{"results":[{"id":"arxiv_2603.04472","title":"Towards Explainable Deep Learning for Ship Trajectory Prediction in Inland Waterways","authors":[{"name":"Tom Legel"},{"name":"Dirk Söffker"},{"name":"Roland Schätzle"},{"name":"Kathrin Donandt"}],"abstract":"Accurate predictions of ship trajectories in crowded environments are essential to ensure safety in inland waterways traffic. Recent advances in deep learning promise increased accuracy even for complex scenarios. While the challenge of ship-to-ship awareness is being addressed with growing success, the explainability of these models is often overlooked, potentially obscuring an inaccurate logic and undermining the confidence in their reliability. This study examines an LSTM-based vessel trajectory prediction model by incorporating trained ship domain parameters that provide insight into the attention-based fusion of the interacting vessels' hidden states. This approach has previously been explored in the field of maritime shipping, yet the variety and complexity of encounters in inland waterways allow for a more profound analysis of the model's interpretability. The prediction performance of the proposed model variants are evaluated using standard displacement error statistics. Additionally, the plausibility of the generated ship domain values is analyzed. With an final displacement error of around 40 meters in a 5-minute prediction horizon, the model performs comparably to similar studies. Though the ship-to-ship attention architecture enhances prediction accuracy, the weights assigned to vessels in encounters using the learnt ship domain values deviate from the expectation. The observed accuracy improvements are thus not entirely driven by a causal relationship between a predicted trajectory and the trajectories of nearby ships. This finding underscores the model's explanatory capabilities through its intrinsically interpretable design. Future work will focus on utilizing the architecture for counterfactual analysis and on the incorporation of more sophisticated attention mechanisms.","source":"arXiv","year":2026,"language":"en","subjects":["cs.LG","cs.AI"],"doi":"10.3850/978-981-94-3281-3_ESREL-SRA-E2025-P1370-cd","url":"https://arxiv.org/abs/2603.04472","pdf_url":"https://arxiv.org/pdf/2603.04472","is_open_access":true,"published_at":"2026-03-04T07:01:59Z","score":70},{"id":"doaj_10.33012/navi.678","title":"Performance Analysis of MADOCA-Enhanced Tightly Coupled PPP/IMU","authors":[{"name":"Cheng-Wei Wang"},{"name":"Shau-Shiun Jan"}],"abstract":"Precise point positioning (PPP), which is characterized by reliable positioning accuracy and flexibility, has been regarded as a highly promising technique. Precise ephemeris is essential for PPP; however, the conventionally used standard product 3 components have an almost biweekly latency. The multi-global navigation satellite system (GNSS) advanced demonstration tool for orbit and clock analysis (MADOCA), a novel next-generation service, aims to provide real-time correction messages for rapid-convergence PPP in regional areas. Additionally, to ensure seamless navigation during signal-interrupted conditions, an inertial measurement unit (IMU) can be tightly integrated with the motion constraint models. This paper presents a comprehensive analysis of standalone MADOCA-PPP and MADOCA-enhanced tightly coupled PPP/IMU. The approaches were evaluated under multiple scenarios. In suburban regions, the horizontal root mean square error (RMSE) was 0.4 m, with a 95th percentile horizontal error of 0.6 m. In GNSS-challenging environments, the horizontal RMSE was 0.92 m, with a 95th percentile horizontal error of 1.6 m.","source":"DOAJ","year":2025,"language":"","subjects":["Canals and inland navigation. Waterways","Naval Science"],"doi":"10.33012/navi.678","url":"https://navi.ion.org/content/72/1/navi.678","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.33012/navi.717","title":"A Horizontal Accuracy Metric for Magnetic Navigation","authors":[{"name":"Prasenjit Sengupta"}],"abstract":"This paper develops a navigation accuracy metric for magnetic navigation when adapted to civil aviation. Metrics that are currently used may not directly apply to magnetic navigation because the assumptions behind these metrics are based on the statistics of more conventional navigation modalities where the uncertainty distribution in the horizontal plane is typically radially symmetric. Magnetic navigation challenges these assumptions. New standard deviation limits based on the probability of exceedance of radial error are derived. Use is made of quadratic forms over random variables, specifically the Hoyt distribution and its associated probability density function and cumulative density function. Although the density functions lack closed-form solutions, new approximations for this distribution are utilized in order to make rapid computation possible, enabling their use for navigation purposes. When used in conjunction with robust numerical methods, the new approach accurately calculates the bounds on error distributions that would meet the requirements associated with Performance Based Navigation.","source":"DOAJ","year":2025,"language":"","subjects":["Canals and inland navigation. Waterways","Naval Science"],"doi":"10.33012/navi.717","url":"https://navi.ion.org/content/72/4/navi.717","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.33012/navi.691","title":"Doppler Positioning Using Multi-Constellation LEO Satellite Broadband Signals as Signals of Opportunity","authors":[{"name":"Amir Allahvirdi-Zadeh"},{"name":"Ahmed El-Mowafy"},{"name":"Kan Wang"}],"abstract":"This paper investigates the potential of signals of opportunity for positioning using broadband low Earth orbit constellations. We developed analytical absolute and differential models based on Doppler-shift observations from multi-constellation satellite bursts across various frequency ranges. Owing to the unavailability of multi-constellation broadband receivers, simulations were conducted with the application of two primary restrictions common for these satellites: a 30° elevation mask angle and a 15-s intermittency for observations. Signal attenuation factors were modeled, indicating that free space loss was the dominant factor whereas cloud and fog losses were minimal. The accuracy of absolute static positioning, considering the aforementioned broadband restrictions, reached 4.32 m. The kinematic receiver showed similar trends, with a degraded accuracy of 4.83 m. Tests in urban areas revealed significant accuracy degradation to approximately 10 m. However, the differential model significantly improved kinematic positioning accuracy, achieving promising sub-meter levels even with a limited number of satellites.","source":"DOAJ","year":2025,"language":"","subjects":["Canals and inland navigation. Waterways","Naval Science"],"doi":"10.33012/navi.691","url":"https://navi.ion.org/content/72/2/navi.691","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.33012/navi.718","title":"SS-RAIM-Based Integrity Architecture for CDGNSSs Against Satellite Measurement Faults","authors":[{"name":"Dongchan Min"},{"name":"Noah Minchan Kim"},{"name":"Gihun Nam"},{"name":"Jiyun Lee"},{"name":"Sam Pullen"}],"abstract":"Carrier-phase differential global navigation satellite systems (CDGNSSs) present an attractive option for autonomous vehicles that require accurate and safe navigation. The key to high precision in a CDGNSS is resolving integer ambiguities. However, the discrete nature of ambiguities complicates the analysis of position errors in relation to satellite measurement faults, which poses challenges in protection level (PL) calculation. This paper presents an integrity architecture based on solution separation receiver autonomous integrity monitoring. The test statistic for this monitor is defined in the position domain, directly capturing position errors due to faults. This approach facilitates easier and less conservative evaluations of PLs. This paper provides detailed derivations of PLs and monitor thresholds starting from a common definition of integrity and continuity risk. Additionally, this work presents a method for ensuring that PLs reliably bound actual position errors using a measurement overbounding technique. Simulation results show that the monitor detects most faults and that the PLs bound the position errors from undetected faults.","source":"DOAJ","year":2025,"language":"","subjects":["Canals and inland navigation. Waterways","Naval Science"],"doi":"10.33012/navi.718","url":"https://navi.ion.org/content/72/4/navi.718","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.33012/navi.675","title":"Classification of Authentic and Spoofed GNSS Signals Using a Calibrated Antenna Array","authors":[{"name":"Michael C. Esswein"},{"name":"Mark L. Psiaki"}],"abstract":"New optimization-based methods have been developed to use measured direction-of-arrival (DoA) information in order to classify received global navigation satellite system signals into authenticated and spoofed sets and to augment that information with pseudorange information when DoA information alone is insufficient to achieve the needed classification. These methods are designed for a system that is being developed to mitigate spoofing and jamming by using signals from a controlled radiation pattern antenna. These new spoofing classification methods operate on DoA outputs from trackers of various signals. This paper presents a multi-hypothesis test that considers all possible hypotheses regarding the authenticated and spoofed sets of tracked signals. A combinatorial analysis is performed in which all possible authenticated-set/spoofed-set classifications are generated for a given set of tracked signals and the correct authenticated set is determined among the different combinations. Results from Monte Carlo simulations show that using a combined DoA and pseudorange method is suitable for determining the correct combinations.","source":"DOAJ","year":2025,"language":"","subjects":["Canals and inland navigation. Waterways","Naval Science"],"doi":"10.33012/navi.675","url":"https://navi.ion.org/content/72/1/navi.675","is_open_access":true,"published_at":"","score":69},{"id":"arxiv_2508.03672","title":"Inland-LOAM: Voxel-Based Structural Semantic LiDAR Odometry and Mapping for Inland Waterway Navigation","authors":[{"name":"Zhongbi Luo"},{"name":"Yunjia Wang"},{"name":"Jan Swevers"},{"name":"Peter Slaets"},{"name":"Herman Bruyninckx"}],"abstract":"Accurate geospatial information is crucial for safe, autonomous Inland Waterway Transport (IWT), as existing charts (IENC) lack real-time detail and conventional LiDAR SLAM fails in waterway environments. These challenges lead to vertical drift and non-semantic maps, hindering autonomous navigation.   This paper introduces Inland-LOAM, a LiDAR SLAM framework for waterways. It uses an improved feature extraction and a water surface planar constraint to mitigate vertical drift. A novel pipeline transforms 3D point clouds into structured 2D semantic maps using voxel-based geometric analysis, enabling real-time computation of navigational parameters like bridge clearances. An automated module extracts shorelines and exports them into a lightweight, IENC-compatible format.   Evaluations on a real-world dataset show Inland-LOAM achieves superior localization accuracy over state-of-the-art methods. The generated semantic maps and shorelines align with real-world conditions, providing reliable data for enhanced situational awareness. The code and dataset will be publicly available","source":"arXiv","year":2025,"language":"en","subjects":["cs.RO"],"url":"https://arxiv.org/abs/2508.03672","pdf_url":"https://arxiv.org/pdf/2508.03672","is_open_access":true,"published_at":"2025-08-05T17:37:43Z","score":69},{"id":"arxiv_2509.06687","title":"Safe Robust Predictive Control-based Motion Planning of Automated Surface Vessels in Inland Waterways","authors":[{"name":"Sajad Ahmadi"},{"name":"Hossein Nejatbakhsh Esfahani"},{"name":"Javad Mohammadpour Velni"}],"abstract":"Deploying self-navigating surface vessels in inland waterways offers a sustainable alternative to reduce road traffic congestion and emissions. However, navigating confined waterways presents unique challenges, including narrow channels, higher traffic density, and hydrodynamic disturbances. Existing methods for autonomous vessel navigation often lack the robustness or precision required for such environments. This paper presents a new motion planning approach for Automated Surface Vessels (ASVs) using Robust Model Predictive Control (RMPC) combined with Control Barrier Functions (CBFs). By incorporating channel borders and obstacles as safety constraints within the control design framework, the proposed method ensures both collision avoidance and robust navigation on complex waterways. Simulation results demonstrate the efficacy of the proposed method in safely guiding ASVs under realistic conditions, highlighting its improved safety and adaptability compared to the state-of-the-art.","source":"arXiv","year":2025,"language":"en","subjects":["cs.RO","eess.SY"],"url":"https://arxiv.org/abs/2509.06687","pdf_url":"https://arxiv.org/pdf/2509.06687","is_open_access":true,"published_at":"2025-09-08T13:43:09Z","score":69},{"id":"arxiv_2504.04835","title":"Inland Waterway Object Detection in Multi-environment: Dataset and Approach","authors":[{"name":"Shanshan Wang"},{"name":"Haixiang Xu"},{"name":"Hui Feng"},{"name":"Xiaoqian Wang"},{"name":"Pei Song"},{"name":"Sijie Liu"},{"name":"Jianhua He"}],"abstract":"The success of deep learning in intelligent ship visual perception relies heavily on rich image data. However, dedicated datasets for inland waterway vessels remain scarce, limiting the adaptability of visual perception systems in complex environments. Inland waterways, characterized by narrow channels, variable weather, and urban interference, pose significant challenges to object detection systems based on existing datasets. To address these issues, this paper introduces the Multi-environment Inland Waterway Vessel Dataset (MEIWVD), comprising 32,478 high-quality images from diverse scenarios, including sunny, rainy, foggy, and artificial lighting conditions. MEIWVD covers common vessel types in the Yangtze River Basin, emphasizing diversity, sample independence, environmental complexity, and multi-scale characteristics, making it a robust benchmark for vessel detection. Leveraging MEIWVD, this paper proposes a scene-guided image enhancement module to improve water surface images based on environmental conditions adaptively. Additionally, a parameter-limited dilated convolution enhances the representation of vessel features, while a multi-scale dilated residual fusion method integrates multi-scale features for better detection. Experiments show that MEIWVD provides a more rigorous benchmark for object detection algorithms, and the proposed methods significantly improve detector performance, especially in complex multi-environment scenarios.","source":"arXiv","year":2025,"language":"en","subjects":["cs.CV"],"url":"https://arxiv.org/abs/2504.04835","pdf_url":"https://arxiv.org/pdf/2504.04835","is_open_access":true,"published_at":"2025-04-07T08:45:00Z","score":69},{"id":"arxiv_2505.00599","title":"Visual Trajectory Prediction of Vessels for Inland Navigation","authors":[{"name":"Alexander Puzicha"},{"name":"Konstantin Wüstefeld"},{"name":"Kathrin Wilms"},{"name":"Frank Weichert"}],"abstract":"The future of inland navigation increasingly relies on autonomous systems and remote operations, emphasizing the need for accurate vessel trajectory prediction. This study addresses the challenges of video-based vessel tracking and prediction by integrating advanced object detection methods, Kalman filters, and spline-based interpolation. However, existing detection systems often misclassify objects in inland waterways due to complex surroundings. A comparative evaluation of tracking algorithms, including BoT-SORT, Deep OC-SORT, and ByeTrack, highlights the robustness of the Kalman filter in providing smoothed trajectories. Experimental results from diverse scenarios demonstrate improved accuracy in predicting vessel movements, which is essential for collision avoidance and situational awareness. The findings underline the necessity of customized datasets and models for inland navigation. Future work will expand the datasets and incorporate vessel classification to refine predictions, supporting both autonomous systems and human operators in complex environments.","source":"arXiv","year":2025,"language":"en","subjects":["cs.CV"],"url":"https://arxiv.org/abs/2505.00599","pdf_url":"https://arxiv.org/pdf/2505.00599","is_open_access":true,"published_at":"2025-05-01T15:31:15Z","score":69},{"id":"arxiv_2506.17743","title":"Optimizing Periodic Operations for Efficient Inland Waterway Lock Management","authors":[{"name":"Julian Golak"},{"name":"Alexander Grigoriev"},{"name":"Freija van Lent"},{"name":"Tom van der Zanden"}],"abstract":"In inland waterways, the efficient management of water lock operations impacts the level of congestion and the resulting uncertainty in inland waterway transportation. To achieve reliable and efficient traffic, schedules should be easy to understand and implement, reducing the likelihood of errors. The simplest schedules follow periodic patterns, reducing complexity and facilitating predictable management. Since vessels do not arrive in perfectly regular intervals, periodic schedules may lead to more wait time. The aim of this research is to estimate this cost by evaluating how effective these periodic schedules manage vessel traffic at water locks. The first objective is to estimate a periodic arrival pattern that closely matches a dataset of irregular vessel arrivals at a specific lock. We develop an algorithm that, given a fixed number of vessel streams, solves the problem in polynomial time. The solution then serves as input for the subsequent part, where we consider algorithms that compute operational schedules by formulating an optimisation problem with periodic arrival patterns as input, and the goal is to determine a periodic schedule that minimises the long-run average waiting time of vessels. We present a polynomial-time algorithm for the two-stream case and a pseudo-polynomial-time algorithm for the general case, along with incremental polynomial-time approximation schemes. In our numerical experiments, use AIS data to construct a periodic arrival pattern closely matching the observed data. Our experiments demonstrate that when evaluated against actual data, intuitive and straightforward policies often outperform optimal policies specifically trained on the periodic arrival pattern.","source":"arXiv","year":2025,"language":"en","subjects":["cs.DS"],"url":"https://arxiv.org/abs/2506.17743","pdf_url":"https://arxiv.org/pdf/2506.17743","is_open_access":true,"published_at":"2025-06-21T15:46:20Z","score":69},{"id":"arxiv_2506.18737","title":"USVTrack: USV-Based 4D Radar-Camera Tracking Dataset for Autonomous Driving in Inland Waterways","authors":[{"name":"Shanliang Yao"},{"name":"Runwei Guan"},{"name":"Yi Ni"},{"name":"Sen Xu"},{"name":"Yong Yue"},{"name":"Xiaohui Zhu"},{"name":"Ryan Wen Liu"}],"abstract":"Object tracking in inland waterways plays a crucial role in safe and cost-effective applications, including waterborne transportation, sightseeing tours, environmental monitoring and surface rescue. Our Unmanned Surface Vehicle (USV), equipped with a 4D radar, a monocular camera, a GPS, and an IMU, delivers robust tracking capabilities in complex waterborne environments. By leveraging these sensors, our USV collected comprehensive object tracking data, which we present as USVTrack, the first 4D radar-camera tracking dataset tailored for autonomous driving in new generation waterborne transportation systems. Our USVTrack dataset presents rich scenarios, featuring diverse various waterways, varying times of day, and multiple weather and lighting conditions. Moreover, we present a simple but effective radar-camera matching method, termed RCM, which can be plugged into popular two-stage association trackers. Experimental results utilizing RCM demonstrate the effectiveness of the radar-camera matching in improving object tracking accuracy and reliability for autonomous driving in waterborne environments. The USVTrack dataset is public on https://usvtrack.github.io.","source":"arXiv","year":2025,"language":"en","subjects":["cs.CV","cs.RO"],"url":"https://arxiv.org/abs/2506.18737","pdf_url":"https://arxiv.org/pdf/2506.18737","is_open_access":true,"published_at":"2025-06-23T15:13:57Z","score":69},{"id":"arxiv_2510.11449","title":"Enhancing Maritime Domain Awareness on Inland Waterways: A YOLO-Based Fusion of Satellite and AIS for Vessel Characterization","authors":[{"name":"Geoffery Agorku"},{"name":"Sarah Hernandez"},{"name":"Hayley Hames"},{"name":"Cade Wagner"}],"abstract":"Maritime Domain Awareness (MDA) for inland waterways remains challenged by cooperative system vulnerabilities. This paper presents a novel framework that fuses high-resolution satellite imagery with vessel trajectory data from the Automatic Identification System (AIS). This work addresses the limitations of AIS-based monitoring by leveraging non-cooperative satellite imagery and implementing a fusion approach that links visual detections with AIS data to identify dark vessels, validate cooperative traffic, and support advanced MDA. The You Only Look Once (YOLO) v11 object detection model is used to detect and characterize vessels and barges by vessel type, barge cover, operational status, barge count, and direction of travel. An annotated data set of 4,550 instances was developed from $5{,}973~\\mathrm{mi}^2$ of Lower Mississippi River imagery. Evaluation on a held-out test set demonstrated vessel classification (tugboat, crane barge, bulk carrier, cargo ship, and hopper barge) with an F1 score of 95.8\\%; barge cover (covered or uncovered) detection yielded an F1 score of 91.6\\%; operational status (staged or in motion) classification reached an F1 score of 99.4\\%. Directionality (upstream, downstream) yielded 93.8\\% accuracy. The barge count estimation resulted in a mean absolute error (MAE) of 2.4 barges. Spatial transferability analysis across geographically disjoint river segments showed accuracy was maintained as high as 98\\%. These results underscore the viability of integrating non-cooperative satellite sensing with AIS fusion. This approach enables near-real-time fleet inventories, supports anomaly detection, and generates high-quality data for inland waterway surveillance. Future work will expand annotated datasets, incorporate temporal tracking, and explore multi-modal deep learning to further enhance operational scalability.","source":"arXiv","year":2025,"language":"en","subjects":["cs.CV"],"url":"https://arxiv.org/abs/2510.11449","pdf_url":"https://arxiv.org/pdf/2510.11449","is_open_access":true,"published_at":"2025-10-13T14:19:58Z","score":69},{"id":"ss_65237343faa2aee6c39ecffd931b1954771b9e8e","title":"Structural health monitoring of inland navigation structures and ports: a review on developments and challenges","authors":[{"name":"P. Negi"},{"name":"R. Kromanis"},{"name":"A. Dorée"},{"name":"K. Wijnberg"}],"abstract":"Inland navigation structures (INS) facilitate transportation of goods in rivers and canals. Transportation of goods over waterways is more energy efficient than on roads and railways. INS, similar to other civil structures, are aging and require frequent condition assessment and maintenance. Countries, in which INS are important to their economies, such as the Netherlands and the United States, allocate significant budgets for maintenance and renovation of exiting INS, as well as for building new structures. Timely maintenance and early detection of a change to material or geometric properties (i.e., damage) can be supported with the structural health monitoring (SHM), in which monitored data, such as load, structural response, environmental actions, are analyzed. Huge scientific efforts are realized in bridge SHM, but when it comes to SHM of INS, the efforts are significantly lower. Therefore, the SHM community has opportunities to develop new solutions for SHM of INS and convince asset owners of their benefits. This review article, first, articulates the need to keep INS safe to use and fit for purpose, and the challenges associated with it. Second, it defines and reviews sensors, sensing technologies, and approaches for SHM of INS. Then, INS and their components, including structures in ports, are identified, described, and illustrated, and their monitoring efforts are reviewed. Finally, the review article emphasizes the added value of SHM systems for INS, concludes on the current achievements, and proposes future trajectories for SHM of INS and ports.","source":"Semantic Scholar","year":2023,"language":"en","subjects":null,"doi":"10.1177/14759217231170742","url":"https://www.semanticscholar.org/paper/65237343faa2aee6c39ecffd931b1954771b9e8e","pdf_url":"https://journals.sagepub.com/doi/pdf/10.1177/14759217231170742","is_open_access":true,"citations":35,"published_at":"","score":68.05},{"id":"doaj_10.33012/navi.665","title":"Galileo High Accuracy Service: Tests in Different Operational Conditions","authors":[{"name":"Luca Cucchi"},{"name":"Sophie Damy"},{"name":"Ciro Gioia"},{"name":"Beatrice Motella"},{"name":"Matteo Paonni"}],"abstract":"With corrections transmitted through the E6 signal, the Galileo High Accuracy Service (HAS) provides the information necessary to execute a stand-alone precise point positioning algorithm in real time. Once fully operational, the service aims to deliver an accuracy of 20 cm and 40 cm (at the 95% confidence level) in the horizontal and vertical channels, respectively.\n\nWhile most of the current literature focuses on analyzing the performance of HAS in static and open-sky signal reception scenarios, this study presents the results of tests conducted in both static and dynamic conditions, including open-sky and urban canyon scenarios. The tests clearly demonstrate that utilizing HAS corrections leads to a significant reduction in positioning error across all tested environments. Furthermore, a specific analysis of HAS message availability in a harsh environment indicates that the corrections obtained from the signal in space are available approximately 95% of the time during dynamic scenario tests.","source":"DOAJ","year":2024,"language":"","subjects":["Canals and inland navigation. Waterways","Naval Science"],"doi":"10.33012/navi.665","url":"https://navi.ion.org/content/71/4/navi.665","is_open_access":true,"published_at":"","score":68},{"id":"doaj_10.33012/navi.676","title":"Improving the Prediction of GNSS Satellite Visibility in Urban Canyons Based on a Graph Transformer","authors":[{"name":"Shaolong Zheng"},{"name":"Kungan Zeng"},{"name":"Zhenni Li"},{"name":"Qianming Wang"},{"name":"Kan Xie"},{"name":"Ming Liu"},{"name":"Shengli Xie"}],"abstract":"Signals from global navigation satellite systems (GNSSs) in urban areas suffer from serious multipath errors caused by building blockages and reflections. The use of deep neural networks offers great potential for predicting and eliminating complex multipath/non-line-of-sight (NLOS) errors. However, existing methods for predicting the original signals face two remaining challenges. The first challenge is an inability to effectively exploit irregular GNSS measurement data caused by an inconsistent number of visible satellites in different epochs. The second challenge is degradation in the generalization performance of the multipath/NLOS prediction model when using data collected from different locations and periods. To address these challenges, this paper proposes a novel graph transformer neural network (GTNN) for predicting satellite visibility that effectively learns environment representations from irregular GNSS measurements to both alleviate multipath interference and improve the generalization performance of the multipath prediction model. To learn from irregular GNSS measurements, a sky satellite graph is constructed as input to a graph neural network by using satellites captured in the same epoch, which can represent the spatial relationships between satellites and enable the model to learn satellite-related features sufficiently well. To improve the generalization ability of our multipath prediction model, a multihead attention mechanism is introduced to aggregate satellite node information by computing the correlation between satellites to extract the environment representation around the receiver. Based on the constructed sky satellite graph and the multihead attention mechanism, our novel GTNN for predicting satellite visibility can not only handle irregular GNSS measurements but can also learn an environment representation via graph attention. Comparative experiments were conducted on real-world GNSS measurement data in urban areas, demonstrating that the proposed method can achieve an accuracy exceeding 96% for satellite visibility prediction and obtain better generalization performance than existing multipath prediction methods. Moreover, the attention weights among satellites were visualized to demonstrate the environment representation learned by the GTNN from the sky satellite graph.","source":"DOAJ","year":2024,"language":"","subjects":["Canals and inland navigation. Waterways","Naval Science"],"doi":"10.33012/navi.676","url":"https://navi.ion.org/content/71/4/navi.676","is_open_access":true,"published_at":"","score":68},{"id":"doaj_10.33012/navi.647","title":"ICET Online Accuracy Characterization for Geometry-Based Laser Scan Matching","authors":[{"name":"Matthew McDermott"},{"name":"Jason Rife"}],"abstract":"Distribution-to-distribution point cloud registration algorithms are fast and interpretable and perform well in unstructured environments. Unfortunately, existing strategies for predicting the solution error for these methods are overly optimistic, particularly in regions containing large or extended physical objects. In this paper, we introduce the iterative closest ellipsoidal transform (ICET), a novel three-dimensional (3D) lidar scan-matching algorithm that re-envisions the normal distributions transform (NDT) in order to provide robust accuracy prediction from first principles. Like NDT, ICET subdivides a lidar scan into voxels in order to analyze complex scenes by considering many smaller local point distributions; however, ICET assesses the voxel distribution to distinguish random noise from deterministic structure. ICET then uses a weighted least-squares formulation to incorporate this noise/structure distinction while computing a localization solution and predicting the solution-error covariance. To demonstrate the reasonableness of our accuracy predictions, we verify 3D ICET in three lidar tests involving real-world automotive data, high-fidelity simulated trajectories, and simulated corner-case scenes. For each test, ICET consistently performs scan matching with sub-centimeter accuracy. With this level of accuracy, combined with the fact that the algorithm is fully interpretable, this algorithm is well suited for safety-critical transportation applications. Code is available at https://github.com/mcdermatt/ICET.","source":"DOAJ","year":2024,"language":"","subjects":["Canals and inland navigation. Waterways","Naval Science"],"doi":"10.33012/navi.647","url":"https://navi.ion.org/content/71/2/navi.647","is_open_access":true,"published_at":"","score":68},{"id":"arxiv_2410.07130","title":"Analysis of vessel traffic flow characteristics in inland restricted waterways using multi-source data","authors":[{"name":"Wenzhang Yang"},{"name":"Peng Liao"},{"name":"Shangkun Jiang"},{"name":"Hao Wang"}],"abstract":"To effectively manage vessel traffic and alleviate congestion on busy inland waterways, a comprehensive understanding of vessel traffic flow characteristics is crucial. However, limited data availability has resulted in minimal research on the traffic flow characteristics of inland waterway vessels. This study addresses this gap by conducting vessel-following experiments and fixed-point video monitoring in inland waterways, collecting multi-source data to analyze vessel traffic flow characteristics. First, the analysis of vessel speed distribution identifies the economic speed for vessels operating in these environments. Next, the relationship between microscopic vessel speed and gap distance is examined, with the logarithmic model emerging as the most accurate among various tested models. Additionally, the study explores the relationships among macroscopic speed, density, and flow rate, proposing a novel piecewise fundamental diagram model to describe these relationships. Lastly, the inland vessel traffic states are categorized using K-means clustering algorithm and applied to vessel navigation services. These findings provide valuable insights for enhancing inland waterway transportation and advancing the development of an integrated waterway transportation system.","source":"arXiv","year":2024,"language":"en","subjects":["cs.CE","stat.AP"],"url":"https://arxiv.org/abs/2410.07130","pdf_url":"https://arxiv.org/pdf/2410.07130","is_open_access":true,"published_at":"2024-09-21T15:23:28Z","score":68},{"id":"arxiv_2403.00554","title":"Distributed MPC for autonomous ships on inland waterways with collaborative collision avoidance","authors":[{"name":"Hoang Anh Tran"},{"name":"Tor Arne Johansen"},{"name":"Rudy R. Negenborn"}],"abstract":"This paper presents a distributed solution for the problem of collaborative collision avoidance for autonomous inland waterway ships. A two-layer collision avoidance framework that considers inland waterway traffic regulations is proposed to increase navigational safety for autonomous ships. Our approach allows for modifying traffic rules without changing the collision avoidance algorithm, and is based on a novel formulation of model predictive control (MPC) for collision avoidance of ships. This MPC formulation is designed for inland waterway traffic and can handle complex scenarios. The alternating direction method of multipliers is used as a scheme for exchanging and negotiating intentions among ships. Simulation results show that the proposed algorithm can comply with traffic rules. Furthermore, the proposed algorithm can safely deviate from traffic rules when necessary to increase efficiency in complex scenarios.","source":"arXiv","year":2024,"language":"en","subjects":["eess.SY"],"doi":"10.1016/j.oceaneng.2026.124802","url":"https://arxiv.org/abs/2403.00554","pdf_url":"https://arxiv.org/pdf/2403.00554","is_open_access":true,"published_at":"2024-03-01T14:24:20Z","score":68},{"id":"arxiv_2410.07915","title":"A Lightweight Target-Driven Network of Stereo Matching for Inland Waterways","authors":[{"name":"Jing Su"},{"name":"Yiqing Zhou"},{"name":"Yu Zhang"},{"name":"Chao Wang"},{"name":"Yi Wei"}],"abstract":"Stereo matching for inland waterways is one of the key technologies for the autonomous navigation of Unmanned Surface Vehicles (USVs), which involves dividing the stereo images into reference images and target images for pixel-level matching. However, due to the challenges of the inland waterway environment, such as blurred textures, large spatial scales, and computational resource constraints of the USVs platform, the participation of geometric features from the target image is required for efficient target-driven matching. Based on this target-driven concept, we propose a lightweight target-driven stereo matching neural network, named LTNet. Specifically, a lightweight and efficient 4D cost volume, named the Geometry Target Volume (GTV), is designed to fully utilize the geometric information of target features by employing the shifted target features as the filtered feature volume. Subsequently, to address the substantial texture interference and object occlusions present in the waterway environment, a Left-Right Consistency Refinement (LRR) module is proposed. The \\text{LRR} utilizes the pixel-level differences in left and right disparities to introduce soft constraints, thereby enhancing the accuracy of predictions during the intermediate stages of the network. Moreover, knowledge distillation is utilized to enhance the generalization capability of lightweight models on the USVInland dataset. Furthermore, a new large-scale benchmark, named Spring, is utilized to validate the applicability of LTNet across various scenarios. In experiments on the aforementioned two datasets, LTNet achieves competitive results, with only 3.7M parameters. The code is available at https://github.com/Open-YiQingZhou/LTNet .","source":"arXiv","year":2024,"language":"en","subjects":["cs.CV"],"url":"https://arxiv.org/abs/2410.07915","pdf_url":"https://arxiv.org/pdf/2410.07915","is_open_access":true,"published_at":"2024-10-10T13:40:30Z","score":68}],"total":188269,"page":1,"page_size":20,"sources":["DOAJ","arXiv","Semantic Scholar","CrossRef"],"query":"Canals and inland navigation. Waterways"}