  This paper outlines the winning solutions employed in addressing the MUAD
uncertainty quantification challenge held at ICCV 2023. The challenge was
centered around semantic segmentation in urban environments, with a particular
focus on natural adversarial scenarios. The report presents the results of 19
submitted entries, with numerous techniques drawing inspiration from
cutting-edge uncertainty quantification methodologies presented at prominent
conferences in the fields of computer vision and machine learning and journals
over the past few years. Within this document, the challenge is introduced,
shedding light on its purpose and objectives, which primarily revolved around
enhancing the robustness of semantic segmentation in urban scenes under varying
natural adversarial conditions. The report then delves into the top-performing
solutions. Moreover, the document aims to provide a comprehensive overview of
the diverse solutions deployed by all participants. By doing so, it seeks to
offer readers a deeper insight into the array of strategies that can be
leveraged to effectively handle the inherent uncertainties associated with
autonomous driving and semantic segmentation, especially within urban
environments.
