Validating safety is an unsolved challenge before autonomous driving on public roads is possible. Since only the use of simulation-based test procedures can lead to an economically viable solution for safety validation, computationally efficient simulation models with validated fidelity are demanded. A central part of the overall simulation tool chain is the simulation of the perception components. In this work, a sequential modular approach for simulation of active perception sensor systems is presented on the example of lidar. It enables the required level of fidelity of synthetic object list data for safety validation using beforehand simulated point clouds. The elaborated framework around the sequential modules provides standardized interfaces packaging for co-simulation such as Open Simulation Interface (OSI) and Functional Mockup Interface (FMI), while providing a new level of modularity, testability, interchangeability, and distributability. The fidelity of the sequential approach is demonstrated on an everyday scenario at an intersection that is performed in reality at first and reproduced in simulation afterwards. The synthetic point cloud is generated by a sensor model with high fidelity and processed by a tracking model afterwards, which, therefore, outputs bounding boxes and trajectories that are close to reality.

The radar scattering characteristics of extended objects are an important parameter for perception and tracking algorithms in automated driving tasks. Therefore, high-fidelity sensor models are required to simulate and evaluate typical driving scenarios in virtual testing applications. While the general analysis of typical scattering centers of passenger cars is well studied, there are only a few publicly available reports that analyze specific features of the scattering characteristics of different vehicle types. Hence, this work presents detection distributions derived from systematic measurements for six different vehicle types, conducted with a commercial automotive radar on a proving ground. In particular, the contribution of underbody reflections to the respective radar signatures is analyzed, which are caused by multipath propagation via the road surface. The measurements reveal distinctive differences between the scattering characteristics of different vehicles, which are attributed to the respective underbody geometry.

The increasing angular resolution of modern automotive radar sensors enables a more detailed and more accurate perception of the environment. This has implications for the measured target detections of extended objects such as vehicles, which cause complex backscatter signatures. Simulation-based approaches strive to enable efficient testing concepts, but require high-fidelity sensor models. To assess the impact of different sensors on the virtual replication of sensor data, it is important to examine the variations between measurements from various sensors. In this letter, radar detections of a passenger vehicle using three radars with different characteristics are presented. Similarities between the spatial distributions of the detections are revealed and differences in the number of target detections and the accuracy of their localization within the bounding box of the vehicle are identified. Furthermore, the spatial fluctuations of point clouds between succeeding measurement cycles are investigated. The results suggest that existing data-driven modeling approaches can be applied to different sensors as well, but that particular attention must be paid to the distinct spatial spread of the detections and to the fluctuations of point clouds.