###### Abstract
The first type covers hardware-related cases, such as a broken lens, whereas the second type is concerned with the attributes of the environment, where, for example, a person in a  
Figure 1: Both Figures depict the same urban scenario: (**a**) View of the front camera. (**b**) Default point cloud. costume could not be detected as a relevant object. A third type is defined as the temporal layer related to cases where the sequence of individual scenes forms an overall result, such as the observation of a traffic accident. In order to identify or construct such corner cases, simulations or real ADAS components can be operated in large environmental simulation chambers to record datasets containing corner cases. Significant effort was made to evaluate the impact of the weather conditions on the lidar performance. [PERSON] et al. The work in [9] laid the foundation in this topic by providing an overview of the physics of the lidar detection during adverse weather and estimating the theoretical disturbance of the detection. An expansion of this work was presented in [10], where the physical model of the backscattered noise was improved and a computer algorithm to simulate adverse weather situations was introduced. In the meantime, experiments on real lidar systems under defined weather conditions were reported in [11; 12], with an analysis of the detection performance. However, the prior work only focused on weather conditions, which are common in Europe, namely rain, fog, and snowfall. As lidar systems will be utilized in all parts of the world, dust and polluted air may have a significant impact on the automotive lidar detection performance. Furthermore, empirical models for calculating the extinction and backscattering coefficient are not available for all weather conditions, although they are essential for a realistic simulation-based validation of the lidar performance under real-world conditions. The aim of this paper is to establish a comprehensive methodology for modeling and simulating lidar systems under the influence of adverse weather conditions. The contributions of the work presented in this paper are twofold: (i) revised empirical models for the extinction and backscattering coefficients are introduced for all of the five major environmental factors, namely rain, fog, snowfall, dust and particulate pollution with potential impact upon the efficiency and safety of automotive lidar applications; and (ii) the development and validation of a physics-based virtual test environment for the simulation and evaluation of automotive lidar systems under real-world adverse weather and traffic conditions. The organization of this paper is as follows: Section 2 provides a literature review of the mathematical and physical models. In Section 3, the methodology of this work is explained by articulating two research questions and the setup of the simulation environment. Section 4 discusses the proposed empirical laws for the weather-related coefficients, and Section 5 contains a comparison of simulations, which utilize the proposed empirical models, and real-world experiments. Finally, in Section 6, the results are discussed, and in Section 7, this work comes to a conclusion.