KI Data Tooling

The University of Kassel is involved in the joint project "AI Data Tooling" along with 17 other partners from science and industry. The Federal Ministry of Economics and Energy (Bundesministerium für Wirtschaft und Energie - BMWi) provides a total of almost €850,000 in funding for the project over three years starting from April 2020. The joint project is part of the artificial intelligence project family of the initiative "Autonomes und Vernetztes Fahren" of the German automotive industry (VDA).

Artificial intelligence (AI) and especially machine learning are the key technologies of autonomous driving. Powerful computers and algorithms learn recognition and patterns, such as the automatic detection of traffic signs, other vehicles, or pedestrians in images, radar, or laser data.

The image depicts an urban traffic situation, in which an automated vehicle approaches an intersection. The automated vehicle perceives its environment using its built-in camera. The AI seeks to recognize all road users in the camera image. Since the weather is rainy and visibility is poor, the oncoming vehicle is not detected. However, to safeguard the automated driving functions, it is necessary that an AI also reliably operates in such difficult and usually critical situations. Therefore, within the AI Data Tooling project, we aim to develop tools that can automatically identify cases that are not well covered by the AI. We add theses automatically detected cases to enlarge the dataset and to improve the AI. In this respect, a large amount of sample data is required to "train" an AI and ensure its functionality. Such an automatically acquired database can shorten development cycles and test phases considerably. In the project "AI Data Tooling," we develop methods and tools for efficiently building a database for the perception of automated driving vehicles. These new methods, for AI, are also based on AI. We test these new methods for highly-automated and intelligent data acquisition on the case study involving the detection of vulnerable road users, e.g., pedestrians.

The researchers of the Department of Intelligent Embedded Systems (Prof. Dr. Bernhard Sick) are leading the sub-project "Quality Requirements and Efficiency Potentials of Data Generation and Provision" in the joint project "AI Data Tooling" together with BMW. They are also mainly responsible for the development of methods to detect corner cases. Corner cases are rare but often critical situations in road traffic. Besides, the University of Kassel is also concerned with the highly automated annotation and labeling of data for AI using active learning methods.situations in road traffic. Besides, the University of Kassel is leading the way in automating data refinement (annotation with additional information) using active learning methods.