自主智能系统(英文)最新文献

{"title":"RISMO: Riemannian Information-Geometric Swarm Motion Orchestration for Heterogeneous UAV–UGV Cooperative Exploration in Unknown Environments","authors":"Fenghe Guo,&nbsp;Runjie Shen,&nbsp;Junjie Zhang,&nbsp;Yongchun Wang,&nbsp;Chenyang Sun,&nbsp;Zishan Wang,&nbsp;Huiming Li","doi":"10.1007/s43684-026-00143-2","DOIUrl":"10.1007/s43684-026-00143-2","url":null,"abstract":"<div><p>The orchestration of heterogeneous swarm systems, such as Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) teams, in unknown, GPS-denied environments requires coordination among active perception, collision avoidance, and topological flexibility. Traditional Artificial Potential Field (APF) methods can suffer from local-minimum deadlocks, while optimization-based Control Barrier Function Quadratic Programs (CBF-QPs) may produce high-frequency control variation in narrow corridors. To address these limitations, this paper proposes the Riemannian Information-geometric Swarm Motion Orchestration (RISMO) framework. RISMO integrates objective-driven navigation, perception-aware motion generation, and obstacle avoidance within a common non-Euclidean metric representation. We construct a Riemannian metric tensor that incorporates the Fisher Information Matrix (FIM) to encourage the UGVs to preserve line-of-sight tracking of the UAVs. To accommodate formation deformation in narrow passages, the swarm topology is modeled as a Semi-Markov Jump System (S-MJS). Under the enforced positive per-agent inter-event bound and the stated Average Dwell Time (ADT) condition, the modeled switching sequence has a bounded transition frequency and cannot exhibit Zeno accumulation. Furthermore, a goal-aligned tangential flux supplies a deterministic local directional bias near saddle-like stagnation. The numerical and ablation results show that this mechanism facilitates escape from the tested non-convex traps. In the planar simulations, RISMO reduces the observed gradient stagnation of APF and the high-frequency control variation of CBF-QP while preserving positive obstacle clearance and cooperative perception.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-026-00143-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UAV-based dynamic crowd monitoring for rescue prioritization using subspace-driven localization","authors":"Zhitong Zhang,&nbsp;Mingliang Xiong,&nbsp;Wen Fang,&nbsp;Qingwei Jiang,&nbsp;Siyuan Du,&nbsp;Tianyi Lv","doi":"10.1007/s43684-026-00142-3","DOIUrl":"10.1007/s43684-026-00142-3","url":null,"abstract":"<div><p>Rapid and reliable localization of survivors is critical in post-disaster search and rescue operations. However, conventional sensing methods such as visual imaging are often ineffective due to smoke, dust, or debris, and traditional RSSI-based localization suffers from severe multipath interference and noise. This paper proposes a subspace-driven multi-UAV system for dynamic crowd monitoring and rescue prioritization. The system employs Boustrophedon scanning to acquire spatially continuous RSSI sequences from survivors’ Wi-Fi devices. A spatial resampling technique is first applied to regularize the irregular measurements, followed by Hankel matrix construction and Singular Value Decomposition (SVD) to separate signal from noise, yielding robust coarse device locations. A multi-pass scanning strategy tracks devices over time, detects mobility as an indicator of vitality, and maintains device records with expiration mechanisms. Detected devices are mapped to predefined rescue zones, and a weighted priority score is computed for each zone based on device count, mobility, and temporal trends. Simulation results under low and high noise conditions demonstrate that the proposed method significantly outperforms Kalman-filtered trilateration and SVD without resampling. At 6 dB noise, it achieves a 46.7% reduction in RMSE and a 60.0% reduction in outlier ratio. Furthermore, spatial resampling reduces computational time by 17% per device-line, with greater gains expected in high-frequency scenarios. The proposed framework transforms raw RSSI measurements into actionable intelligence for rescue command, enabling efficient resource allocation.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-026-00142-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OCE: an optimization convergence metric for LiDAR SLAM degeneracy detection","authors":"Junrui Zhang,&nbsp;Mengfan Zhu,&nbsp;Chenyang Sun,&nbsp;Fenghe Guo,&nbsp;Quanxi Zhan,&nbsp;Runjie Shen","doi":"10.1007/s43684-026-00144-1","DOIUrl":"10.1007/s43684-026-00144-1","url":null,"abstract":"<div><p>LiDAR-based Simultaneous Localization and Mapping (SLAM) systems suffer from environment-induced degeneracy in geometrically self-similar scenes such as tunnels and cylindrical structures, where the point cloud registration problem becomes ill-conditioned. Existing degeneracy detection methods predominantly rely on eigenvalue analysis of the Hessian matrix, which assesses geometric conditioning at a fixed linearization point. However, in semi-degenerate environments containing partial but sufficient geometric constraints, these methods tend to produce false positive detections because measurement noise inflates eigenvalues and obscures the contribution of minor structural features. In this paper, we propose the Optimization Convergence Efficiency (OCE) metric, which evaluates the relative contraction of the directional optimizer gradient across Levenberg-Marquardt evaluations and directly characterizes the convergence response of LiDAR registration along the monitored state direction. The proposed method is evaluated through controlled simulation with ground-truth-based quantitative assessment and real-world UAV experiments in a wind-turbine tower and a long tunnel. In the nominal simulation, OCE achieves 98.21% recall with a 2.33% false-positive rate, compared with 100% recall and a 49.42% false-positive rate for the conventional eigenvalue-ratio detector. Controlled measurement-noise experiments further demonstrate that OCE maintains substantially lower false-alarm rates as spectral discrimination becomes less reliable. These results show that OCE can retain usable LiDAR pose estimates in semi-degenerate environments while reliably identifying actual registration failure.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-026-00144-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive emergency braking for highway safety: a simulation framework for performance and reliability evaluation","authors":"Ricardo Ribeiro,&nbsp;Catarina Gonçalves,&nbsp;Nuno Pombo","doi":"10.1007/s43684-026-00140-5","DOIUrl":"10.1007/s43684-026-00140-5","url":null,"abstract":"<div><p>Highway safety is a critical concern for automated vehicles, particularly in dynamic and high-risk scenarios requiring precise braking and trajectory adjustments. This paper presents a comprehensive evaluation of an Automated Emergency Braking (AEB) system designed to enhance safety in diverse highway conditions. Using a Matlab Simulink-based test bench, the study explores Car-to-Car Rear Moving, Car-to-Car Rear Stationary, and Car-to-Car Rear Braking scenarios, adhering to ISO 8855 standards and leveraging a 3 Degrees of Freedom (3DOF) vehicle model.</p><p>The analysis highlights the AEB system’s ability to mitigate collisions through adaptive Model Predictive Control and dynamic Path Planning, ensuring safe and efficient navigation. Results demonstrate the AEB system’s effectiveness in handling challenges such as deadlocks, rapid deceleration, and complex lane-change scenarios while balancing safety and performance trade-offs. Despite its strengths, limitations related to real-world unpredictability and computational demands are acknowledged, with recommendations for future work to refine integration with predictive algorithms and enhance scalability.</p><p>This study underscores the importance of advanced AEB systems in improving highway driving safety and provides a robust framework for evaluating their performance in simulated environments. By addressing critical gaps in current research, this work contributes to the development of more reliable and adaptable braking systems for automated highway driving.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-026-00140-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CDJMP: an adaptive conditional diffusion model for multi-agent joint motion prediction in autonomous driving","authors":"Wenwen Zheng,&nbsp;Chao Huang,&nbsp;Hao Zhang,&nbsp;Huilin Yin","doi":"10.1007/s43684-026-00138-z","DOIUrl":"10.1007/s43684-026-00138-z","url":null,"abstract":"<div><p>In autonomous driving trajectory prediction, it is important to generate multi-modal trajectories. As a generative method, diffusion model has been increasingly adopted in the field of trajectory prediction. In this paper, we propose CDJMP (Conditional Diffusion model-based Joint Motion Prediction), an adaptive conditional diffusion framework designed for multi-agent trajectory forecasting in autonomous driving. However, when conventional diffusion models generate multi-modal trajectories, particularly for multi-agent joint prediction, they often fail to balance the diversity of generated trajectories and the accuracy of prediction. At the same time, diffusion models also suffer from time-consuming inference. To address these issues, CDJMP utilizes a two-stage framework to generate diverse and highly accurate trajectories. In the first stage, a probabilistic initializer predicts proposal trajectories together with adaptive denoising steps. In the second stage, a group-aware conditional encoder captures dynamic multi-agent interactions and guides the diffusion process to produce coherent multimodal outcomes. Experiments on the INTERACTION and Argoverse datasets demonstrate that CDJMP achieves state-of-the-art performance, reducing minADE and minFDE by up to 9% and 10%, respectively, on the INTERACTION dataset. Ablation experiments demonstrate that CDJMP can effectively reduce inference time while maintaining prediction accuracy. These results highlight the potential of CDJMP as an efficient and accurate framework for real-time multi-agent trajectory prediction in autonomous driving.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-026-00138-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148926493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UAV-SGDFNet: structured guided dual-level feature fusion for low-altitude anti-UAV detection","authors":"Yang Yu,&nbsp;Kuixian Cai,&nbsp;Rui Fan,&nbsp;Brian C. W. Kei,&nbsp;Bohuan Xue","doi":"10.1007/s43684-026-00141-4","DOIUrl":"10.1007/s43684-026-00141-4","url":null,"abstract":"<div><p>In complex low-altitude environments, illegally operated UAVs are typically small and can easily be confused with background clutter, leading to reduced detection stability. To address this challenge, we propose UAV-SGDFNet, an efficient small UAV detector. The model enhances robustness by jointly optimizing feature representation and multi-scale fusion. In the feature extraction phase, we introduce a spatial-channel collaborative attention mechanism to strengthen discriminative features for the target and suppress background interference. In the feature fusion phase, we design a structurally guided dual-level fusion module that enables structured cross-level interaction through the cooperation of a guidance path and a fidelity path, mitigating semantic dilution and improving the localization of small targets. Experimental results on a complex-scene dataset show that the proposed method outperforms several mainstream detectors, achieving an mAP@0.5 of 0.748 and increasing Recall to 0.692, while maintaining high inference efficiency, indicating a favorable accuracy-efficiency trade-off. Ablation studies and visual analyses further confirm the effectiveness of the proposed components for stable small-target detection and indicate its potential for real-time deployment.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-026-00141-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148837814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analytical study on the exposedness of potential positions for external human-machine interfaces","authors":"Jose Gonzalez-Belmonte,&nbsp;Jaerock Kwon","doi":"10.1007/s43684-026-00139-y","DOIUrl":"10.1007/s43684-026-00139-y","url":null,"abstract":"<div><p>As we move towards a future of autonomous vehicles, questions regarding their method of communication have arisen. One of the common concerns is the placement of the signaling used to communicate with pedestrians and road users, but few works have been fully dedicated to the matter. This paper uses a simulation made in the Unity game engine to record the fifteen different vehicles under fifty-seven different scenarios each for the first time, in order to find how often their forward-facing exterior surfaces can be seen by a pedestrian on the sidewalk. Variables include the vehicle type, position, number of vehicles on the road, camera position and direction, as well as its minimum and maximum distance from the recorded points. It was concluded that the areas of the vehicle most often seen by pedestrians on the sidewalk attempting to cross the road were the wheels, front fenders, and headlights. Based on these results, a suggestion is made to implement displays on at least two of the following regions: windshield, front fenders, side mirrors. These findings are valuable in the future design of signaling for autonomous vehicles in order to ensure pedestrians are able to see them on approaching vehicles. The software used provides a platform for similar work in the future to be conducted.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-026-00139-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148837815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating road infrastructure condition into intelligent transportation systems: a machine learning-based infrastructure-aware risk prediction approach","authors":"Mayssa Hamdani,&nbsp;Nafaa Jabeur,&nbsp;Ansar Yasar","doi":"10.1007/s43684-026-00137-0","DOIUrl":"10.1007/s43684-026-00137-0","url":null,"abstract":"<div><p>Although the majority of Intelligent Transportation Systems (ITS) and risk-prediction frameworks keep considering pavement quality and dynamic traffic behavior as separate phenomena, road infrastructure degradation and traffic flow instability both contribute to risky driving situations. This study combines segment-level Pavement Condition Index (PCI) data with actual traffic observations from New York City to present an integrated, data-driven methodology for simulating infrastructure-induced unsafe driving circumstances. A supervised machine learning model is developed by combining measures of traffic congestion, speed variation, and pavement deterioration to estimate hourly instability risk, which serves as a proxy for risky driving behavior. Evaluating on a temporally separated test set, the infrastructure-aware model achieves an ROC-AUC of 0.9804 and a PR-AUC of 0.9074, outperforming a traffic-only baseline (ROC-AUC 0.8785, PR-AUC 0.5833). These instability indicators correspond to high-level behavioral patterns commonly observed in ITS monitoring contexts, without relying on raw visual data. Model explainability using SHAP indicates that pavement condition and congestion are the most influential features, with comparable contributions to instability prediction. Predicted risk probabilities are geospatially mapped to identify infrastructure-driven hotspots, and the ORQCIAM framework demonstrates how such risk outputs can inform infrastructure-aware routing and maintenance prioritization. The findings reveal that machine learning enhanced with pavement condition data offers a data-driven approach for predicting hazardous driving situations and supporting infrastructure-aware decision-making, demonstrating how infrastructure-aware risk estimates might help with routing analysis and repair priority in future ITS applications.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-026-00137-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148782206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Safe integration of Large Language Models into industrial process control: a multi-agent architecture with P&ID-grounded validation","authors":"Daniel Schall","doi":"10.1007/s43684-026-00136-1","DOIUrl":"10.1007/s43684-026-00136-1","url":null,"abstract":"&lt;div&gt;&lt;p&gt;Large Language Models (LLMs) offer powerful reasoning capabilities for industrial process control, yet their non-deterministic nature, susceptibility to hallucination, and lack of intrinsic physical understanding make direct deployment in safety-critical environments unacceptable. This paper addresses five research questions on safely integrating LLM-based reasoning into industrial process automation through the Autonomous Action Execution (AAE) framework. For safe architectural integration (RQ1), we present a four-layer multi-agent architecture that confines LLM inference to an observation-only Monitor layer while safety-critical decisions are made by deterministic Verification and Execution agents. For structuring heterogeneous plant data (RQ2), we introduce a text-level aggregation framework with pluggable analyzers that transforms SCADA states, time-series measurements, Piping and Instrumentation Diagrams (P&amp;IDs), and Standard Operating Procedures (SOPs) into contextually rich documents for LLM consumption. For automated validation (RQ3), a P&amp;ID-grounded method uses graph traversal over the P&amp;ID topology to verify physical consistency of LLM-generated proposals, checking tag existence, actuatability, fail-state consistency, and downstream impact. For quantifiable context enrichment (RQ4), a graduated baseline comparison (B0–B3) demonstrates the incremental value of each pipeline component. For cross-domain generalisability (RQ5), evaluation across five industrial scenarios—three derived from the Tennessee Eastman Process (TEP) benchmark (Downs &amp; Vogel, 1993) providing community-standard validation, plus two retained scenarios (PolyReactor, Dryer) that establish performance boundaries from best-case (zero hallucination) to worst-case (70% safety violations)—demonstrates portability of the framework across continuous and batch processes of varying P&amp;ID complexity within the evaluated synthetic scenarios. An error injection study across 43 crafted proposals demonstrates that the validation layer achieves 100% recall (zero false negatives) on the covered failure modes (P&amp;ID-grounded structural checks together with the scenario-specific forbidden-action rubric). A statistical robustness study (&lt;i&gt;N&lt;/i&gt; = 50 LLM runs) shows that even when LLMs propose unsafe actions in 10%–70% of runs, the deterministic validation layer catches every invalid proposal in the covered categories (structurally invalid &lt;i&gt;or&lt;/i&gt; forbidden-action) under the evaluated scenarios. The core finding is that architecturally constrained advisory integration of LLMs in process industries is fundamentally an architectural challenge, and that established systems engineering principles of separation of concerns, independent protection layers, and deterministic safety logic provide deterministic checks against the covered validation-layer failure modes (structural and rubric-defined) when deploying LLM-based advisory systems in process industries.&lt;/p&gt;","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-026-00136-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diffusion-augmented YOLO26-Swin cascaded framework with hybrid SHAP-CAM for autonomous power grid inspection","authors":"Stefano Frizzo Stefenon,&nbsp;João Pedro Matos-Carvalho,&nbsp;Viviana Cocco Mariani,&nbsp;Leandro dos Santos Coelho,&nbsp;Kin-Choong Yow","doi":"10.1007/s43684-026-00135-2","DOIUrl":"10.1007/s43684-026-00135-2","url":null,"abstract":"<div><p>Deep learning-based autonomous inspection of power grid insulators is challenged by data imbalance and model opacity. This paper presents an end-to-end solution integrating advanced data synthesis, detection, classification, and explainability. First, a conditional diffusion model generates realistic synthetic fault images to balance the dataset. A two-stage architecture based on You Only Look Once version 26 (YOLO26) extra-large and Shifted windows (Swin)-V2-B, called YOLO26-Swin, fine-tuned with Bayesian optimization, performs robust insulator detection and then fault classification. Finally, a novel SHapley Additive exPlanations with Class Activation Mapping (SHAP-CAM) method provides intuitive visual explanations for model predictions. Extensive experiments validate our framework’s superiority: it achieves an F1-score of 0.98149 and a mean Average Precision (mAP)@[0.5] of 0.98951, exceeding leading detection and classification models. This work highlights the efficacy of diffusion models for data augmentation in critical infrastructure and advances the interpretability of vision-based inspection systems.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13249930/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148229509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}