alexandria engineering journal最新文献_第9页

A Müntz spectral method for solving fractional Fredholm integro–differential equations with convergence analysis 求解分数阶Fredholm积分微分方程的<s:1> ntz谱法及其收敛性分析

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-01-01 Epub Date: 2025-12-24 DOI: 10.1016/j.aej.2025.12.040

Jabbar Mahdy Hadaad , Masoud Allame , Habeeb Abed Kadhim Aal-Rkhais , Majid Tavassoli-Kajani

The solutions of fractional order equations might involve certain fractional-power terms that classical orthogonal polynomials connot match. Consequently, the advancement of effective numerical methods using generalized orthogonal polynomials such as fractional Jacobi, Müntz and fractional Chelyshkov functions enhances the precision of approximate solutions. This paper proposes a novel Müntz–Legendre spectral approach for a class of fractional Fredholm integro–differential equations with Caputo or Caputo–Fabrizio (CF) derivative. We first construct a matrix method that transforms the given linear problem to a system of linear algebraic equations. Then, we give a comprehensive convergence analysis of the proposed method. As opposed to the Caputo definition, the derivative of CF has no singularity at the end point, so it is expected that it is more convenient for numerical studies. Nonetheless, we propose a new approach to deal with the singularity in the definition of the Caputo derivative, efficiently. Some numerical examples are given and comparisons with other existing methods are provided to demonstrate the efficiency and accuracy of the proposed method. The extension of the proposed method to nonlinear problems via the linearization technique is also illustrated in an example.

分数阶方程的解可能涉及某些经典正交多项式无法匹配的分数次项。因此，利用分数阶Jacobi、m ntz和分数阶Chelyshkov函数等广义正交多项式的有效数值方法的进步提高了近似解的精度。针对一类具有Caputo或Caputo - fabrizio （CF）导数的分数阶Fredholm积分微分方程，提出了一种新的m ntz - legendre谱方法。我们首先构造一个矩阵方法，将给定的线性问题转化为线性代数方程组。然后，对该方法进行了全面的收敛性分析。与Caputo定义相反，CF的导数在端点处没有奇点，因此有望更方便于数值研究。尽管如此，我们还是提出了一种新的方法来有效地处理卡普托导数定义中的奇点。最后给出了数值算例，并与现有方法进行了比较，验证了该方法的有效性和准确性。通过实例说明了该方法通过线性化技术推广到非线性问题。

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引用次数: 0

Assessment of groundwater quantity and quality indexes and WQI determination for drinking and agricultural uses: Alborz Plain, Iran case study 用于饮用和农业用途的地下水数量和质量指标评价及WQI的确定：伊朗Alborz平原案例研究

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-01-01 Epub Date: 2025-12-19 DOI: 10.1016/j.aej.2025.12.025

Mehdi Hedayatpour , Hamidreza Rabieifar , Hossein Hassanpour Darvishi , Reza Shirinabadi , Saeid Farokhizadeh

This study evaluates groundwater quantity and quality variations in Alborz Plain and assesses suitability for drinking and agriculture using the Water Quality Index (WQI). Data from 19 aquifers (2002–2023) were analyzed for key parameters: K⁺, Na⁺, Mg²⁺, Ca²⁺, SO₄²⁻, Cl⁻, HCO₃⁻, NO₃⁻, pH, EC, and TDS. Piper, Scholler, and Wilcox diagrams were prepared using Aq.QA and MATLAB to identify dominant facies and evaluate agricultural and drinking suitability. WQI results indicate “very good” quality in northwest and central regions, declining to “good” in east and improving in southeast. Dominant facies include Ca-HCO₃ or Mg-HCO₃ in northwest, center, and southeast, and Na-HCO₃ in east, with three aquifers exhibiting Na-SO₄, Na-Cl, and Ca-SO₄ facies. W1 and W3 aquifers are suitable for drinking despite high EC but show agricultural limitations in salt-sensitive areas. Long-term quality trends were assessed using Mann-Kendall test and Sen’s slope, while PCA and K-means clustering identified seven stable aquifer groups. SVR and Random Forest models accurately predicted TDS trends. Salinity is mainly influenced by regional geology, including evaporitic and dolomitic rocks, rather than proximity to sea. This integrated approach provides a practical framework for understanding groundwater dynamics and supporting sustainable management at regional and national levels.

本研究利用水质指数（WQI）评价了阿尔博斯平原地下水的数量和质量变化，并对饮用和农业适宜性进行了评价。对来自19个含水层（2002-2023年）的数据进行了关键参数分析：K⁺、Na⁺、Mg⁺、Ca⁺、SO₄²⁻、Cl⁻、HCO₃⁻、NO₃⁻、pH、EC和TDS。利用Aq.QA和MATLAB编制Piper、Scholler和Wilcox图，确定优势相，评价农业和饮用适宜性。WQI结果显示，西北和中部地区为“非常好”，东部地区为“好”，东南部地区为“好”。主要相为西北部、中部和东南部的Ca-HCO₃或Mg-HCO₃，东部的Na-HCO₃，3个含水层表现为Na-SO₄、Na-Cl和Ca-SO₄相。W1和W3含水层适合饮用，尽管高EC，但在盐敏感地区显示出农业限制。采用Mann-Kendall检验和Sen’s slope对含水层的长期质量趋势进行了评价，而PCA和K-means聚类则确定了7个稳定的含水层组。SVR和随机森林模型准确地预测了TDS的趋势。盐度主要受区域地质影响，包括蒸发岩和白云岩，而不是靠近海洋。这种综合方法为了解地下水动态和支持区域和国家一级的可持续管理提供了一个实用框架。

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引用次数: 0

EEG-based multi-scale perturbed brain cognitive pattern recognition network for gamer level classification 基于脑电图的多尺度摄动脑认知模式识别网络用于玩家等级分类

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-01-01 Epub Date: 2025-12-05 DOI: 10.1016/j.aej.2025.12.004

Lijun Jiang , Yanping Chen , Kexuan Liu , Xingyuan Chen , Li Dong , Weiyi Ma , Diankun Gong , Dezhong Yao

Electroencephalogram (EEG)-based classification of video game experts versus amateurs reveals cognitive brain patterns underlying complex abilities, with applications in attention modulation, medical rehabilitation, and health monitoring. While deep learning has advanced EEG-based cognitive state detection, challenges remain in extracting meaningful patterns from noisy data and preventing reverse inference attacks on user privacy. Here, we propose a novel multi-scale perturbed brain cognitive pattern recognition network (MsPE). Its key contributions are: (1) a multi-scale weak encryption method with attention mechanisms that protects privacy by perturbing EEG signals in temporal and frequency domains; (2) ConvFormer modules with adaptive channel sizes (3, 5, 15) and attention fusion to generate personalized perturbations while preserving task-relevant information; (3) a Denoise Feature Extraction Block (DFEB) using deep separable CNNs with skip connections to extract spatio-temporal features and reduce noise. Validated on a gaming EEG dataset, MsPE achieves 88.75% accuracy, 90.27% recall, 85.11% specificity, an F1 score of 0.8923, and a Kappa coefficient of 0.6957, outperforming existing methods. Interpretability analysis reveals distinct cognitive patterns between experts and amateurs in the temporal, occipital, and frontal lobes, with the most pronounced differences in the frontal lobe. This study advances an effective, secure, and accurate EEG-based cognitive pattern analysis solution.

基于脑电图（EEG）的电子游戏专家与业余爱好者的分类揭示了复杂能力背后的认知大脑模式，并在注意力调节、医疗康复和健康监测方面得到了应用。虽然深度学习具有先进的基于脑电图的认知状态检测，但在从噪声数据中提取有意义的模式和防止对用户隐私的反向推理攻击方面仍然存在挑战。本文提出了一种新的多尺度扰动脑认知模式识别网络（MsPE）。主要贡献有：(1)提出了一种基于注意机制的多尺度弱加密方法，该方法通过对脑电图信号进行时域和频域扰动来保护隐私；(2)具有自适应通道大小（3,5,15）和注意力融合的ConvFormer模块，在保留任务相关信息的同时产生个性化扰动；(3)利用带跳跃连接的深度可分离cnn提取时空特征并降低噪声的降噪特征提取块（DFEB）。在游戏脑电数据集上验证，MsPE的准确率为88.75%，召回率为90.27%，特异性为85.11%，F1得分为0.8923，Kappa系数为0.6957，优于现有方法。可解释性分析揭示了专家和业余爱好者在颞叶、枕叶和额叶的不同认知模式，其中额叶的差异最明显。本研究提出了一种有效、安全、准确的基于脑电图的认知模式分析解决方案。

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引用次数: 0

CCFormer: A cascaded transformer framework for precise temporal audio-visual deepfake localization CCFormer：一种用于精确时相视听深度定位的级联变压器框架

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-01-01 Epub Date: 2025-12-12 DOI: 10.1016/j.aej.2025.12.001

Peifan Li , Jinluan Ren

Audio-visual deepfake detection presents significant computational challenges in achieving precise temporal boundary localization beyond traditional binary classification approaches. This study presents CCFormer, a cascaded optimization framework that integrates ConvNeXt-V2 visual forgery detection with CrossFormer cross-modal localization for precise temporal forgery localization. The framework employs a two-stage strategy where ConvNeXt-V2 performs efficient suspicious segment screening through multi-scale spatiotemporal feature extraction, while CrossFormer achieves frame-level precision through multi-head cross-modal attention mechanisms for optimal audio-visual feature alignment. Experiments on the LAV-DF dataset demonstrate that CCFormer achieving 96.30 % [email protected] and 84.96 % [email protected] The framework achieves inference time of 23.4 ms per video, representing 58.1 % improvement over conventional end-to-end architectures. Ablation studies reveal that the CrossFormer module increases detection performance in high-precision IoU intervals by 153.4 % compared to the baseline methods. The optimization framework successfully transforms coarse-grained binary classification into precise temporal boundary localization,

视听深度假检测在实现精确的时间边界定位方面面临着巨大的计算挑战，超出了传统的二值分类方法。本研究提出了CCFormer，一个级联优化框架，集成了ConvNeXt-V2视觉伪造检测和CrossFormer跨模态定位，用于精确的时间伪造定位。该框架采用两阶段策略，其中ConvNeXt-V2通过多尺度时空特征提取进行有效的可疑片段筛选，而CrossFormer通过多头跨模态注意机制实现帧级精度，实现最佳视听特征对齐。在LAV-DF数据集上的实验表明，CCFormer实现了96.30 % [email protected]和84.96 % [email protected]，每个视频的推理时间为23.4 ms，比传统的端到端架构提高了58.1% %。烧蚀研究表明，与基准方法相比，CrossFormer模块在高精度IoU层的检测性能提高了153.4 %。优化框架成功地将粗粒度二值分类转化为精确的时间边界定位；

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引用次数: 0

A transfer learning-based deep focal multiclass network for psychological emotion recognition in community-correction populations 基于迁移学习的社区矫正人群心理情绪识别深度焦点多类网络

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-01-01 Epub Date: 2026-01-07 DOI: 10.1016/j.aej.2025.12.027

Xuan Chen

Facial expression-based emotion recognition technology holds significant application value in fields such as intelligent security and psychological intervention. In particular, for individuals under community correction, automated emotion analysis can assist in psychological assessment and behavioral risk monitoring, thereby enhancing the scientific rigor and real-time effectiveness of interventions. Recent studies have proposed various deep learning-based classification models to improve emotion recognition performance in complex scenarios. However, the performance of psychological emotion recognition models based on facial images is still limited by factors such as the scale of training data and class imbalance. To address these challenges, this study focuses on the task of psychological emotion recognition for community-correction populations and propose a novel Transfer Learning-based deep Focal multiclass Network (TLFNet). Specifically, the TLFNet model incorporates a new multiclass Focal Loss function to optimize its parameters, which enhances the model’s sensitivity to minority-class samples and mitigates the bias introduced by class imbalance. Moreover, under the transfer learning framework, TLFNet adopts ImageNet pre-trained weights to incorporate large-scale visual prior knowledge. Extensive experiments conducted on a real-world emotion recognition dataset demonstrate the effectiveness of each component of the TLFNet model and further validate its superior overall performance in the target task.

基于面部表情的情感识别技术在智能安防、心理干预等领域具有重要的应用价值。特别是对于社区矫正的个体，自动化情绪分析可以辅助心理评估和行为风险监测，从而提高干预的科学严密性和实时性。最近的研究提出了各种基于深度学习的分类模型来提高复杂场景下的情绪识别性能。然而，基于人脸图像的心理情绪识别模型的性能仍然受到训练数据规模和类别不平衡等因素的限制。针对这些挑战，本研究针对社区矫正人群的心理情绪识别任务，提出了一种基于迁移学习的深度焦点多类网络（TLFNet）。具体来说，TLFNet模型引入了一个新的多类焦点损失函数来优化其参数，提高了模型对少数类样本的灵敏度，减轻了类不平衡带来的偏差。此外，在迁移学习框架下，TLFNet采用ImageNet预训练的权值来整合大规模的视觉先验知识。在现实世界的情感识别数据集上进行的大量实验证明了TLFNet模型的每个组成部分的有效性，并进一步验证了其在目标任务中的优越整体性能。

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引用次数: 0

A meta-learning enhanced dynamic graph convolutional network for cross-region financial risk propagation prediction 基于元学习的动态图卷积网络跨区域金融风险传播预测

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-01-01 Epub Date: 2026-01-16 DOI: 10.1016/j.aej.2025.12.050

Chao Zhang , Yingyue Hu

The increasing interconnectedness of global financial systems has amplified the risk of cross-regional financial contagion, posing significant challenges to economic stability. Traditional models often struggle to capture the dynamic spatio-temporal dependencies in financial networks, particularly under heterogeneous and sparse data conditions. To address this, we propose a novel framework, Meta-Dynamic Graph Convolutional Network, which integrates meta-learning with dynamic graph convolutional networks for cross-regional financial risk propagation prediction—the first such integration to enhance adaptability in sparse and heterogeneous financial scenarios. Our approach employs dynamic graph convolutional networks to model the evolving financial network’s spatio-temporal dynamics, incorporating graph convolution, temporal attention mechanisms, and dynamic edge updates. Furthermore, meta-learning optimizes model initialization, enhancing generalization across regions with limited data. Experiments on public financial datasets and simulated networks demonstrate that our framework outperforms baselines, achieving a statistically significant (p < 0.05 via t-tests) 25 %–49 % reduction in mean absolute error and root mean square error, and a 20 %–34 % improvement in F1 score. It predicts both regression-based risk values, such as economic recession indices, and classification-based risk categories, such as high or low risk.

全球金融体系相互联系日益紧密，加大了跨区域金融传染的风险，对经济稳定构成重大挑战。传统模型往往难以捕捉金融网络中的动态时空依赖关系，特别是在异构和稀疏数据条件下。为了解决这个问题，我们提出了一个新的框架——元动态图卷积网络，它将元学习与动态图卷积网络集成在一起，用于跨区域金融风险传播预测——这是第一个这样的集成，以增强在稀疏和异构金融场景中的适应性。我们的方法采用动态图卷积网络来模拟不断发展的金融网络的时空动态，结合图卷积、时间注意机制和动态边缘更新。此外，元学习优化了模型初始化，增强了数据有限区域的泛化能力。在公共金融数据集和模拟网络上的实验表明，我们的框架优于基线，实现了统计显著（p <； 0.05通过t检验），平均绝对误差和均方根误差降低了25 % -49 %，F1分数提高了20 % -34 %。它既可以预测基于回归的风险值，如经济衰退指数，也可以预测基于分类的风险类别，如高风险或低风险。

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引用次数: 0

Analytic function classes defined by Mittag–Leffler inspired Poisson-type series 由Mittag-Leffler启发的泊松类型系列定义的解析函数类

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-01-01 Epub Date: 2026-01-12 DOI: 10.1016/j.aej.2026.01.001

Stalin Thangamani , Dumitru Baleanu , Supprabha Authimoolam , Majeed Ahmad Yousif , Thabet Abdeljawad , Pshtiwan Othman Mohammed

In this article, we introduce new subclass

{SM}_{α, β, λ}^{m} (γ)

of univalent functions based on Mittag-Leffler function related to the distribution series within the unit disc

U = {ζ \in C : | ζ | < 1}

. Further the fundamental properties such as growth, distortion, extreme points, convexity, close-to-convexity, starlike and coefficient inequalities have been estimated for the subclass. In addition, we consider an integral means of inequality for the subclass. This work bridges the gap between fractional integral operators and Poisson distribution series by incorporating both into a new subclass of univalent functions. This integrative approach provides the valuable insights into the applications of geometric function theory in signal and image processing.

本文基于单位圆盘U={ζ∈C:|ζ|<；1}内分布级数相关的Mittag-Leffler函数，引入一元函数的新子类SMα，β,λm（γ）。进一步估计了该类的生长、畸变、极值点、凸性、近凸性、星形不等式和系数不等式等基本性质。此外，我们考虑了子类的不等式的积分方法。这项工作弥合了分数积分算子和泊松分布级数之间的差距，将两者合并到一价函数的新子类中。这种综合方法为几何函数理论在信号和图像处理中的应用提供了有价值的见解。

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引用次数: 0

Intelligent fault detection of zero-sample rotating machinery with embedded physical knowledge of vibration envelope and time-frequency analysis 基于振动包络物理知识和时频分析的零样本旋转机械故障智能检测

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-01-01 Epub Date: 2025-12-24 DOI: 10.1016/j.aej.2025.12.030

Guoqiang Li , Cheng Chen , Qijun Liu , Yiwei Cheng , Meirong Wei , Defeng Wu

Data-driven fault detection methods for rotating machinery have achieved impressive performance. Nevertheless, their practical deployment faces substantial challenges, including the high cost of acquiring fault data and inherent difficulties in constructing accurate models. This paper integrates domain knowledge of vibration signal analysis and proposes a physical knowledge-driven modeling method for rotating machinery fault detection with zero fault sample. First, Hilbert envelope analysis is introduced to attenuate the impact of fundamental frequency components. Subsequently, multi-dimensional evaluation metrics are used to select and filter multiple time-frequency analysis methods, thereby constructing a robust time-frequency knowledgebase. Then, three novel loss function driven by zero-fault samples is designed based on the differences between the selected time-frequency analysis methods and physical knowledge regarding the similarity among sliding window samples in monitoring signals. Finally, an end-to-end intelligent fault detection algorithm is developed based on the trained feature encoder and the introduced physical knowledge. The effectiveness of the proposed method is validated on both the rolling bearing experimental platform and the turbine experimental platform. The validation results demonstrate that the proposed method can achieve intelligent fault detection modelling without any fault samples, attaining fault detection test accuracies of 98.97 % and 96.19 % in the two respective case studies.

数据驱动的旋转机械故障检测方法已经取得了令人瞩目的成绩。然而，它们的实际部署面临着巨大的挑战，包括获取故障数据的高成本和构建准确模型的固有困难。结合振动信号分析的领域知识，提出了一种零故障样本下旋转机械故障检测的物理知识驱动建模方法。首先，引入希尔伯特包络分析来减弱基频分量的影响。然后，利用多维评价指标对多种时频分析方法进行选择和过滤，构建鲁棒的时频知识库。然后，基于所选时频分析方法与监测信号滑动窗样本相似性物理知识的差异，设计了三种新的零故障样本驱动的损失函数。最后，基于训练好的特征编码器和引入的物理知识，开发了端到端的智能故障检测算法。在滚动轴承实验平台和涡轮实验平台上验证了该方法的有效性。验证结果表明，该方法可以在不需要任何故障样本的情况下实现智能故障检测建模，在两个案例中分别获得了98.97 %和96.19 %的故障检测测试准确率。

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引用次数: 0

Quantum-inspired deep learning optimisation for real-time student engagement analysis in virtual classrooms 量子启发的深度学习优化，用于虚拟教室中的实时学生参与分析

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-01-01 Epub Date: 2025-12-19 DOI: 10.1016/j.aej.2025.12.019

Hanan T. Halawani , Shuhrah Alghamdi , Fahad Ali Aloufi , Maryam Alsolami , Abdulellah Alsulaimani , Hassan M. Aljohani

Quantum computing (QC) employs quantum-mechanical principles such as superposition and entanglement to solve specific problems far more efficiently than classical computers. E-learning is the only option for students and teachers during the pandemic. Conversely, it is challenging for an instructor to observe every student’s engagement while educating online. Students are distracted during such activities. The teachers want to identify their students' states, whether they are concentrated or concerned with teaching. Hence, from a teacher’s viewpoint, it is significant to assess students' levels of engagement to understand their actual reactions and take the necessary steps to involve students and help them achieve goals, machine learning (ML) and deep learning (DL) is deployed for predictive analytics of a student’s performance and engagement depending upon interactions, contribution in class, etc. In this paper, the Deep Learning-Driven Quantum Inspired Moth Flame Optimizer for Real-Time Student Engagement Analysis (DLQIMFO-RSEA) method is proposed. The DLQIMFO-RSEA method aims to categorise student engagement in online classes. To accomplish this, the DLQIMFO-RSEA method uses the YOLOv5 object detection model with backbones SPPF, CBS, and CSPI-X. Next, the image pre-processing stage employs the Wiener filter (WF) to remove the noise. For feature extraction, the InceptionResNetV2 technique is used. Furthermore, a stacked autoencoder (SAE) is applied for detection. At last, the parameter tuning process is performed by the quantum-inspired moth flame optimiser (QIMFO) model to improve the classification performance of the SAE model. The comparison analysis of the DLQIMFO-RSEA approach showed superior accuracy of 94.34 % compared to other models on the student engagement dataset.

量子计算（QC）利用量子力学原理，如叠加和纠缠来解决特定问题，比经典计算机更有效。电子学习是大流行期间学生和教师的唯一选择。相反，对于教师来说，在线教学时观察每个学生的参与度是一项挑战。学生们在这样的活动中会分心。教师想要识别学生的状态，无论他们是专注于教学还是专注于教学。因此，从教师的角度来看，评估学生的参与水平以了解他们的实际反应并采取必要的步骤让学生参与并帮助他们实现目标是很重要的，机器学习（ML）和深度学习（DL）被用于根据互动、课堂贡献等对学生的表现和参与进行预测分析。本文提出了一种深度学习驱动的量子启发飞蛾火焰优化器实时学生参与分析（DLQIMFO-RSEA）方法。DLQIMFO-RSEA方法旨在对学生在在线课程中的参与度进行分类。为了实现这一点，DLQIMFO-RSEA方法使用YOLOv5目标检测模型，该模型具有骨干SPPF、CBS和CSPI-X。接下来，图像预处理阶段采用维纳滤波器（WF）去除噪声。对于特征提取，使用了InceptionResNetV2技术。此外，采用堆叠自编码器（SAE）进行检测。最后，采用量子启发飞蛾火焰优化器（QIMFO）模型进行参数整定，提高SAE模型的分类性能。DLQIMFO-RSEA方法的对比分析显示，与学生参与数据集上的其他模型相比，DLQIMFO-RSEA方法的准确率为94.34 %。

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引用次数: 0

Multimodal vehicle trajectory prediction at urban uncontrolled intersections considering multiple types of traffic participants and driving risks 考虑多类型交通参与者和驾驶风险的城市非受控交叉口多模式车辆轨迹预测

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-01-01 Epub Date: 2026-01-06 DOI: 10.1016/j.aej.2025.12.059

Xixi Li , Minglun Ren , Hongmeng Xu

The trajectory prediction of the subject vehicle (SV) contributes to the autonomous vehicle’s recognition of potentially risky situations, serving as an essential basis for decision-making and planning. Complex interactions between multiple types of traffic participants at non-signalized intersections increase vehicles’ driving risks, which poses challenges to vehicle trajectory prediction. In this work, a multimodal vehicle trajectory prediction method considering multiple types of traffic participants and driving risks is proposed, which adds the selection of the optimal trajectory and the avoidance of driving risks to multimodal trajectory prediction. Specifically, a Transformer model based on spatio-temporal feature fusion is constructed to extract spatio-temporal interaction features of multiple types of traffic participants and generate candidate multimodal trajectories. An inverse reinforcement learning reward function is designed to evaluate candidate multimodal trajectories and select the optimal trajectory. A risk avoidance module based on the driving risk field is proposed to ensure safe interaction. Experimental results indicate that the model achieves higher trajectory prediction accuracy while fully considering interaction with multiple types of traffic participants. The driving risk measurement results highlight the model’s excellent risk avoidance performance. This work provides an effective new idea for improving the driving safety and efficiency of autonomous vehicles.

主体车辆（SV）的轨迹预测有助于自动驾驶汽车识别潜在的危险情况，是决策和规划的重要依据。在非信号交叉口，多类型交通参与者之间的复杂交互增加了车辆的行驶风险，给车辆轨迹预测带来了挑战。本文提出了一种考虑多类型交通参与者和驾驶风险的多模式车辆轨迹预测方法，将最优轨迹选择和驾驶风险规避纳入多模式轨迹预测。具体而言，构建了基于时空特征融合的Transformer模型，提取多类型交通参与者的时空交互特征，生成候选多模态轨迹。设计了一个逆强化学习奖励函数来评估候选的多模态轨迹并选择最优轨迹。提出了基于驾驶风险场的风险规避模块，以保证安全交互。实验结果表明，该模型在充分考虑多类型交通参与者相互作用的情况下，具有较高的轨迹预测精度。驱动风险度量结果表明，该模型具有良好的风险规避性能。该工作为提高自动驾驶汽车的行驶安全性和效率提供了有效的新思路。

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引用次数: 0