Seminar

[The seminar is only available in German.]

The availability of rich vehicle data has been increasing rapidly for years - on the one hand, historical data sets from measurement campaigns and fleet observations exist, and on the other hand, modern vehicles in companies are recording more and more driving data.

At the same time, the development of efficient data collection, data storage and data management techniques is also progressing rapidly. In addition, a diverse range of mathematical tools is also available today for analyzing existing data sets and extracting further information from them. Methods of data analysis and so-called machine learning (ML), for example, are suitable for deriving data-based dynamic prediction models or for identifying structures, patterns and correlations in existing data sets. In addition to the vehicle or customer usage data just mentioned, the quantity and quality of available environmental data is also steadily increasing. However, a profound benefit for the entire design, development and validation process often only arises from a combination of the two types of data mentioned: Vehicle or customer data on the one hand and environment data on the other.

The aim of this seminar is to teach basic methods, procedures and techniques from the fields of data analysis and machine learning and to show, using selected examples, how these can improve the vehicle development process.