Information Sciences最新文献_第2页

Interpretable structural modeling of MR images using q−Bézier curves: A geometry-aware paradigm beyond deep learning 使用q - bsamzier曲线的MR图像的可解释结构建模：一种超越深度学习的几何感知范式

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-27 DOI: 10.1016/j.ins.2026.123147

Faruk Özger , Aytuğ Onan , Nezihe Turhan , Zeynep Ödemiş Özger

Magnetic resonance (MR) imaging plays a critical role in diagnostic workflows, yet its reliability is frequently compromised by scanner-dependent bias, contrast variability, and intensity drift. Although deep learning methods achieve high performance, they generally require extensive supervision and demonstrate limited robustness across diverse clinical settings.

To address these challenges, we propose a transparent, geometry-aware framework for annotation-free MR enhancement based on

q

-Bézier curves. This model incorporates an adaptive deformation parameter

q (x)

that modulates local curvature, facilitating flexible adaptation to complex anatomical boundaries. The framework comprises three principal mechanisms: (i) adaptive

q (x)

for local responsiveness, (ii) monotone

q

-Bézier tone curves for intensity standardization, and (iii) Tikhonov-regularized optimization for smooth mapping. As a result, the operator remains interpretable, operates in linear time, and provides explicit control over smoothness.

The proposed approach was validated across five public cohorts (BraTS, ACDC, PROMISE12, fastMRI, IXI), demonstrating significant improvements in image fidelity (SSIM, CNR, NIQE) and downstream segmentation accuracy (Dice, HD95) relative to variational filters and state-of-the-art foundation models. Additionally, cross-vendor experiments confirm its robustness without the need for retraining. Collectively, these findings establish

q

-Bézier modeling as a principled, lightweight, and clinically interpretable alternative that complements deep learning by providing a geometry-aware pathway to robust MR representation.

磁共振成像在诊断工作流程中起着至关重要的作用，但其可靠性经常受到扫描仪相关偏差、对比度可变性和强度漂移的影响。尽管深度学习方法实现了高性能，但它们通常需要广泛的监督，并且在不同的临床环境中表现出有限的鲁棒性。为了解决这些挑战，我们提出了一个透明的、几何感知的框架，用于基于q- bsamizier曲线的无注释MR增强。该模型包含一个自适应变形参数q(x)，可调节局部曲率，促进对复杂解剖边界的灵活适应。该框架包括三个主要机制：(i)用于局部响应的自适应q(x), （ii）用于强度标准化的单调q- bzier音调曲线，以及（iii）用于平滑映射的tikhonov正则化优化。因此，操作符保持可解释性，在线性时间内操作，并提供对平滑性的显式控制。该方法在5个公共队列（BraTS、ACDC、PROMISE12、fastMRI、IXI）中得到验证，与变分滤波器和最先进的基础模型相比，在图像保真度（SSIM、CNR、NIQE）和下游分割精度（Dice、HD95）方面有了显著改善。此外，跨厂商实验证实了其鲁棒性，无需再训练。总的来说，这些发现确立了q- bsamzier建模作为一种有原则的、轻量级的、临床可解释的替代方案，通过提供一种几何感知的途径来实现鲁棒的MR表示，从而补充了深度学习。

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引用次数: 0

Global context modeling for image super-resolution transformer 图像超分辨率转换器的全局上下文建模

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-27 DOI: 10.1016/j.ins.2026.123135

Dongsheng Ruan , Yuan Zheng , Lide Mu , Ao Ran , Lei Pan , Mingfeng Jiang , Chengjin Yu , Nenggan Zheng , Huafeng Liu

Window-based Transformers have achieved remarkable results in image super-resolution (SR). State-of-the-art SR models generally employ a window self-attention mechanism combined with a multi-layer perceptron (MLP) to effectively capture long-range dependencies. However, the window design and the MLP’s deficiency in capturing spatial dependencies restrict their capacity to utilize global contextual information in images. This paper aims to address this limitation by introducing global context modeling. Specifically, we propose a general global context-injected framework for window self-attention. Within this framework, we develop a new instantiation with a novel global context-injected (GCI) module, which allows each window to take advantage of the contextual information from other windows. The GCI module is lightweight and can be easily integrated into existing window-based Transformers, improving performance with negligible increases in parameters and computational costs. Furthermore, we introduce a window self-attention (WSA) to vision state space (VSS) flow to further enhance the ability for global context modeling. We incorporate our advancements into popular SR models, such as SwinIR and SRFormer, creating enhanced versions. Extensive experiments on three representative SR tasks demonstrate the effectiveness of our methods, showing substantial performance improvements over their vanilla counterparts. Notably, our GCI-MSRformer outperforms current state-of-the-art models like MambaIR.

基于窗口的变形器在图像超分辨率（SR）方面取得了显著的成绩。最先进的SR模型通常采用窗口自注意机制结合多层感知器（MLP）来有效捕获远程依赖关系。然而，窗口设计和MLP在捕获空间依赖性方面的不足限制了它们利用图像中全局上下文信息的能力。本文旨在通过引入全局上下文建模来解决这一限制。具体来说，我们提出了一个通用的全局上下文注入框架，用于窗口自关注。在这个框架中，我们开发了一个新的实例化，使用一个新的全局上下文注入（GCI）模块，它允许每个窗口利用来自其他窗口的上下文信息。GCI模块重量轻，可以很容易地集成到现有的基于窗口的变压器中，在参数和计算成本几乎可以忽略不计的情况下提高性能。此外，在视觉状态空间流中引入窗口自关注（WSA），进一步增强了全局上下文建模的能力。我们将我们的进步纳入流行的SR模型，如SwinIR和SRFormer，创建增强版本。在三个具有代表性的SR任务上进行的大量实验证明了我们的方法的有效性，显示出与普通任务相比有实质性的性能改进。值得注意的是，我们的gci - msformer比MambaIR等当前最先进的机型性能更好。

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引用次数: 0

Temporal knowledge graph completion via global structural representation and deep interaction 基于全局结构表示和深度交互的时间知识图谱完成

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-24 DOI: 10.1016/j.ins.2026.123139

Jingbin Wang, Yumeng Zhang, Zeyuan Lin, Jinsong Lai, Kun Guo

Temporal knowledge graphs (TKGs) comprise timestamped facts and are widely used in intelligent systems. However, large-scale TKGs are often incomplete, therefore Temporal Knowledge Graph Completion (TKGC) is an important task. Existing approaches mostly use local neighborhoods to learn entity and relation representations, ignoring query-aware global semantics and semantic linkages between quadruples. Furthermore, timestamps are frequently considered as independent features, ignoring their periodicity and interactions with the graph structure. We propose T-GRIN (Temporal Graph completion via Representation and INteraction) to incorporate query-aware global semantic representations and deep interaction between entities and relations. T-GRIN employs a dynamic time encoder to capture periodic temporal patterns, an entity encoder with relation-enhanced mechanisms to highlight query-relevant contexts, and a relation encoder with multi-head attention to model diverse semantics under temporal and entity contexts. Furthermore, an interactive convolutional decoder is designed to improve feature interaction and high-order semantic composition. Extensive experiments on benchmark datasets demonstrate the effectiveness of T-GRIN. In ICEWS05-15, T-GRIN outperforms the previous best model by 8.9% MRR and 10.9% Hit@1.

时间知识图（TKGs）由时间标记的事实组成，广泛应用于智能系统。然而，大规模的知识图谱往往是不完整的，因此时间知识图谱补全（TKGC）是一个重要的任务。现有的方法大多使用局部邻域来学习实体和关系表示，忽略了查询感知的全局语义和四元组之间的语义联系。此外，时间戳经常被认为是独立的特征，忽略了它们的周期性和与图结构的相互作用。我们提出了T-GRIN（通过表示和交互完成时态图）来结合查询感知的全局语义表示和实体和关系之间的深度交互。T-GRIN采用动态时间编码器捕获周期性时间模式，采用关系增强机制的实体编码器突出显示查询相关上下文，采用多头关注的关系编码器在时间和实体上下文中建模不同的语义。此外，设计了交互式卷积解码器，以改善特征交互和高阶语义组合。大量的基准数据集实验证明了T-GRIN的有效性。在ICEWS05-15中，T-GRIN的MRR比之前的最佳模型高出8.9%，Hit@1高出10.9%。

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引用次数: 0

Maximum-Value retinex decomposition guided generative priors for joint deraining and low-light image enhancement 基于最大值视差分解的生成先验联合训练与弱光图像增强

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-24 DOI: 10.1016/j.ins.2026.123136

Yanfei Sun , Junyu Wang , Rui Yin

Nighttime rainy conditions severely degrade visual quality in applications such as autonomous driving and aerial surveillance, where images suffer from compounded low-light and rain degradations. Diffusion models offer strong generative priors but face limitations in image restoration, including poor controllability, structural distortion, and domain gaps with degraded images. We present MR-SDformer, a novel framework that integrates Retinex-based decomposition with diffusion priors for joint nighttime deraining and low-light enhancement. The key innovation is the Maximum-Value Retinex decomposition, which isolates high-intensity rain streaks into the illumination map and produces a rain-free reflectance map that faithfully preserves intrinsic scene content. This decomposition not only bridges the gap between rainy inputs and rain-free priors but also provides complementary guidance to the generative process. Building on this, we design an asymmetric Hybrid Conditional Transformer that leverages the decomposed illumination and reflectance maps to condition the frozen diffusion model more effectively, enabling precise multi-modal feature fusion and high-fidelity reconstruction. Extensive experiments on both synthetic and real-world datasets confirm that MR-SDformer achieves state-of-the-art performance, delivering clearer structure, enhanced illumination, and more realistic visual quality under nighttime rainy conditions.

夜间多雨条件会严重降低自动驾驶和空中监视等应用的视觉质量，因为这些应用的图像会受到低光和雨水的双重影响。扩散模型提供了强大的生成先验，但在图像恢复方面存在局限性，包括可控性差、结构失真和退化图像的域间隙。我们提出了MR-SDformer，这是一种新颖的框架，将基于维甲酸的分解与扩散先验相结合，用于联合夜间脱模和弱光增强。关键的创新是Maximum-Value Retinex分解，它将高强度的雨条纹隔离到照明图中，并产生一个忠实地保留固有场景内容的无雨反射图。这种分解不仅弥补了有雨输入和无雨先验之间的差距，而且还为生成过程提供了补充指导。在此基础上，我们设计了一个非对称混合条件转换器，利用分解的光照和反射率映射更有效地调节冻结扩散模型，实现精确的多模态特征融合和高保真重建。在合成和真实数据集上进行的大量实验证实，MR-SDformer实现了最先进的性能，在夜间下雨条件下提供更清晰的结构，增强的照明和更逼真的视觉质量。

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引用次数: 0

Scalable and generalizable path planning for robotic navigation using transformer-based heuristic learning 基于变换的启发式学习的机器人导航路径规划

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-24 DOI: 10.1016/j.ins.2026.123149

Elie Thellier, Adolfo Perrusquía, Antonios Tsourdos

Efficient and scalable path planning is a critical challenge for autonomous robotic systems, particularly in complex real-world scenarios. Traditional heuristic search algorithms like A

^{*}

often struggle with scalability and adaptability in such environments. To address these limitations, we improve a search framework that integrates learned, instance-specific heuristics with conventional pathfinding techniques. Leveraging autoencoder transformer networks, we predict two key heuristic functions—Correction Factor (CF) and Path Probability Map (PPM)—trained on diverse datasets—the Motion Planning (MP) and Tiled-MP datasets—to cover a wide range of path planning scenarios. When integrated with Weighted A

^{*}

(WA

^{*}

) algorithm, this approach optimally solves 88% of MP instances, with paths averaging less than 0.7% longer than optimal, and requiring nearly five times fewer node expansions. The framework demonstrates the advantages of heuristic learning in handling larger path planning problems, with inference time accounting for just 10% of the total search duration. It solves nearly half of the most complex instances optimally, showcasing strong scalability for real-time robotics applications. The framework performs well in unseen environments, solving over 25% of new problems perfectly, finding near-optimal solutions with paths less than 7% longer than optimal, and requiring fewer than two-thirds of the typical expansions. Our framework outperforms learnable planners in both scalability and generalization.

高效和可扩展的路径规划是自主机器人系统面临的关键挑战，特别是在复杂的现实世界场景中。传统的启发式搜索算法（如A *）在这样的环境中经常与可伸缩性和适应性作斗争。为了解决这些限制，我们改进了一个搜索框架，该框架将学习的、特定于实例的启发式与传统的寻路技术集成在一起。利用自编码器变压器网络，我们预测了两个关键的启发式函数-校正因子（CF）和路径概率图(PPM) -在不同的数据集上训练-运动规划（MP）和平铺MP数据集-以覆盖广泛的路径规划场景。当与加权A∗(WA∗)算法集成时，该方法最优地解决了88%的MP实例，平均路径比最优时间长不到0.7%，并且需要的节点扩展几乎减少了五倍。该框架展示了启发式学习在处理较大路径规划问题方面的优势，推理时间仅占总搜索时间的10%。它最优地解决了近一半最复杂的实例，展示了实时机器人应用程序的强大可扩展性。该框架在不可见的环境中表现良好，完美地解决了超过25%的新问题，找到了接近最优的解决方案，比最优路径长不到7%，所需的扩展不到典型扩展的三分之二。我们的框架在可扩展性和泛化方面都优于可学习规划器。

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引用次数: 0

BPHD: Enterprise bankruptcy prediction with a hierarchical hypergraph and dual-decision experts 基于层次超图和双重决策专家的企业破产预测

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-23 DOI: 10.1016/j.ins.2026.123142

Boyuan Ren , Hongrui Guo , Hongzhi Liu , Xudong Tang , Jingming Xue , Zhonghai Wu

In today’s economic climate, enterprises face a variety of internal and external risks, making bankruptcy prediction critical for risk management. The traditional statistical and machine learning-based methods mainly rely on economic indicators, which are insufficient for deducing the risk propagation among enterprises. Recently, researchers have begun to explore the use of graph neural networks, utilizing their message-passing mechanisms to simulate the risk propagation process. However, existing graph-based methods often neglect degree imbalances, leading to high misjudgment rates for sparsely connected nodes. Furthermore, existing methods typically use a risk-oriented decision model to evaluate the likelihood of bankruptcy, which may lead to the overestimation of bankruptcy probabilities.

To address these issues, we propose a novel bankruptcy prediction model which consists of several key components, including a data-driven explicit risk encoding module, a global multihead attention-based implicit risk encoding module, a hierarchical hypergraph-based external risk enhancement module, and a dual-decision expert-based risk assessment module. We extend the traditional graph structure to a hierarchical hypergraph structure and design a corresponding information propagation strategy to alleviate the degree imbalance issue. Furthermore, a dual-decision assessment module is designed to integrate the perspectives of both risk and non-risk experts to prevent the overestimation of bankruptcy probabilities. Extensive experiments conducted on a real-world dataset demonstrate the effectiveness of the proposed model, which achieves an accuracy of 77.64% and an AUC of 0.8270, significantly outperforming existing methods.

在当今的经济气候下，企业面临着各种各样的内部和外部风险，破产预测对于风险管理至关重要。传统的统计方法和基于机器学习的方法主要依赖于经济指标，不足以推断企业之间的风险传播。近年来，研究人员开始探索使用图神经网络，利用其消息传递机制来模拟风险传播过程。然而，现有的基于图的方法往往忽略度不平衡，导致对稀疏连接节点的误判率很高。此外，现有方法通常使用风险导向的决策模型来评估破产可能性，这可能导致对破产概率的高估。为了解决这些问题，我们提出了一种新的破产预测模型，该模型由几个关键组件组成，包括数据驱动的显式风险编码模块、基于全局多头注意的隐式风险编码模块、基于分层超图的外部风险增强模块和基于双决策专家的风险评估模块。我们将传统的图结构扩展为层次超图结构，并设计了相应的信息传播策略来缓解度不平衡问题。此外，设计了双重决策评估模块，以整合风险专家和非风险专家的观点，防止对破产概率的高估。在真实数据集上进行的大量实验证明了该模型的有效性，其准确率达到77.64%，AUC为0.8270，显著优于现有方法。

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引用次数: 0

T-MLA: A targeted multiscale log–exponential attack framework for neural image compression 一种针对神经图像压缩的多尺度对数指数攻击框架

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-23 DOI: 10.1016/j.ins.2026.123143

Nikolay I. Kalmykov , Razan Dibo , Kaiyu Shen , Zhonghan Xu , Anh-Huy Phan , Yipeng Liu , Ivan Oseledets

Neural image compression (NIC) has become the state-of-the-art for rate-distortion performance, yet its security vulnerabilities remain significantly less understood than those of classifiers. Existing adversarial attacks on NICs are often naive adaptations of pixel-space methods, overlooking the unique, structured nature of the compression pipeline. In this work, we propose a more advanced class of vulnerabilities by introducing T-MLA, the first targeted multiscale log–exponential attack framework. We introduce adversarial perturbations in the wavelet domain that concentrate on less perceptually salient coefficients, improving the stealth of the attack. Extensive evaluation across multiple state-of-the-art NIC architectures on standard image compression benchmarks reveals a large drop in reconstruction quality while the perturbations remain visually imperceptible. On standard NIC benchmarks, T-MLA achieves targeted degradation of reconstruction quality while improving perturbation imperceptibility (higher PSNR/VIF of the perturbed inputs) compared to PGD-style baselines at comparable attack success, as summarized in our main results. Our findings reveal a critical security flaw at the core of generative and content delivery pipelines.

神经图像压缩（NIC）已经成为最先进的率失真性能，但其安全漏洞仍然远远少于分类器。现有的针对nic的对抗性攻击通常是对像素空间方法的天真改编，忽略了压缩管道的独特、结构化性质。在这项工作中，我们提出了一个更高级的漏洞类别，通过引入T-MLA，第一个目标多尺度对数指数攻击框架。我们在小波域中引入对抗性扰动，集中在感知上不太显著的系数上，提高了攻击的隐身性。在标准图像压缩基准上对多个最先进的NIC架构进行了广泛的评估，发现重建质量大幅下降，而扰动在视觉上仍然难以察觉。在标准NIC基准测试中，与pgd风格的基线相比，T-MLA实现了有针对性的重构质量退化，同时提高了扰动不可感知性（受扰动输入的更高PSNR/VIF），并取得了类似的攻击成功，正如我们的主要结果所总结的那样。我们的发现揭示了生成和内容交付管道核心的一个关键安全漏洞。

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引用次数: 0

Input convex stochastic configuration networks modeling method for predictive control 预测控制的输入凸随机组态网络建模方法

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-23 DOI: 10.1016/j.ins.2026.123131

Aijun Yan , Xin Liu

In the modeling process of model predictive control (MPC), the model typically exhibits non-convex characteristics, which make the optimization problem complex and prone to local optima. To address this, an MPC modeling method based on input convex stochastic configuration networks (SCN) is proposed. The method imposes convexity constraints on both network architecture and activation functions. A Sparsemax-based activation function selection mechanism is developed to adaptively choose convex activation functions for each configuration node. Output weights are determined using the alternating direction method of multipliers to solve least-squares problems with non-negative constraints. Two architectures are constructed: fully input convex and partially input convex SCN. Through a dynamic supervision mechanism, it is theoretically proven that the proposed model approximates convex functions to arbitrary accuracy under weight constraints. Experimental results demonstrate improved fitting accuracy with convex approximation guarantees, and control examples show enhanced closed-loop performance by ensuring MPC optimization convexity.

在模型预测控制（MPC）的建模过程中，模型通常具有非凸特性，这使得优化问题复杂且容易出现局部最优。针对这一问题，提出了一种基于输入凸随机配置网络（SCN）的MPC建模方法。该方法对网络结构和激活函数都施加了凸性约束。提出了一种基于sparsemax的激活函数选择机制，为每个配置节点自适应地选择凸激活函数。利用乘子的交替方向法确定输出权值，求解非负约束的最小二乘问题。构造了两种结构：完全输入凸SCN和部分输入凸SCN。通过动态监督机制，从理论上证明了该模型在权约束下可以逼近任意精度的凸函数。实验结果表明，采用凸近似保证可以提高拟合精度，控制实例通过保证MPC优化的凸性来提高闭环性能。

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引用次数: 0

Neural architecture search using an enhanced particle swarm optimization algorithm for industrial image classification 基于神经结构搜索的增强粒子群优化工业图像分类算法

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-23 DOI: 10.1016/j.ins.2026.123141

Rongna Cai , Haibin Ouyang , Steven Li , Gaige Wang , Weiping Ding

To tackle challenges in industrial image defect detection, guided by three core hypotheses: dataset representativeness, continuous differentiable NAS search space, and GPU-based computing environment, this study presents an enhanced particle swarm optimization (PSO)-based neural architecture search (NAS) method designated as DNE-PSO-NAS. Firstly, it employs a two-level binary particle encoding scheme for network layer configurations and connectivity, transforming architecture search into a multi-dimensional optimization problem. Secondly, an improved MBConv module with CBAM is developed to reinforce the model’s ability to perceive local and global features of defects, thereby raising the signal-to-noise ratio for tiny defect regions. Additionally, dynamic ring neighborhood velocity topology and swarm entropy-driven mutation are proposed to balance exploration and exploitation, boosting PSO’s optimization efficiency. Finally, a low-fidelity evaluation strategy is incorporated, forming a three-stage framework that reduces input space via image downsampling, compresses convolutional layer parameters to lower spatial complexity, and adopts a dynamic training termination mechanism based on fitness tracking. Experiments on NEU-DET and WM-811 K datasets demonstrate that its discovered architectures surpass traditional CNNs and SOTA methods, with classification accuracy reaching 100% on NEU-DET and 93% on WM-811K. Meanwhile, our algorithm cuts computational costs significantly and the results highlight major benefits for real-time industrial quality inspection.

针对工业图像缺陷检测中存在的问题，在数据集代表性、连续可微NAS搜索空间和基于gpu的计算环境三个核心假设的指导下，提出了一种基于增强粒子群优化（PSO）的神经结构搜索（NAS）方法，命名为DNE-PSO-NAS。首先，对网络层配置和连通性采用两级二进制粒子编码方案，将架构搜索转化为多维优化问题；其次，开发了改进的CBAM MBConv模块，增强了模型感知缺陷局部和全局特征的能力，从而提高了微小缺陷区域的信噪比。此外，提出了动态环邻域速度拓扑和群体熵驱动突变来平衡勘探和开采，提高了粒子群算法的优化效率。最后，引入低保真度评估策略，通过图像降采样减少输入空间，压缩卷积层参数降低空间复杂度，采用基于适应度跟踪的动态训练终止机制，形成三阶段框架。在nue - det和WM-811K数据集上的实验表明，其发现的体系结构优于传统的cnn和SOTA方法，在nue - det上的分类准确率达到100%，在WM-811K上的分类准确率达到93%。同时，我们的算法显著降低了计算成本，结果突出了实时工业质量检测的主要优势。

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引用次数: 0

Lane-flow-learning based autonomous vehicle trajectory prediction using spatial–temporal fusion attention 基于车道流学习的自动驾驶车辆轨迹预测

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-22 DOI: 10.1016/j.ins.2026.123134

Haipeng Cui , Kai Xiao , Hua Wang , Xuxin Zhang

High-precision trajectory prediction can promote safe and efficient autonomous driving decisions. Existing state-of-the-art models, such as Dual-Attention Mechanism (DAM) and Hierarchical Attention Network (HAN), treat all neighboring vehicles as undifferentiated sets, ignoring lane structures when extracting spatial features. In this study, we propose a novel Lane-specific Spatial-Temporal Attention Network (LSTAN) to address the lane-level traffic information in vehicle trajectory prediction. Specifically, we employ an encoder module based on a Long Short-Term Memory Network to extract temporal features for target vehicles and their surrounding vehicles. Meanwhile, a lane attention module (LAM) and a temporal self-attention module (TSAM) are proposed for spatial and temporal feature extractions. The LAM introduces a dual-attention framework to discern spatial relationships between the target vehicle and its surrounding vehicles considering the lane-level effects. The TSAM refines the temporal features for target vehicles. Finally, the decoder integrates the learned features with the driving intention to obtain the predicted trajectories. Experiments are conducted using two real-world datasets: the next generation simulation (NGSIM) and HighD. Results show that the LSTAN outperforms the benchmarks by an average root mean square error (RMSE) of 0.37 m. Ablation studies and component replacement experiments are conducted to evaluate the effectiveness of the components in LSTAN.

高精度的轨迹预测可以促进安全高效的自动驾驶决策。现有的先进模型，如双注意机制（Dual-Attention Mechanism， DAM）和分层注意网络（Hierarchical Attention Network， HAN），在提取空间特征时将所有相邻车辆视为未分化集合，忽略车道结构。在这项研究中，我们提出了一种新的车道特定时空注意网络（LSTAN）来解决车道级交通信息在车辆轨迹预测中的问题。具体而言，我们采用基于长短期记忆网络的编码器模块来提取目标车辆及其周围车辆的时间特征。同时，提出了车道注意模块（LAM）和时间自注意模块（TSAM）进行时空特征提取。LAM引入了一个双重注意框架来识别考虑车道水平效应的目标车辆和周围车辆之间的空间关系。TSAM改进了目标车辆的时间特征。最后，解码器将学习到的特征与驾驶意图相结合，得到预测轨迹。实验使用了两个真实世界的数据集：下一代模拟（NGSIM）和HighD。结果表明，LSTAN的平均均方根误差（RMSE）为0.37 m，优于基准测试。通过烧蚀研究和组件替换实验来评估LSTAN中组件的有效性。

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引用次数: 0