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.

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