Various adverse weather conditions pose a significant challenge to autonomous driving (AD) street scene semantic understanding (segmentation). A common strategy is to minimize the disparity between images captured in clear and adverse weather conditions. However, this technique typically relies on utilizing clear image as a reference, which is challenging to obtain in practice. Furthermore, this method typically targets a single adverse condition, and thus perform poorly when confronting a mixture of multiple adverse weather conditions. To address these issues, we introduce a reference-free and Adverse weather-Immune scheme (called AdvImmu) that leverages the invariance of weather conditions over short periods (seconds). Specifically, AdvImmu includes three components: Locally Sequential Mechanism (LSM), Globally Shuffled Mechanism (GSM), and Unfolded Regularizers (URs). LSM leverages temporal correlations between adjacent frames to enhance model performance. GSM is proposed to shuffle LSM segments to prevent overfitting of temporal patterns. URs are the deep unfolding implementation of two proposed regularizers to penalize the model complexity to enhance across-weather generalization. In addition, to overcome the over-reliance on consecutive frame-wise annotations in the training of AdvImmu (typically unavailable in AD scenarios), we incorporate a foundation model named Segment Anything Model (SAM) to assist to annotate frames, and additionally propose a cluster algorithm (denoted as SBICAC) to surmount SAM's category-agnostic issue to generate pseudo-labels. Extensive experiments demonstrate that the proposed AdvImmu outperforms existing state-of-the-art methods by 88.56% in mean Intersection over Union (mIoU).
CADENet: Condition-Adaptive Asynchronous Dual-Stream Enhancement Network for Adverse Weather Perception in Autonomous Driving
Computer Science & Engineering Department, German University in Cairo (GUC), Egypt（德国开罗大学（GUC）计算机科学与工程系，埃及）；C-DRiVeS Lab: Cognitive Driving Research in Vehicular Systems, Cairo, Egypt（认知驾驶系统实验室（C-DRiVeS Lab），开罗，埃及）
An Iterative Task-Driven Framework for Resilient LiDAR Place Recognition in Adverse Weather
School of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）电子与信息工程学院）；College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学与技术学院）；School of Information Communication Technology, Shenzhen Institute of Information Technology（深圳信息职业技术学院信息通信技术学院）；Harvard AI and Robotics Lab, Harvard University（哈佛大学AI与机器人实验室）；State Key Laboratory of Robotics and System, Harbin Institute of Technology（哈尔滨工业大学机器人系统国家重点实验室）；School of Electronic and Communication Engineering, Shenzhen Polytechnic University（深圳职业技术大学电子与通信工程学院）
A Robust Simulation Framework for Verification and Validation of Autonomous Maritime Navigation in Adverse Weather and Constrained Environments
Department of Mechanical Engineering, Texas A&M University, College Station, USA（机械工程系，德克萨斯A&M大学，学院站，美国）；Department of Ocean Engineering, Texas A&M University, College Station, USA（海洋工程系，德克萨斯A&M大学，学院站，美国）；American Bureau of Shipping, Spring, TX, USA（美国船舶局，斯普林，德克萨斯州，美国）
CoInfra: A Large-Scale Cooperative Infrastructure Perception System and Dataset for Vehicle-Infrastructure Cooperation in Adverse Weather
Department of Mechanical and Mechatronics Engineering, University of Waterloo（滑铁卢大学机械与机电工程系）；Department of Mechanical Engineering, University of New Brunswick（新不伦瑞克大学机械工程系）；Department of Data and Systems Engineering, University of Hong Kong（香港大学数据与系统工程系）；Technology Partnerships and Innovations, Rogers Communications, Canada Inc.（罗杰斯通讯公司技术创新部）；Department of Mechanical Engineering, University of Alberta（阿尔伯塔大学机械工程系）
Enhancing Lidar-based Object Detection in Adverse Weather using Offset Sequences in Time
Institute for Automotive Engineering RWTH Aachen University（亚琛工业大学汽车工程研究所）；Institute of Imaging and Computer Vision RWTH Aachen University（亚琛工业大学图像与计算机视觉研究所）
Towards Generalized Range-View LiDAR Segmentation in Adverse Weather
University of Electronic Science and Technology of China（电子科技大学）；Dalian University of Technology（大连理工大学）；Lancaster University（兰卡斯特大学）；Nanyang Technological University（南洋理工大学）；Tongji University（同济大学）
针对距离视图 LiDAR 分割在恶劣天气下泛化不足的问题，提出轻量模块化 GAS 与 RDC 双分支框架，在不改动核心架构的情况下显著提升鲁棒性且推理开销极小。
DIDLM: A SLAM Dataset for Difficult Scenarios Featuring Infrared, Depth Cameras, LIDAR, 4D Radar, and Others under Adverse Weather, Low Light Conditions, and Rough Roads
School of Information Science and Technology, Northwest University（信息科学与技术学院，西北大学）；Department of Electrical and Computer Engineering, The University of British Columbia（电气与计算机工程系，不列颠哥伦比亚大学）
CFMW: Cross-modality Fusion Mamba for Robust Object Detection under Adverse Weather
School of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）；USC Viterbi School of Engineering, the University of Southern California（南加州大学维特比工程学院）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；School of Robotics, Hunan University（湖南大学机器人学院）
