Skip to content Skip to mainnavigation Skip to footer

Research Laboratory Automated Driving

The content of the research laboratory are feasibility studies within the following topics

► Introduction of modern automation methods (adaptive, self-optimizing and predictive algorithms) incl. realization of these complex methods/algorithms on industrial control devices

► Usage of soft sensors, digital twin, predictive analytics and quality methods as health monitor for safety functions

► Utilization of novel automation approaches from the Automated Driving (AD) area for I4.0 applications with the goal of knowledge transfer

► Intelligent decision making and optimal motion planning (Path Planning MPC)

► Using AI methods in order to enable self-adapting / self-optimizing AD systems

  • Development of key performance indicators (KPI) including intelligent KPI management
  • Use AI for online controller attachments (min. commissioning and application time + max. comfort and acceptance)
  • Increasing the energy efficiency of automation solutions through intelligent controller optimization → GLOSA

► Cooperative driving functions (intersection, lane change, ACC, GLOSA)

  • Designing a cognitive overall system based on connected individual vehicles
  • Single agents can act, learn and make decisions semi-autonomously

Illustration: Frank Schrödel, Stephanie Brittnacher & Melanie Freund

👋 Hello!

I’m here to answer your questions about studying

HS Schmalkalden Chatbot

👋 Hello!

This is a chatbot designed to answer questions from prospective students.
By using the chatbot, you agree that the data you enter (e.g., questions and answers) will be transmitted to an external service provider and processed there. Data processing is carried out exclusively for the purpose of providing the chatbot function. You can object to data processing at any time. For more information, please see our privacy policy.