The hygrothermal aging of short glass fiber-reinforced polyamide 6 materials (PA6 GF) represents a major problem, especially in thin-walled components, such as in the automotive sector. In this study, therefore, the thickness and the glass fiber content of PA6 GF materials were varied and the materials were exposed to hygrothermal aging. The temperature and relative humidity were selected in the range from −40 °C up to 85 °C, and from 10% up to 85% relative humidity (RH). In the dry-as-molded state, the determined Poisson’s ratio of the PA6 GF materials was correlated with the fiber orientation based on computer tomography (MicroCT) data and shows a linear dependence with respect to the fiber orientation along and transverse to the flow direction of the injection molding process. With hygrothermal aging, the value of Poisson’s ratio increases in the flow direction in the same way as it decreases perpendicular to the flow direction due to water absorption.

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.

The validation of sensor systems, particularly lidar, is crucial in advancing autonomous vehicle technology. Despite their robust perception capabilities, certain weather conditions and object characteristics can challenge detection performance, leading to potential safety concerns. This study investigates corner cases where object detection may fail due to physical constraints. Utilizing virtual testing environments like Carla and ROS2, simulations analyze how reflection characteristics affect detectability by implementing weather models into a real-time simulation. Results reveal challenges in detecting black objects compared to white ones, particularly in adverse weather conditions. A time-sensitive corner case was analyzed, revealing that while bad weather and wet roads restrict the safe driving speed range, complete deactivation of the driving assistant at certain speeds may be unnecessary despite current manufacturer practices. The study underscores the importance of considering such factors in future safety protocols to mitigate accidents and ensure reliable autonomous driving systems.