Modeling Perception Errors of Automated Vehicles

The assessment of automated driving relies increasingly on scenario-based virtual tests to achieve sufficient test coverage. Scenarios are generally based on ground truth information. Therefore, it is necessary to reproduce the view of the environment of the automated vehicle as it is seen by the autonomous driving function in the simulation. Typically, this view is erroneous compared to the ground truth due to sensor errors. This paper presents a novel approach to cluster, identify and finally to reproduce sensor errors by maneuver-dependent statistical models for the detection of other traffic objects. Errors are classified by their static and dynamic influences and incorporated into individual error models. These are evaluated in a final step based on real driving data.