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）
Mayur S. Patil, Nataraj Sudharsan, Anthony S. Saaiby, JiaChang Xing, Keliang Pan, Veneela Ammula, Jude Tomdio, Jin Wang, Michael Kei, Heonyong Kang, Sivakumar R… 展开作者
Mayur S. Patil, Nataraj Sudharsan, Anthony S. Saaiby, JiaChang Xing, Keliang Pan, Veneela Ammula, Jude Tomdio, Jin Wang, Michael Kei, Heonyong Kang, Sivakumar Rathinam, Prabhakar R. Pagilla
Maritime Autonomous Surface Ships (MASS) have emerged as a promising solution to enhance navigational safety, operational efficiency, and long-term cost effectiveness. However, their reliable deployment requires rigorous verification and validation (V\&V) under various environmental conditions, including extreme and safety-critical scenarios. This paper presents an enhanced virtual simulation framework to support the V\&V of MASS in realistic maritime environments, with particular emphasis on the influence of weather and bathymetry on autonomous navigation performance. The framework incorporates a high-fidelity environmental modeling suite capable of simulating adverse weather conditions such as rain, fog, and wave dynamics. The key factors that affect weather, such as rain and visibility, are parameterized to affect sea-state characteristics, perception, and sensing systems, resulting in position and velocity uncertainty, reduced visibility, and degraded situational awareness. Furthermore, high-resolution bathymetric data from major U.S. ports are integrated to enable depth-aware navigation, grounding prevention capabilities, and evaluation of vessel controllability in shallow or confined waterways. The proposed framework offers extensive configurability, enabling systematic testing in a wide spectrum of maritime conditions, including scenarios that are impractical or unsafe to replicate in real-world trials, thus supporting the V\&V of MASS.
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）
MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application
Institute of Advanced Machines and Design, SNU, Seoul, S. Korea（先进机械与设计研究所，首尔国立大学）；Dept. of Mechanical Engineering, SNU, Seoul, S. Korea（机械工程系，首尔国立大学）；Seadronix, Seoul, S. Korea（Seadronix公司）；Defence Science and Technology Agency, Singapore（新加坡国防科学与技术局）
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）
Automated Driving Systems (ADS) open up a new domain for the automotive industry and offer new possibilities for future transportation with higher efficiency and comfortable experiences. However, autonomous driving under adverse weather conditions has been the problem that keeps autonomous vehicles (AVs) from going to level 4 or higher autonomy for a long time. This paper assesses the influences and challenges that weather brings to ADS sensors in an analytic and statistical way, and surveys the solutions against inclement weather conditions. State-of-the-art techniques on perception enhanceme…
Reliable point cloud data is essential for perception tasks \textit{e.g.} in robotics and autonomous driving applications. Adverse weather causes a specific type of noise to light detection and ranging (LiDAR) sensor data, which degrades the quality of the point clouds significantly. To address this issue, this letter presents a novel point cloud adverse weather denoising deep learning algorithm (4DenoiseNet). Our algorithm takes advantage of the time dimension unlike deep learning adverse weather denoising methods in the literature. It performs about 10\% better in terms of intersection over …
Daimler AG（戴姆勒股份公司）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）
Lidar sensors are frequently used in environment perception for autonomous vehicles and mobile robotics to complement camera, radar, and ultrasonic sensors. Adverse weather conditions are significantly impacting the performance of lidar-based scene understanding by causing undesired measurement points that in turn effect missing detections and false positives. In heavy rain or dense fog, water drops could be misinterpreted as objects in front of the vehicle which brings a mobile robot to a full stop. In this paper, we present the first CNN-based approach to understand and filter out such adver…
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）
A robust and reliable semantic segmentation in adverse weather conditions is very important for autonomous cars, but most state-of-the-art approaches only achieve high accuracy rates in optimal weather conditions. The reason is that they are only optimized for good weather conditions and given noise models. However, most of them fail, if data with unknown disturbances occur, and their performance decrease enormously. One possibility to still obtain reliable results is to observe the environment with different sensor types, such as camera and lidar, and to fuse the sensor data by means of neura…
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。
Autonomous vehicles rely heavily upon their perception subsystems to see the environment in which they operate. Unfortunately, the effect of variable weather conditions presents a significant challenge to object detection algorithms, and thus it is imperative to test the vehicle extensively in all conditions which it may experience. However, development of robust autonomous vehicle subsystems requires repeatable, controlled testing - while real weather is unpredictable and cannot be scheduled. Real-world testing in adverse conditions is an expensive and time-consuming task, often requiring acc…
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）
Autonomous cars are an emergent technology which has the capacity to change human lives. The current sensor systems which are most capable of perception are based on optical sensors. For example, deep neural networks show outstanding results in recognising objects when used to process data from cameras and Light Detection And Ranging (LiDAR) sensors. However these sensors perform poorly under adverse weather conditions such as rain, fog, and snow due to the sensor wavelengths. This thesis explores new sensing developments based on long wave polarised infrared (IR) imagery and imaging radar to …
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）
