Simulation-Based Parameter Identification for Accuracy Definitions in Virtual Environment Models for Validation of Automated Driving


For testing and validation of automated driving functions, simulations are absolutely essential to manage the required test effort. Therefore, the simulation models need to be modeled adequately in order to use the simulation results for virtual validation. As the required accuracy in virtual environment models is not clearly defined, this contribution investigates and quantifies accuracy requirements for the static domain of virtual environment models. By the use of an appropriate sensitivity analysis and a unique metric for the evaluation of simulation results suitable parameters are identified and statistically analyzed for validity and sensitivity assessment for a highway scenario. The results reveal that influences on the creation of virtual environment descriptions for automated driving could be derived and used for defining requirements in the generation and updating of virtual test fields.

2021 IEEE Intelligent Vehicles Symposium (IV)