Objectbased sensor model for virtual testing of ADASAD functions A novel generic sensor model is introduced for testing and validation of ADASAD (Advanced Driver Assistance SystemsAutonomous Drive) functions. The model converts an incoming object list into a sensor specific object list and is suitable for all ADASAD perception sensors operating on object level. The field of view of the sensor is represented by a two dimensional polygon, that can be defined by a set of points. A simple raytracing method is applied to simulate coverage by objects. The model allows multiple range specifications for a single sensor depending on object type and classification capabilities. A lookup table is used to convert the object class definitions of the virtual environment into the class definitions of the considered sensor. Additional object parameters that are detected by the sensor may be included. False negative and false positive detections are generated by probabilistic functions. The parametrisation procedure of the sensor model is explained and depicted in an example using the data sheet of the Radar Continental ARS404. The modelu0027s capability to simulate a complete sensor set is demonstrated with the sensor set of the Renault Zoe that was used to collect the nuScenes data set. The sensor model is integrated into a virtual testbed using Vires VTD and Open Simulation Interface.

Methods for Improving the Accuracy of the Virtual Assessment of Autonomous Driving Assessing the safety of an autonomous vehicle is an open problem within the research domain for autonomous vehicles. Next to realworld driving tests, simulation and reprocessing of recordings play a crucial role in validating the correct and safe behavior. Current stateoftheart methods for function reprocessing suffer from several sources of error and hence, might lead to incorrect results. In this work, an overview of the most recent reprocessing methods is given and their shortcomings are described. We suggest the derivation of explicit sensor models and the learning of behavior models for traffic objects. An overview of different levels of sensor and different kinds of agent models is given along with a discussion for the need for statistical and machine learning based models. Furthermore, a novel method, based on infrastructure sensors, to collect the data needed for the derivation of the models is presented.