Philipp Rigoll

• Master of Science
• FZI Forschungszentrum Informatik

Automated Driving Systems (ADS) development relies on utilizing real-world vehicle data. The volume of data generated by modern vehicles presents transmission, storage, and computational challenges. Focusing on Dynamic Behavior (DB) offers a promising approach to distinguish relevant from irrelevant information for ADS functionalities, thereby redu...

Huge image data sets are the foundation for the development of the perception of automated driving systems. A large number of images is necessary to train robust neural networks that can cope with diverse situations. A sufficiently large data set contains challenging situations and objects. For testing the resulting functions, it is necessary that...

Handling large amounts of data has become a key for developing automated driving systems. Especially for developing highly automated driving functions, working with images has become increasingly challenging due to the sheer size of the required data. Such data has to satisfy different requirements to be usable in machine learning-based approaches....

The transition to autonomous driving is one of the major trends for the transformation of the automotive industry. Highly automated features are required to always operate and ensure robust functionality and safety. This raises the challenge for exhaustive training and test data during development, but recording real-world data is not sufficient en...

The validation of Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) is a major challenge for the automotive industry. Scenario based testing is a promising validation approach, but relies on a scenario database with a high coverage of possible scenarios. Especially in urban areas, the presence of different traffic object...