Seminar

The availability of rich vehicle data has been increasing rapidly for years – on the one hand, there are historical data sets from measurement campaigns and fleet observations, 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 acquisition, data storage and data management technologies is also progressing rapidly. In addition, a wide range of mathematical tools are now available to analyze existing data volumes and extract further information from them. Methods of data analysis and machine learning (ML), for example, are suitable for deriving data-based dynamic prediction models or for identifying structures, patterns and correlations in existing data volumes. In addition to the vehicle and customer usage data just mentioned, the quantity and quality of available environmental data is also constantly increasing. However, a profound benefit for the entire design, development and validation process often only arises through a combination of the two types of data mentioned: Vehicle or customer data on the one hand and environmental 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 use selected examples to show how these can improve the vehicle development process.