Data Analysis and Machine Learning in Vehicle Engineering


About once a year, our department offers a two-day seminar on the topic of »Data Analysis and Machine Learning in Vehicle Development«. The seminar is held in German.



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.

Event Location 

Fraunhofer ITWM, Fraunhofer-Platz 1, 67663 Kaiserslautern


Costs and Registration  

The participation fee is €1300 and includes the conference documents, lunch and drinks.


Next Date  

The seminar is scheduled to take place again in June 2025.

If you are interested in an in-house seminar, please do not hesitate to contact us.

Save your place now


The seminar is divided into two blocks. The first block introduces the basics of various methods and procedures of data analysis and machine learning, while the second block illustrates their application and use in the vehicle development process using concrete example scenarios.


  • Fundamentals of data analysis
  • Introduction to machine learning
  • Procedures and methods of machine learning
    • Supervised and unsupervised learning
    • Reinforcement Learning
    • Methodological introduction and algorithmic aspects
  • Identification and approximation of dynamic systems for prediction and control

Application Scenarios  

  • Application and driving condition recognition based on customer data to derive usage profiles
  • Data-based derivation of driver and operator models
  • Data analysis and machine learning in the simulation and control of traffic systems
  • Automated structuring and classification of environmental data
  • ML systems for approximating nonlinear vehicle subsystems / virtual sensors
  • ML systems as surrogate models for increasing efficiency in simulation chains and optimization applications


  • Dr. Michael Burger, Deputy Head of the Dapartment »Dynamics, Loads and Environmental Data« 
  • Dr. Jochen Fiedler, Fraunhofer ITWM
  • Dr. Klaus Dreßler, Head of the division »Mathematics for Vehicle Engineering« and Head of the department »Dynamics, Loads and Environmental Data«
  • Dr. Michael Speckert, Deputy Head of the Dapartment »Dynamics, Loads and Environmental Data«