alexandria engineering journal最新文献_第2页

Invasive weed optimization based metaheuristic approach for solving constrained risk budgeted portfolio selection problem 基于入侵杂草优化的约束风险投资组合选择元启发式方法

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-01-01 DOI: 10.1016/j.aej.2025.12.067

Zubair Ashraf , Mohammad Shahid , Lamaan Sami , Faraz Hasan , Mohd Shamim

Portfolio management focuses on investing in the financial sector to achieve the highest return while tolerating the lowest risk. The optimal financial allocation has long been considered one of the essential aspects of risk-adjusted financial sector investment. Therefore, many optimization techniques have been established to maximize the return on risk. This paper presents a novel framework named the risk-budgeted portfolio selection (RBPS) model, which allocates the total risk of a portfolio across different securities by incorporating risk budgeting (RB) levels to ensure the portfolio's risk is diversified while maximizing the Sharpe ratio. To address the proposed RBPS model, an invasive weed optimization (IWO) algorithm-based solution method is suggested, and risk budgeting constraints are accommodated using resilient and flexible repairing procedures. Experiments have been performed using two newly created datasets from the Sensex of the Bombay Stock Exchange and the National Stock Exchange from India. The percentage improvement of the maximum Sharpe ratio obtained by IWO is up to 1.95 % at RB% = 12.5 among its peer's algorithms. Moreover, the experiments have been extended to global benchmark datasets to evaluate the proposed approach. Finally, statistical analysis is conducted to test the significance of improvement in the RBPS model.

投资组合管理的重点是投资于金融部门，以实现最高的回报，同时承受最低的风险。长期以来，最优金融配置一直被认为是风险调整金融部门投资的重要方面之一。因此，建立了许多优化技术来最大化风险回报。本文提出了风险预算投资组合选择（RBPS）模型，该模型通过纳入风险预算（RB）水平来分配投资组合的总风险，以确保投资组合的风险分散，同时最大化夏普比率。针对RBPS模型，提出了一种基于入侵杂草优化（IWO）算法的求解方法，并采用弹性和柔性修复程序来适应风险预算约束。实验使用了两个新创建的数据集，这些数据集来自孟买证券交易所的Sensex和印度国家证券交易所。在RB% = 12.5时，IWO算法获得的最大夏普比在同类算法中提高了1.95 %。此外，实验已经扩展到全球基准数据集来评估所提出的方法。最后进行统计分析，检验RBPS模型改进的显著性。

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引用次数: 0

Data-driven sustainable supply chain management with MEREC-EDAS approach using bipolar fuzzy credibility numbers 采用双极模糊可信数的MEREC-EDAS方法的数据驱动可持续供应链管理

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-01-01 DOI: 10.1016/j.aej.2025.12.053

Muhammad Riaz , Nabiah Mazher , Asia Tahir , Muhammad Aslam , Dragan Pamucar , Vladimir Simic

This paper presents the novel concept of bipolar fuzzy credibility numbers (BFCNs) which is a robust extension of bipolar fuzzy numbers (BFNs). The BFCNs integrate credibility measures to effectively manage bipolarity and vagueness. A new multi-criteria decision making (MCDM) technique is proposed which determine objective weights by evaluating the relevance of the criteria with “method based on the removal effects of criteria” (MEREC). The modified “evaluation based on distance from average solution” (EDAS)” method is applied to rank the feasible alternatives. Einstein operations are used to construct new AOs namely bipolar fuzzy credibility Einstein weighted averaging (BFCEWA) and bipolar fuzzy credibility Einstein weighted geometric (BFCEWG) operators. Furthermore, bipolar fuzzy credibility Einstein ordered weighted averaging (BFCEOWA) and bipolar fuzzy credibility Einstein ordered weighted geometric (BFCEOWG) operators are also developed to prioritize the objects using score function. The MEREC-EDAS approach is proposed for sustainable solution in real-life problems involving bipolarity and vagueness. A real-world case study is conducted to demonstrate the practical application of the MEREC-EDAS approach for evaluating the most effective supply chain management (SCM) strategy in e-commerce.

本文提出了双极模糊可信数的新概念，它是双极模糊数的鲁棒扩展。BFCNs整合了可信度措施，有效管理两极化和模糊性。提出了一种新的多准则决策（MCDM）技术，该技术采用“基于准则去除效果的方法”（MEREC），通过评价准则的相关性来确定客观权重。采用改进的“基于平均解距离的评价”（EDAS）方法对可行方案进行排序。利用爱因斯坦运算构造了双极模糊可信度爱因斯坦加权平均算子和双极模糊可信度爱因斯坦加权几何算子。此外，还提出了双极模糊可信度爱因斯坦有序加权平均算子（BFCEOWA）和双极模糊可信度爱因斯坦有序加权几何算子（BFCEOWG），利用分数函数对目标进行排序。MEREC-EDAS方法是针对现实生活中涉及双极性和模糊性的问题提出的可持续解决方案。一个现实世界的案例研究进行，以证明MEREC-EDAS方法的实际应用，以评估最有效的供应链管理（SCM）战略在电子商务。

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引用次数: 0

Enhancing biometric authentication privacy and security: A synergistic approach using cancelable biometrics and federated learning 增强生物识别身份验证隐私和安全性：使用可取消生物识别和联合学习的协同方法

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-01-01 DOI: 10.1016/j.aej.2025.12.017

Vijay D. Katkar , Riman Mandal , Utpal Biswas , Munindra Lunagaria , Ghanshyam G. Tejani , Seyed Jalaleddin Mousavirad

While biometric authentication offers higher security than other methods, the compromise of biometric data generates major privacy concerns. Combining federated learning for distributed privacy protection with non-invertible transformations and deep learning-based feature extraction, this work proposes a novel cancelable biometric architecture. The approach uses pretrained CNNs – MobileNetV3Small and ResNet50V2 – to extract features from intermediate layers and random projection and kernel PCA are used to generate irreversible biometric templates. Secure model training guaranteed by federated learning protects raw biometric data. Using MobileNetV3Small features from layers -7 and -8, experimental results on three benchmark datasets – AMI (ear), ORL (facial), and IITD (iris) – showcase 100% or near-perfect accuracy for KNN classifiers. Using layer -7 features, the SVM on the AMI dataset attained an F1-score of 0.9665 and an accuracy of 97.8%. The proposed transformation pipeline improves accuracy by 9.16% over baseline approaches without proposed method. These findings confirm that federated learning preserves privacy without compromising recognition efficiency and that mid-level CNN features provide improved discrimination. This work proposes a deployable cancelable biometric solution concurrently addressing accuracy, revocability, and distributed security in modern authentication systems.

虽然生物特征认证比其他方法具有更高的安全性，但生物特征数据的泄露会产生重大的隐私问题。将分布式隐私保护的联邦学习与不可逆变换和基于深度学习的特征提取相结合，提出了一种新的可取消生物识别体系结构。该方法使用预训练的cnn - MobileNetV3Small和ResNet50V2 -从中间层提取特征，并使用随机投影和核主成分分析生成不可逆生物特征模板。由联邦学习保证的安全模型训练保护原始生物特征数据。使用来自第7层和第8层的MobileNetV3Small特征，在三个基准数据集——AMI（耳朵）、ORL（面部）和IITD（虹膜）上的实验结果显示，KNN分类器的准确率达到100%或接近完美。使用-7层特征，SVM在AMI数据集上的f1得分为0.9665，准确率为97.8%。所提出的转换管道比没有提出方法的基线方法提高了9.16%的精度。这些发现证实，联邦学习在不影响识别效率的情况下保护了隐私，并且中级CNN特征提供了改进的识别。这项工作提出了一种可部署的可取消的生物识别解决方案，同时解决了现代身份验证系统中的准确性、可撤销性和分布式安全性。

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引用次数: 0

VoxVeritasNet: A new feature engineering model leveraging iterative feature selection for detecting fake or real speech VoxVeritasNet：一个新的特征工程模型，利用迭代特征选择来检测虚假或真实的语音

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-01-01 DOI: 10.1016/j.aej.2026.01.009

Burak Çelik , Burcu Zeybek , Mahmut Burak Karadeniz , Adem Kocyigit , Onur Arsalı , Ebru Efeoglu , Bahattin Türetken

This study introduces VoxVeritasNet, a high-precision and computationally efficient feature engineering framework for deepfake audio detection. The methodology leverages a nine-level Multi-Level Discrete Wavelet Transform (MDWT) to capture intricate time–frequency artifacts. A key innovation is the quantum-inspired dual-path mapping algorithm, which models parallel signal dependencies and embeds features into a high-dimensional Hilbert space for enhancing geometric separability. To optimize performance, an iterative ensemble selection strategy utilizing Neighborhood Component Analysis (NCA), Chi

^{2}

, and ReliefF is employed alongside Support Vector Machines and k-Nearest Neighbors. The framework was evaluated across three public datasets with varying class distributions, achieving state-of-the-art peak accuracies of 99.96% with db4 and 99.71% with sym8 wavelets. Even using with the computationally efficient sym4 baseline, the model maintained exceptional detection rates above 98.99% and an equal error rate (EER) as low as 0.14%. VoxVeritasNet operates with a processing throughput of 6.45 segments per second on standard CPU hardware with a negligible storage footprint, offering a lightweight and explainable alternative to resource-intensive deep learning architectures.

本研究介绍了VoxVeritasNet，这是一个用于深度假音频检测的高精度和计算效率高的特征工程框架。该方法利用九级多电平离散小波变换（MDWT）来捕获复杂的时频伪像。一个关键的创新是量子启发的双路径映射算法，该算法模拟并行信号依赖并将特征嵌入高维希尔伯特空间以增强几何可分性。为了优化性能，利用邻域成分分析（NCA）、Chi2和ReliefF的迭代集成选择策略与支持向量机和k近邻一起使用。该框架在三个具有不同类别分布的公共数据集上进行了评估，db4和sym8小波的峰值精度分别达到了99.96%和99.71%。即使使用计算效率高的sym4基线，该模型的异常检测率也保持在98.99%以上，相等错误率（EER）低至0.14%。VoxVeritasNet在标准CPU硬件上以每秒6.45段的处理吞吐量运行，存储占用可以忽略不计，为资源密集型深度学习架构提供了轻量级和可解释的替代方案。

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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 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 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 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

Study on unsaturated rainfall-induced slope stability and failure probability based on GA-LSTM-MC 基于GA-LSTM-MC的非饱和降雨边坡稳定性及破坏概率研究

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-01-01 DOI: 10.1016/j.aej.2025.12.057

Tao Deng, Kegang Li, Chengliang Zhang, Xiaoqiang Zhang

As rainfall-induced landslide hazards increase, accurately predicting slope stability becomes essential. This study combines FLAC3D numerical simulations with unsaturated seepage theory to develop a GA-LSTM-MC model for predicting slope displacement, safety factors, and failure probability. K-fold cross-validation was used to enhance model robustness, especially in cases with small sample sizes. The results show that low-intensity, short-duration rainfall (e.g., ≤50 mm) maintains relatively stable safety factors, with small displacements and low failure probabilities. In contrast, heavy or prolonged rainfall (≥100 mm) significantly reduces safety factors, causing a large increase in displacement and a rapid rise in failure probability. Particularly, when rainfall exceeds 150 mm, safety factors drop to critical levels, displacements exceed 50 cm, and failure probability approaches 100 %. The GA-LSTM-MC model performs well under moderate to high-intensity rainfall conditions, accurately predicting dynamic changes in slope displacement and safety factors. Combined with a graphical user interface (GUI), the system allows real-time input and analysis of rainfall parameters, providing an efficient tool for slope risk assessment and early warning. However, under extreme rainfall conditions, displacement predictions show some bias, especially near failure. Future improvements could include optimizing the GUI interface, incorporating field data validation, and considering multi-factor interactions to further enhance the system's practicality and accuracy.

随着降雨引起的滑坡灾害的增加，准确预测边坡稳定性变得至关重要。本研究将FLAC3D数值模拟与非饱和渗流理论相结合，建立了预测边坡位移、安全系数和破坏概率的GA-LSTM-MC模型。使用K-fold交叉验证来增强模型的稳健性，特别是在小样本量的情况下。结果表明：低强度、短持续时间降雨（如≤50 mm）保持相对稳定的安全系数，且位移小，破坏概率低；相比之下，强降雨或长时间降雨（≥100 mm）会显著降低安全系数，导致位移大幅增加，破坏概率迅速上升。特别是当降雨量超过150 mm时，安全系数降至临界水平，位移超过50 cm，破坏概率接近100% %。GA-LSTM-MC模型在中高强度降雨条件下表现良好，能准确预测边坡位移和安全系数的动态变化。该系统配合图形用户界面，可实时输入和分析降雨参数，为斜坡风险评估和预警提供有效工具。然而，在极端降雨条件下，位移预测显示出一些偏差，特别是在接近失败的情况下。未来的改进可能包括优化GUI界面，纳入现场数据验证，并考虑多因素交互，以进一步提高系统的实用性和准确性。

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引用次数: 0

Experimental and theoretical analysis of the bearing mechanism of the novel underground pipe curtain support system 新型地下管幕支护系统承载机理的实验与理论分析

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-01-01 DOI: 10.1016/j.aej.2025.12.054

Qian Bai , Zheng Li , Jiachao Dong , Wen Zhao , Cheng Cheng , Pengjiao Jia

The existing pipe curtain support systems can effectively control the settlement caused by the construction, but there are problems such as high precision requirements for pipe jacking, complicated connection between pipes and high self-weight, which increase the construction difficulty and period. A novel pipe curtain support system was proposed in this paper, which mainly consisted of the cutting steel pipe, connecting steel plate and section steel (named CSP-SS method). First, the construction steps and applicability of this support system were described in detail. Then, four experiments were conducted to examine the failure mode of this support system and the impacts of stiffening ribs, connecting steel plate thickness, and steel pipe cutting height on the bearing performance. Subsequently, the calculation models of the ultimate capacity and bending stiffness of the pipe curtain were established. Finally, the application process of the calculation model in actual engineering was given. Experimental and theoretical analysis demonstrated that installing stiffening ribs increased the ultimate capacity by 5.88 %, while reducing the pipe cutting height significantly enhanced the flexural stiffness by 38.37 %; in contrast, the thickness of the connecting steel plate had a marginal influence (stiffness reduction less than 7 %). The proposed calculation models for ultimate capacity and flexural stiffness showed good agreement with test results, with average errors of 8.34 % and 9.2 %, respectively, and the theoretical predictions were generally conservative, which is conducive to the safety design of the project.

现有管幕支护系统可以有效控制施工引起的沉降，但存在顶管精度要求高、管间连接复杂、自重大等问题，增加了施工难度和工期。本文提出了一种以切割钢管、连接钢板和型钢为主的新型管幕支撑体系（CSP-SS法）。首先，详细介绍了该支撑体系的施工步骤和适用性。然后进行了4项试验，研究了该支撑体系的破坏模式以及加筋肋、连接钢板厚度、钢管切割高度对承载性能的影响。建立了管幕的极限承载力和抗弯刚度计算模型。最后给出了计算模型在实际工程中的应用过程。实验和理论分析表明，加劲肋的安装可使极限承载力提高5.88 %，降低切管高度可使抗弯刚度显著提高38.37 %；相比之下，连接钢板厚度的影响较小（刚度降低小于7 %）。所建立的极限承载力和抗弯刚度计算模型与试验结果吻合较好，平均误差分别为8.34 %和9.2 %，理论预测总体保守，有利于工程的安全设计。

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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 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