The chassis and driving characteristics design defines the customer benefit-centered and brand character-specific driving behavior already in the early concept phase and thus forms the basis for the first virtual prototypes.

The FKFS offers a modular toolkit of methods and functions and supports integration into our customers' development environment

• Design and tracking of objective driving characteristics
• Efficient modelling, optimized with regard to the design criteria
• Objective driving characteristics with a clear correlation to subjective goals
• Systematic target derivation from overall vehicle to subsystem properties using target cascading and solution space methods
• Efficient testing of subsystem properties fulfilling the target ranges of driving properties
• Solution Spaces show decoupled target spaces for independent development and guarantee the robustness of the solutions

Nowadays, vehicle parameters are still mainly calibrated on test tracks and often only in late stages of development process. This poses practical challenges, such as limited availability of tracks and vehicles, changing environmental conditions and high time and cost expenditure. The FKFS develops innovative calibration methods that can be carried out either completely virtually or hybrid (by integrating modern test benches). This enables the parameterization of vehicle functions at an early stage of the development process. This not only leads to considerable cost savings, but also delivers high-quality results.

• Subjective calibration of ride comfort functions
• Objective calibration in simulation or test bench measurement
• Virtual verification of AD/ADAS
• No deeper knowledge of functional relationships necessary (black box)
• Highly efficient optimization algorithms for complex systems (regarding number of measurements)

Artificial Intelligence (AI) has proven to be a versatile and powerful tool in numerous areas. The virtual development of driving characteristics also benefits significantly from these innovative technologies. Not only completely new functions can be implemented, but also existing development processes can be significantly optimized. The FKFS uses AI methods specifically in various application of driving characteristics development:

• Anomaly detection: Automated identification of deviations in driving behavior through cluster recognition.
• Tire properties: Use of AI to evaluate and interpret contact areas with the road surface.
• Vehicle calibration: Efficient parameterization and adaptation of vehicle systems with minimal effort.
• Comfort analysis: Use of statistical or stochastic models to simulate subjective evaluations.
• Autonomous driving: Decision-making and driving strategy planning for autonomous vehicles.

The use of appropriate models is essential for theoretical and simulative analyses of vehicle behavior. Custom-designed modeling is necessary in order to achieve an optimum balance between mapping quality and parameterization effort. The FKFS works with established simulation tools from the automotive industry and also develops individual solutions as requested. The expertise of the FKFS includes:

• Full vehicle models
• Component models
• Driver models
• Environment models
• Models of active chassis systems