The virtual validation of automated driving functions requires meaningful simulation models of environment perception sensors such as radar, lidar, and cameras. There does not yet exist an unrivaled standard for perception sensor models, and radar especially lacks modeling approaches that consistently produce realistic results. In this paper, we present measurements that exemplify challenges in the development of meaningful radar sensor models. We highlight three major challenges: multi-path propagation, separability, and sensitivity of radar cross section to the aspect angle. We also review previous work addressing these challenges and suggest further research directions towards meaningful automotive radar simulation models.

Simulation-based testing is seen as a major requirement for the safety validation of highly automated driving. One crucial part of such test architectures are models of environment perception sensors such as camera, lidar and radar sensors. Currently, an objective evaluation and the comparison of different modeling approaches for automotive lidar sensors are still a challenge. In this work, a real lidar sensor system used for object recognition is first functionally decomposed. The resulting sequence of processing blocks and interfaces is then mapped onto simulation methods. Subsequently, metrics applied to the aforementioned interfaces are derived, enabling a quantitative comparison between simulated and real sensor data at different steps of the processing pipeline. Benchmarks for several existing sensor models at a concrete selected interface are performed using those metrics by comparing them to measurements gained from the real sensor. Finally, we outline how metrics on low-level interfaces can correlate with results on more abstract ones. A major achievement of this work lies within the commonly accepted interfaces and a common understanding of real and virtual lidar sensor systems and, even more important, an initial guideline for the quantitative comparison of sensor models with the ambition to support future validation of virtual sensor models.

Safety validation tests of automated driving (AD) use simulated environments and perception sensor models. To achieve the level of fidelity needed for safety approval, such sensor simulations can be physically based. For formulating requirements for sensor models to be used in such test frameworks, the extent to which they must include physical effects should be determined. One approach is to clarify their relevance for following processing steps like object detection or mapping. But at first, an analysis is needed to determine, which effects are relevant and if they can be implemented at all. In this work, we focus on one lidar sensor and analyze its observable real world sensor behavior to derive the possible effects, physically based lidar sensor models can include. Consequently, we describe environmental parameters that could be considered to influence physically based lidar sensor models. By investigating the specifications given by the manufacturer with own measurements, we show that some of them should be implemented in a dynamic manner. In conclusion, we enable to formulate detailed requirements for sensor models, as their actual possible fidelity is presented.

Radar sensors were among the first perceptual sensors used for automated driving. Although several other technologies such as lidar, camera, and ultrasonic sensors are available, radar sensors have maintained and will continue to maintain their importance due to their reliability in adverse weather conditions. Virtual methods are being developed for verification and validation of automated driving functions to reduce the time and cost of testing. Due to the complexity of modelling high-frequency wave propagation and signal processing and perception algorithms, sensor models that seek a high degree of accuracy are challenging to simulate. Therefore, a variety of different modelling approaches have been presented in the last two decades. This paper comprehensively summarises the heterogeneous state of the art in radar sensor modelling. Instead of a technology-oriented classification as introduced in previous review articles, we present a classification of how these models can be used in vehicle development by using the V-model originating from software development. Sensor models are divided into operational, functional, technical, and individual models. The application and usability of these models along the development process are summarised in a comprehensive tabular overview, which is intended to support future research and development at the vehicle level and will be continuously updated.

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.