Safety validation of automated driving functions is a major challenge that is partly tackled by means of simulation-based testing. The virtual validation approach always entails the modeling of automotive perception sensors and their environment. In the real world, these sensors are exposed to adverse influences by environmental conditions such as rain, fog, snow, etc. Therefore, such influences need to be reflected in the simulation models. In this publication, a novel data set is introduced and analyzed. This data set contains lidar data with synchronized reference measurements of weather conditions from a stationary long-term experiment. Recorded weather conditions comprise fog, rain, snow, and direct sunlight. The data are analyzed by pairing lidar values, such as the number of detections in the atmosphere, with weather parameters such as rain rate in mm/h. This results in expectation values, which can directly be utilized for stochastic modeling or model calibration and validation. The results show vast differences in the number of atmospheric detections, range distribution, and attenuation between the different sensors of the data set.

Simulation-based testing is playing an increasingly important role in the development and validation of automated driving functions, as real-world testing is often limited by cost, safety, and scalability. An essential part of this is the simulation of active perception sensors such as lidar and radar which enable accurate perception of the vehicle’s environment. In this context, the particular challenge lies in ensuring the credibility of these sensor simulations. This paper presents a novel method for the efficient and credible realization and validation of active perception sensor simulations in the context of the overall development process. Since the validity of these simulations is crucial for the safety augmentation of automated driving functions, the proposed method integrates a continuous verification and validation approach into the development process. Using this method, requirements like individual sensor effects are iteratively implemented into the simulation. Every iteration ends with the verification and validation of the resulting simulation. In addition, initial practical approaches are presented for validating measurement data required for the development process to avoid errors in data acquisition and for deriving quantified acceptance criteria as part of the validation process. All new approaches and methods are subsequently demonstrated on the example of a ray tracing-based lidar sensor simulation.