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