Events and trade fairs

The availability of comprehensive 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 are recording more and more driving data during operation.

At the same time, the development of efficient data acquisition, storage and management techniques is also progressing rapidly. Furthermore, a wide range of mathematical tools is now available to analyze existing data sets and extract further information from them.

For example, methods of data analysis and machine learning (ML) are suitable for deriving dynamic prediction models based on data or for identifying structures, patterns and correlations in existing data sets. In addition to the vehicle and usage data mentioned above, the quantity and quality of the available environmental data is also constantly 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 usage data on the one hand and environmental data on the other.

The aim of this seminar is to teach fundamental 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.