Search Results (28)

Autonomous driving systems (ADSs) are reliable only when heterogeneous sensors, estimation algorithms, and safety mechanisms are engineered as a single coherent safety-critical measurement system rather than as loosely coupled modules. Production stacks integrate cameras, LiDAR, automotive radar, and GNSS/IMU, yet deployment remains constrained by modality-specific failure modes, calibration and synchronization drift, and out-of-distribution (OOD) conditions that violate modeling assumptions. These limitations induce overconfidence and downstream decision errors whenever planning assumes certainty sharper than sensing can justify. This survey introduces a sensor-centric framework linking measurement physics, uncertainty propagation, fusion integrity, safety assurance, and risk-aware planning and control. We formalize what each modality physically measures; unify probabilistic, evidential, and conformal uncertainty representations; analyze filtering, factor-graph, BEV, transformer, and state-space fusion architectures with an emphasis on robustness and graceful degradation; and generalize aviation-style integrity concepts (RAIM/ARAIM) to multi-modal autonomy. The distinctive contribution is a single sensor-to-assurance throughline in which every uncertainty representation is tied to its measurement physics, every fusion architecture is evaluated against an explicit integrity-monitoring requirement generalized from RAIM/ARAIM, and every safety-standard clause is mapped to a concrete architectural mechanism. We map these mechanisms onto ISO 26262, ISO 21448 (SOTIF), ISO/PAS 8800, ANSI/UL 4600, and the UNECE framework, and connect perception uncertainty to decision-making through chance-constrained MPC and formal safety filters (RSS, CBF). Industry case studies and emerging V2X and generative-simulation approaches close the loop to deployable safety arguments. Full article

Reliable tabular data correction is a prerequisite for trustworthy analytics in enterprise information systems. Tabular data in such environments frequently contain formatting errors, semantic conflicts, missing values, and cross-field inconsistencies that degrade downstream analytics and machine learning performance. Rule-based methods efficiently handle structural violations but miss context-dependent errors, whereas large language models (LLMs) offer strong semantic-correction capability at inference costs prohibitive for enterprise-scale deployment. This paper formulates data error correction as a progressive decision process and proposes a complexity-aware framework with three processing stages. The first stage applies deterministic rules for low-complexity structural errors. The second stage employs a task-specialized distilled language model for medium-complexity semantic correction. The third stage performs neural probabilistic–logical reasoning on a factor graph for high-complexity cross-field errors. A learnable routing mechanism assigns each record to the appropriate stage based on a lightweight complexity score. Layer-wise conformal prediction is further introduced to construct calibrated prediction sets with coverage guarantees at each stage, together with a rejection mechanism for low-confidence corrections. The framework is evaluated on one enterprise dataset and two public benchmarks (Hospital and Flights). It improves the record-level complete repair rate by 2.1 to 3.1 percentage points over the strongest baseline (GPT-4o-Direct) and by up to 16.8 points over purely rule-based repair, while reducing average inference latency by approximately 80% relative to direct GPT-4o invocation. Ablation studies confirm the critical role of complexity-aware routing and rule-trigger features, and reliability analyses show that hierarchical conformal calibration maintains tighter coverage than single-level alternatives across varying confidence requirements. These results indicate that complexity-aware progressive routing coupled with hierarchical conformal calibration provides a practical path toward high-throughput, auditable, and reliability-controlled data cleaning suitable for enterprise deployment. Full article

As cleaner production gains prominence in wooden product manufacturing, green evaluation of process schemes during early design is crucial. However, dust concentration, a key environmental indicator in wood product manufacturing, is often evaluated in a subjective and fragmented manner, which greatly hinders the selection of green process schemes in early design. To address this gap, an adaptability evaluation model for green process schemes was proposed based on process knowledge graphs (PKG) and fuzzy Bayesian network (FBN), with the objective of minimizing dust concentration. First, a PKG for wooden products was constructed based on the requirement-function-structure-characteristic-process-equipment (RFSCPE) ontology using patents and process manuals. Second, candidate process schemes were generated via the PKG, and dust-related causal relationships encoded in the PKG were mapped onto a Bayesian network structure. Third, conditional probabilities were obtained by combining probabilistic hesitant fuzzy sets and experimental dust data. The FBN was then updated to perform probabilistic reasoning on dust concentration. Finally, a case study on a wooden toy car validated the proposed approach, and sensitivity analysis identified the key dust-influencing factors, thereby providing quantitative support for greener process decisions. Full article

To address unstable localization and sparse mapping for autonomous vehicles operating in GPS-denied and low-visibility environments, this paper proposes GTS-SLAM, a tightly coupled dense visual SLAM framework integrating Generalized Iterative Closest Point (GICP) and 3D Gaussian Splatting (3DGS). The system is designed for intelligent driving platforms such as underground mining vehicles, inspection robots, and tunnel autonomous navigation systems. The front-end performs covariance-aware point-cloud registration using GICP to achieve robust pose estimation under low texture, dust interference, and dynamic disturbances. The back-end employs probabilistic dense mapping based on 3DGS, combined with scale regularization, scale alignment, and keyframe factor-graph optimization, enabling synchronized optimization of localization and mapping. A Compact-3DGS compression strategy further reduces memory usage while maintaining real-time performance. Experiments on public datasets and real underground-like scenarios demonstrate centimeter-level trajectory accuracy, high-quality dense reconstruction, and real-time rendering. The system provides reliable perception capability for vehicle autonomous navigation, obstacle avoidance, and path planning in confined and weak-light environments. Overall, the proposed framework offers a deployable solution for autonomous driving and mobile robots requiring accurate localization and dense environmental understanding in challenging conditions. Full article

High resolution underwater mapping is fundamental to the sustainable development of the blue economy, supporting offshore energy expansion, marine habitat protection, and the monitoring of both living and non-living resources. This work presents a pose-graph SLAM and calibration framework specifically designed for 3D profiling sonars, such as the Coda Octopus Echoscope 3D. The system integrates a probabilistic scan matching method (3DupIC) for direct registration of 3D sonar scans, enabling accurate trajectory and map estimation even under degraded dead reckoning conditions. Unlike other bathymetric SLAM methods that rely on submaps and assume short-term localization accuracy, the proposed approach performs direct scan-to-scan registration, removing this dependency. The factor graph is extended to represent the sonar extrinsic parameters, allowing the sonar-to-body transformation to be refined jointly with trajectory optimization. Experimental validation on a challenging real world dataset demonstrates outstanding localization and mapping performance. The use of refined extrinsic parameters further improves both accuracy and map consistency, confirming the effectiveness of the proposed joint SLAM and calibration approach for robust and consistent underwater mapping. Full article

Inspired by the remarkable collaborative echolocation mechanisms of dolphin pods, the paper addresses the challenge of achieving high-precision cooperative positioning for clusters of unmanned underwater vehicles (UUVs) in complex marine environments. Cooperative positioning systems for UUVs typically rely on acoustic ranging information to correct positional errors. However, the propagation characteristics of underwater acoustic signals are susceptible to environmental disturbances, often resulting in non-Gaussian, heavy-tailed distributions of ranging noise. Additionally, the strong nonlinearity of the system and the limited observability of measurement information further constrain positioning accuracy. To tackle these issues, this paper innovatively proposes a Factor Graph-based Adaptive Cooperative Positioning Algorithm (FGAWSP) suitable for heavy-tailed noise environments. The method begins by constructing a factor graph model for UUV cooperative positioning to intuitively represent the probabilistic dependencies between system states and observed variables. Subsequently, a novel factor graph estimation mechanism integrating adaptive weights with the product algorithm is designed. By conducting online assessment of residual information, this mechanism dynamically adjusts the fusion weights of different measurements, thereby achieving robust handling of anomalous range values. Experimental results demonstrate that the proposed method reduces positioning errors by 22.31% compared to the traditional algorithm, validating the effectiveness of our approach. Full article

Three-dimensional multi-object tracking (3D MOT) plays a vital role in enabling reliable perception for LiDAR-based autonomous systems. However, LiDAR measurements often exhibit sparsity, occlusion, and sensor noise that lead to uncertainty and instability in downstream tracking. To address these challenges, we propose FGO-PMB, a unified probabilistic framework that integrates the Poisson Multi-Bernoulli (PMB) filter from Random Finite Set (RFS) theory with Factor Graph Optimization (FGO) for robust LiDAR-based object tracking. In the proposed framework, object states, existence probabilities, and association weights are jointly formulated as optimizable variables within a factor graph. Four factors, including state transition, observation, existence, and association consistency, are formulated to uniformly encode the spatio-temporal constraints among these variables. By unifying the uncertainty modeling capability of RFS with the global optimization strength of FGO, the proposed framework achieves temporally consistent and uncertainty-aware estimation across continuous LiDAR scans. Experiments on KITTI and nuScenes indicate that the proposed method achieves competitive 3D MOT accuracy while maintaining real-time performance. Full article

The widespread application of robots in complex and dynamic environments demands that Visual SLAM is both robust and accurate. However, dynamic objects, varying illumination, and environmental complexity fundamentally challenge the static world assumptions underlying traditional SLAM methods. This review provides a comprehensive investigation into the mathematical foundations of V-SLAM and systematically analyzes the key optimization techniques developed for dynamic environments, with particular emphasis on advances since 2020. We begin by rigorously deriving the probabilistic formulation of V-SLAM and its basis in nonlinear optimization, unifying it under a Maximum a Posteriori (MAP) estimation framework. We then propose a taxonomy based on how dynamic elements are handled mathematically, which reflects the historical evolution from robust estimation to semantic modeling and then to deep learning. This framework provides detailed analysis of three main categories: (1) robust estimation theory-based methods for outlier rejection, elaborating on the mathematical models of M-estimators and switch variables; (2) semantic information and factor graph-based methods for explicit dynamic object modeling, deriving the joint optimization formulation for multi-object tracking and SLAM; and (3) deep learning-based end-to-end optimization methods, discussing their mathematical foundations and interpretability challenges. This paper delves into the mathematical principles, performance boundaries, and theoretical controversies underlying these approaches, concluding with a summary of future research directions informed by the latest developments in the field. The review aims to provide both a solid mathematical foundation for understanding current dynamic V-SLAM techniques and inspiration for future algorithmic innovations. By adopting a math-first perspective and organizing the field through its core optimization paradigms, this work offers a clarifying framework for both understanding and advancing dynamic V-SLAM. Full article

Cognitive diagnosis is an important component of adaptive learning, as it infers learners’ latent knowledge states and enables tailored feedback. However, existing approaches often emphasize sequential modeling or latent factorization, while insufficiently incorporating curriculum structures that embody prerequisite relations. This gap constrains both predictive accuracy and pedagogical interpretability. To address this limitation, we propose a Curriculum-Aware Graph Neural Cognitive Diagnosis (CA-GNCD) framework that integrates curriculum priors into graph-based neural modeling. The framework combines graph representation learning, knowledge-prior fusion, and interpretability constraints to jointly capture relational dependencies among concepts and individual learner trajectories. Experiments on three widely used benchmark datasets, ASSISTments2017, EdNet-KT1, and Eedi, show that CA-GNCD achieves consistent improvements over classical probabilistic, psychometric, and recent neural baselines. On average, it improves AUC by more than 4.5 percentage points and exhibits relatively faster convergence, greater robustness to noisy conditions, and stronger cross-domain generalization. These results suggest that aligning diagnostic predictions with curriculum structures can enhance interpretability and reliability, offering implications for personalized learning support. While promising, further validation in diverse educational contexts is required to establish the generalizability and practical deployment of the proposed framework. Full article

Forecasting antimicrobial resistance (AMR) is critical for public health, yet most models neglect the interconnected nature of agricultural systems. Focusing on ciprofloxacin resistance in Campylobacter jejuni—a leading foodborne pathogen in poultry—this study aims to develop a probabilistic framework for identifying high-risk environmental conditions. We employed a graph-based machine learning and Bayesian approach, integrating and discretizing data from international databases. An exploratory classification with XGBoost and SVC was followed by core analysis using a Generalized Naive Bayes (GNB) model for feature selection and a Bayesian Network (BN) to uncover conditional dependencies. The GNB model identified pesticides, land use, and precipitation as key features. The BN revealed a complex web of interactions, showing that resistance probability is highly context-dependent. Precipitation was a critical effect modifier; for example, expanded land use correlated with an 18.3% increase in resistance probability during dry conditions but a 73.7% decrease during wet periods. Scenarios with low and high precipitation were associated with high risk, indicating multiple environmental pathways. Our results demonstrate that Bayesian networks can effectively model the complex, non-linear relationships driving AMR. Ciprofloxacin resistance emerges from system-wide interactions rather than isolated factors. This approach provides a valuable framework for generating hypotheses and supports the development of early-warning systems for targeted antimicrobial stewardship in poultry production. Full article

In modern industrial environments, quickly and accurately identifying faults is crucial for ensuring the smooth operation of production processes. Non-negative Matrix Factorization (NMF)-based fault detection technology has garnered attention due to its wide application in industrial process monitoring and machinery fault diagnosis. As an effective dimensionality reduction tool, NMF can decompose complex datasets into non-negative matrices with practical and physical significance, thereby extracting key features of the process. This paper presents a novel approach to fault detection in industrial processes, called Graph-Regularized Orthogonal Non-negative Matrix Factorization with Itakura–Saito Divergence (GONMF-IS). The proposed method addresses the challenges of fault detection in complex, non-Gaussian industrial environments. By using Itakura–Saito divergence, GONMF-IS effectively handles data with probabilistic distribution characteristics, improving the model’s ability to process non-Gaussian data. Additionally, graph regularization leverages the structural relationships among data points to refine the matrix factorization process, enhancing the robustness and adaptability of the algorithm. The incorporation of orthogonality constraints further enhances the independence and interpretability of the resulting factors. Through extensive experiments, the GONMF-IS method demonstrates superior performance in fault detection tasks, providing an effective and reliable tool for industrial applications. The results suggest that GONMF-IS offers significant improvements over traditional methods, offering a more robust and accurate solution for fault diagnosis in complex industrial settings. Full article

The precise identification of priority areas for conservation based on connectivity can significantly enhance protection efficacy and mitigate biodiversity loss in fragmented landscapes. Priority area selection efforts are typically conducted in landscapes with a limited number of patches or simplified to focus on large patches, while landscapes with numerous patches are rarely explored. In this paper, we used a forest in Kanas, Xinjiang, China, as a case study to explore priority patches for conservation according to their contribution to maintaining overall landscape connectivity, as well as to assess how structural factors influence patch importance in connectivity, based on graph theory. We found that the rank of patches varied with patch importance indices (which can be used to calculate the contribution of individual patches to maintaining overall landscape). Dispersal distances were selected, as they placed different emphasis on the size and topological location of patches, and different types of links (binary or probabilistic connection) were used. One critical and seven important connected patches were identified as priority patches for conservation after taking multiple connectivity indices and dispersal distances into comprehensive consideration. In addition, neighboring patch density was the dominant factor that influenced patch importance for species with 50 and 100 m dispersal distances, while patch size contributed most for species with 200 m and longer dispersal distances; therefore, we suggested that neighboring patch density and patch size could be used to support efforts to identify priority patches. Overall, our results provide a unique perspective and a more simplified process for the selection of priority protected sites in patch-rich landscapes, allowing us to highlight which action is suitable for optimizing landscape connectivity and biodiversity conservation. Full article

Dilution of Precision (DOP) is routinely used in GNSS to assess the quality of the constellation geometry for the positioning algorithm. Those DOP indicators are computed from the estimation covariance of a snapshot weighted least squares (WLS) estimate under certain hypotheses. This paper proposes to define DOP indicators for GNSS solutions based on Factor Graph Optimization (FGO). FGO solutions have become popular in the GNSS domain. They allow to easily model probabilistic contraints, called factors, over a large time window, by mixing observations and motion constraints accross consecutive epochs. The solution is solved by performing a batch WLS estimation for the states at all considered epochs, using all available factors. Due to the simple nature of the estimation algorithm—a WLS solution—it is possible to derive the theoretical estimation error covariance, which will indicate the accuracy of the computed solution. In this paper, a formula is proposed to approximate the DOP for the FGO solution. Then, the formula is validated in various scenarios involving fixed or changing satellite visibility. Full article

Ophthalmic diseases such as glaucoma, age-related macular degeneration (ARMD), and optic neuritis involve complex molecular and cellular disruptions that challenge current diagnostic and therapeutic approaches. Advanced artificial intelligence (AI) and machine learning (ML) models offer a novel lens to analyze these diseases by integrating diverse datasets, identifying patterns, and enabling precision medicine strategies. Over the past decade, applications of AI in ophthalmology have expanded from imaging-based diagnostics to molecular-level modeling, bridging critical gaps in understanding disease mechanisms. This paper systematically reviews the application of AI-driven methods, including reinforcement learning (RL), graph neural networks (GNNs), Bayesian inference, and generative adversarial networks (GANs), in the context of these ophthalmic conditions. RL models simulate transcription factor dynamics in hypoxic or inflammatory environments, offering insights into disrupted molecular pathways. GNNs map intricate molecular networks within affected tissues, identifying key inflammatory or degenerative drivers. Bayesian inference provides probabilistic models for predicting disease progression and response to therapies, while GANs generate synthetic datasets to explore therapeutic interventions. By contextualizing these AI tools within the broader framework of ophthalmic disease management, this review highlights their potential to transform diagnostic precision and therapeutic outcomes. Ultimately, this work underscores the need for continued interdisciplinary collaboration to harness AI’s potential in advancing the field of ophthalmology and improving patient care. Full article

This paper presents GraphPPL.jl, a novel probabilistic programming language designed for graphical models. GraphPPL.jl uniquely represents probabilistic models as factor graphs. A notable feature of GraphPPL.jl is its model nesting capability, which facilitates the creation of modular graphical models and significantly simplifies the development of large (hierarchical) graphical models. Furthermore, GraphPPL.jl offers a plugin system to incorporate inference-specific information into the graph, allowing integration with various well-known inference engines. To demonstrate this, GraphPPL.jl includes a flexible plugin to define a Constrained Bethe Free Energy minimization process, also known as variational inference. In particular, the Constrained Bethe Free Energy defined by GraphPPL.jl serves as a potential inference framework for numerous well-known inference backends, making it a versatile tool for diverse applications. This paper details the design and implementation of GraphPPL.jl, highlighting its power, expressiveness, and user-friendliness. It also emphasizes the clear separation between model definition and inference while providing developers with extensibility and customization options. This establishes GraphPPL.jl as a high-level user interface language that allows users to create complex graphical models without being burdened with the complexity of inference while allowing backend developers to easily adopt GraphPPL.jl as their frontend language. Full article