Dimensionality reduction and identification of valid parameter bounds for the efficient calibration of automated driving functions

摘要

The industrialization of automated driving functions according to level 3 requires an efficient test and calibration concept todeal with an increased complexity, growing customer demands, and a larger vehicle fleet offered. Therefore, a method fora complexity reduction of the calibration parameter space is presented. In the two-step approach, a qualitative sensitivityanalysis is used to identify valid regions in the search space and subsequently decrease dimensionality based on the parameterspecificglobal influences. The reduced parameter space and sensitivity information can then serve as a starting point foran efficient calibration process on the target hardware. To examine the method’s potential, our approach is applied to theparameter space of an automated driving function. The results expose clear dependencies between parameters and drivingscenarios and allow an exclusion of parameter space dimensions based on sensitivity values. The predefined search spacecan be narrowed down to valid regions using the parameter range identification approach. Finally, the findings are validatedwith a quantitative variance-based sensitivity analysis. The validation confirms that our method provides equivalent resultswith a comparably smaller number of system evaluations.