Tracking
Object Prediction
Object Association
Object Fusion
Week 10-11: Vehicle Guidance
Introduction to Vehicle Guidance
Navigation-Level
Guidance-Level
Stabilization-Level
Week 12-13: Connected Driving
Introduction to Connected Driving
Collective Cloud Functions
V2I-Communication
Week 14-15: Final Exam Period
We suggest you take between one and two weeks to recap the materials of the course and then to finish the exam. Of course, you may take the exam whenever you prefer, if you completed the course earlier than planned.
(Optional) Week 14+
Self-paced work on an automated and connected driving challenge you may choose
List of challenges, instructions, data, supporting materials are provided
Challenges can be tackled alone or in groups
Your results may be published on your personal GitHub page
