###### Abstract  
Lidar technology is a key sensor for autonomous driving due to its precise environmental perception. However, adverse weather and atmospheric conditions involving fog, rain, snow, dust, and smog can impair lidar performance, leading to potential safety risks. This paper introduces a comprehensive methodology to simulate lidar systems under such conditions and validate the results against real-world experiments. Existing empirical models for the extinction and backscattering of laser beams are analyzed, and new models are proposed for dust storms and smog, derived using Mie theory. These models are implemented in the CARLA simulator and evaluated using Robot Operating System 2 (ROS 2). The simulation methodology introduced allowed the authors to set up test experiments replicating real-world conditions, to validate the models against real-world data available in the literature, and to predict the performance of the lidar system in all weather conditions. This approach enables the development of virtual test scenarios for corner cases representing rare weather conditions to improve robustness and safety in autonomous systems. lidar; adverse weather; rain; fog; snow; dust; particulate matter; simulation; Carla; Robot Operating System 2020  
12 June 202513 July 202513 July 202517 July 202517 July  
detected photons, the lidar will construct a point cloud of the surroundings, as illustrated in Figure 1. However, environmental phenomena, such as rain and fog, impact the lidar's performance by diverting photons and thus reducing the efficiency of the system and impacting the robustness of the system due to the noise associated with the perception of the environment. Many Advanced Driver Assistance Systems (ADAS) rely on sensing technologies and components such as lidar to underpin autonomous driving features. Higher levels of automated driving commonly employ a sensor fusion approach [1; 2] to centrally process the information from multiple sensors and combinations of sensors to enhance the robustness of the driving environment monitoring. Given the safety consequences associated with the robust and dependable operation of ADAS systems, which must be assured throughout the lifetime of the vehicle, suppliers of such technologies are legally obliged to prove the robustness of their products. The German industrial standard, exemplified by the OEM specific norm VW 80000 [3], specifies an expected lifetime of a vehicle of 15 years, an operating time of 8000 h and a mileage of 300,000 km. During the development phase, the product must be validated by performing various software, system, or hardware-related tests, such as rigorous accelerated life tests, which simulate the aging process during its lifetime, considering the diverse mission profiles experienced in real-world usage. As an example, to comply with the IEC 60068-1 [4], all OEMs have adapted their test specifications corresponding to the mission profiles and the climate and road conditions in their target markets. This requires extensive validation programs that are very costly in terms of resources and time, with accelerated test durations often exceeding 100 days. Validation also requires proving the functional safety of the component, as defined by ISO 26262 [5], which means that circumstances where the component might not be safe to use have to be robustly identified. The concept of \"corner case\" is often used in the literature relating to autonomous systems to refer to points that mark the limits of the safe zone. In the literature and in practice, there are different definitions of what a corner case is. For example, ref. [6] describes corner cases as non-predictable relevant objects located in a relevant position, whereas [7] includes overexposed sensors and partially concealed objects. A different approach is suggested by [8], where three types of corner cases are suggested.