IEEE Transactions on Intelligent Transportation Systems最新文献_第10页

Drivable Area Detection Method in Dark Unstructured-Roads Based on CNN Data Fusion With Surface Normal Estimation 基于CNN数据融合和表面法向估计的黑暗非结构化道路可行驶区域检测方法

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-15 DOI: 10.1109/TITS.2025.3557013

Pengyu Xue;Dawei Pi;Hongliang Wang;Yuejun Cheng;Yongjun Yan;Xiaowang Sun;Yibo Liu;Xianhui Wang;Dingge Fan;Xian Li;Yibo Hu

{"title":"Drivable Area Detection Method in Dark Unstructured-Roads Based on CNN Data Fusion With Surface Normal Estimation","authors":"Pengyu Xue;Dawei Pi;Hongliang Wang;Yuejun Cheng;Yongjun Yan;Xiaowang Sun;Yibo Liu;Xianhui Wang;Dingge Fan;Xian Li;Yibo Hu","doi":"10.1109/TITS.2025.3557013","DOIUrl":"https://doi.org/10.1109/TITS.2025.3557013","url":null,"abstract":"Vehicle perception of roads is a critical foundation for intelligent transportation systems. While most existing research on intelligent transportation focuses on structured roads, the identification of drivable areas in complex dark-field environments remains challenging and underexplored. To address this issue, this paper proposes a drivable area detection network based on the fusion of images and point clouds. Specifically, a convolutional neural network (CNN) architecture augmented with a surface normal estimator (SNE) model is proposed. This approach significantly reduces resolution while effectively extracting and fusing features from multiple sensors. The proposed model is trained and validated using the Off-Road Freex Detection (ORFD) dataset, demonstrating its effectiveness and accuracy through comprehensive performance metrics. Finally, real-world vehicle tests are conducted to evaluate the drivable area detection system. These tests involve real-time data collection, image enhancement, and dense upsampling of point clouds. The processed multimodal data are fed into the detection network in real time for synchronous training and learning. The detection and recognition of unstructured drivable areas in a real dark environment perception platform confirm the efficacy of the proposed method.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"8694-8706"},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization of Regenerative Braking Control Strategy for Dual-Motor Electric Vehicles Based on Deep Reinforcement Learning 基于深度强化学习的双电机电动汽车再生制动控制策略优化

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-15 DOI: 10.1109/TITS.2025.3553875

Ziran Peng;Zhenyu He

{"title":"Optimization of Regenerative Braking Control Strategy for Dual-Motor Electric Vehicles Based on Deep Reinforcement Learning","authors":"Ziran Peng;Zhenyu He","doi":"10.1109/TITS.2025.3553875","DOIUrl":"https://doi.org/10.1109/TITS.2025.3553875","url":null,"abstract":"In order to maximize the braking energy recovery of dual motor-driven electric vehicles to guarantee braking safety and comfort, this paper proposes a Deep Reinforcement Learning (DRL) braking energy recovery strategy with an improved prioritized experience replay and exploration mechanism. Firstly, the energy recovery process of the electric vehicle is analyzed, and a mathematical model reflecting the dual-motor braking energy conversion and constraint characteristics is established. Secondly, an energy recovery decision-making framework is established based on the twin delayed deep deterministic policy gradient (TD3) algorithm, and an improved prioritized experience replay policy is designed to address the problem of inefficient empirical sampling in the traditional TD3. Finally, in order to change the imbalance between exploration and utilization that exists in the deterministic policy approach, an exploration policy that is automatically adjusted with the training process is introduced to enhance the algorithm’s decision-making ability in complex environments. The effectiveness of the proposed strategy was verified using a Matlab/Simulink simulation model under China Light Vehicle Test Cycle (CLTC) and World Light Vehicle Test Cycle (WLTC) operating conditions. The results showed that the proposed method exhibits higher efficiency in braking energy recovery when compared to other algorithms, with energy recovery efficiencies of 35.18% and 25.79% under two operating conditions, which both met the braking comfort and safety targets.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"10954-10967"},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

pyrtklib: An Open-Source Package for Tightly Coupled Deep Learning and GNSS Integration for Positioning in Urban Canyons pyrtklib：一个用于城市峡谷定位的紧密耦合深度学习和GNSS集成的开源包

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-15 DOI: 10.1109/TITS.2025.3552691

Runzhi Hu;Penghui Xu;Yihan Zhong;Weisong Wen

{"title":"pyrtklib: An Open-Source Package for Tightly Coupled Deep Learning and GNSS Integration for Positioning in Urban Canyons","authors":"Runzhi Hu;Penghui Xu;Yihan Zhong;Weisong Wen","doi":"10.1109/TITS.2025.3552691","DOIUrl":"https://doi.org/10.1109/TITS.2025.3552691","url":null,"abstract":"Global Navigation Satellite Systems (GNSS) are crucial for intelligent transportation systems (ITS), providing essential positioning capabilities globally. However, in urban canyons, the GNSS performance could significantly degraded due to the blockage of direct GNSS signals. The pseudorange measurements are largely affected and the conventional model of weighting observations is not suitable in urban canyons. This paper addresses these challenges by integrating Artificial Intelligence (AI), specifically deep learning, into GNSS positioning process to enhance positioning accuracy. Traditional methods have primarily focused on pseudorange correction due to the absence of ground truth for weight estimation. In response, we propose an innovative indirect training approach using deep learning to optimize both pseudorange bias and weight estimation, aiming to minimize the positioning errors. To support this integration, we developed pyrtklib, a Python binding for the open-source RTKLIB tool, bridging the gap between traditional GNSS algorithms, typically developed in Fortran or C, and modern Python-based AI frameworks. Comparative analyses demonstrate that our method surpasses established tools like goGPS and RTKLIB in positioning accuracy, marking a significant advancement in the field. The source code of tightly coupled deep learning and GNSS integration, along with pyrtklib, is available on GitHub at <uri>https://github.com/ebhrz/TDL-GNSS</uri> and <uri>https://github.com/IPNL-POLYU/pyrtklib</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"10652-10662"},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vehicle Tracking Using Shape-Dependent Mixture Model With Edge-Concentrated Measurements 基于边缘集中测量的形状相关混合模型的车辆跟踪

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-15 DOI: 10.1109/TITS.2025.3558529

Zheng Wen;Jian Lan;Le Zheng;Tao Zeng

{"title":"Vehicle Tracking Using Shape-Dependent Mixture Model With Edge-Concentrated Measurements","authors":"Zheng Wen;Jian Lan;Le Zheng;Tao Zeng","doi":"10.1109/TITS.2025.3558529","DOIUrl":"https://doi.org/10.1109/TITS.2025.3558529","url":null,"abstract":"For tracking a rectangular vehicle, real-world automotive radar position measurements are distributed not uniformly over the vehicle extension but typically around the edges of the vehicle, i.e., the distribution of measurements is shape-dependent. To describe this phenomenon, a shape-dependent Gaussian mixture measurement model is presented, with each mixture component being used to describe a sub-rectangle region by introducing a shape scaling factor. The shape scaling factor is also shape-dependent and can characterize the measurement spread across the corresponding edge. In this model, parameters and mixture structure are highly shape-dependent, and the rectangular shape prior information is also incorporated. Based on the proposed model, a variational Bayesian approach is derived, which recursively and efficiently estimates the kinematic, shape, shape scaling factors, and orientation states of a vehicle. Additionally, the Doppler velocity measurement can also be integrated into the variational Bayesian framework by introducing a latent variable. This approach can effectively and adaptively describe the complex measurement distribution. From the simulation and real experimental results, the proposed approach has a great improvement in the tracking performance, and the superior performance of the proposed model is more significant in estimating the centroid position compared with the state-of-the-art approaches.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"8337-8352"},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-Supervised Transformer for Trajectory Prediction Using Noise Imputed Past Trajectory 基于噪声的自监督变压器轨迹预测

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-14 DOI: 10.1109/TITS.2025.3550711

Vibha Bharilya;Ashok Arora;Neetesh Kumar

{"title":"Self-Supervised Transformer for Trajectory Prediction Using Noise Imputed Past Trajectory","authors":"Vibha Bharilya;Ashok Arora;Neetesh Kumar","doi":"10.1109/TITS.2025.3550711","DOIUrl":"https://doi.org/10.1109/TITS.2025.3550711","url":null,"abstract":"Trajectory prediction is one of the important components for achieving higher levels of Society of Automotive Engineers (SAE) driving automation, enabling them to navigate through complex driving scenarios and make informed decisions in unexplored roads. Problems such as the varying behaviour of drivers on the road, sensor measurement inaccuracies, and dense and complex environmental circumstances make this task difficult. To address these challenges, a self-supervised transformer (SST) framework is proposed. The noise-imputed trajectory points for road agents are generated. This enhances the model’s ability to handle uncertain data. A self-supervised task is proposed, which focuses on predicting past trajectory points using both true and noise-imputed trajectory encoded features. This approach highlights the important patterns or connections in the data that could go unnoticed if supervised tasks were the only one used. Further, in addition to the trajectory prediction task, a consistent loss function should be introduced to preserve spatial consistency with noise imputed trajectory points. Moreover, learnable query embeddings are added to the system to improve the diversity of multi-modal predictions. The SST model has been evaluated on the widely used and recent Argoverse 2 dataset and outperforms state-of-the-art models by a margin of 2.17%-24.15%, 5.38%-30.05%, 9.41%-46.89% and 0.20%-21.21% on the minADE6, minFDE6, MR6 and b-FDE6 respectively.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"8454-8466"},"PeriodicalIF":7.9,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Secure Consensus for Multi-Agent Systems With Euler-Lagrange Dynamics and Multiple DoS Attacks 具有Euler-Lagrange动态和多重DoS攻击的多智能体系统的安全一致性

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-14 DOI: 10.1109/TITS.2025.3554274

Ruimei Zhang;Liang Liu;Ju H. Park;Deqiang Zeng;Xiangpeng Xie

引用次数: 0

UnMoDE: Uncertainty Modeling for Driver Gaze Estimation via Feature Disentanglement UnMoDE：基于特征解纠缠的驾驶员注视估计的不确定性建模

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-14 DOI: 10.1109/TITS.2025.3556553

Daosong Hu;Mingyue Cui;Kai Huang

{"title":"UnMoDE: Uncertainty Modeling for Driver Gaze Estimation via Feature Disentanglement","authors":"Daosong Hu;Mingyue Cui;Kai Huang","doi":"10.1109/TITS.2025.3556553","DOIUrl":"https://doi.org/10.1109/TITS.2025.3556553","url":null,"abstract":"Gaze estimation can be used for assessing the attention level of drivers. Current works predominantly focus on enhancing model accuracy, often overlooking the influence of input sample and label uncertainty. In this paper, we propose a framework for uncertainty modeling in driver gaze estimation via feature disentanglement, referred to as UnMoDE. Our approach begins by extracting facial information into distinct feature spaces using an asymmetric dual-branch encoder to obtain gaze features. Subsequently, a multi-layer perceptron (MLP) is employed to project gaze features and labels into an embedding space, representing them as Gaussian distributions. The uncertainty is described using a covariance matrix. Random sampling is applied to derive samples from the gaze embedding distribution to estimate the most probable embedding representation. This estimated representation is then used to regress the gaze direction and is projected back into the gaze feature space, along with identity information, to facilitate facial reconstruction. Extensive experimental evaluations demonstrate that UnMoDE significantly outperforms baseline and state-of-the-art methods on the latest benchmark datasets collected for drivers, particularly in reducing the number of samples with significant errors.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"10612-10622"},"PeriodicalIF":7.9,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Tensor Ring Decomposition for Urban Traffic Data Imputation 基于鲁棒张量环分解的城市交通数据输入

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-14 DOI: 10.1109/TITS.2025.3555449

Linfang Yu;Chenyu Guan;Hao Wang;Yuxin He;Wenming Cao;Chi-Sing Leung

{"title":"Robust Tensor Ring Decomposition for Urban Traffic Data Imputation","authors":"Linfang Yu;Chenyu Guan;Hao Wang;Yuxin He;Wenming Cao;Chi-Sing Leung","doi":"10.1109/TITS.2025.3555449","DOIUrl":"https://doi.org/10.1109/TITS.2025.3555449","url":null,"abstract":"In urban transportation systems, missing data and noise contamination are almost inevitable. To address the challenge of imputing traffic data corrupted by noise and outliers in real-world scenarios, this paper proposes a novel algorithm based on spatiotemporal tensor completion. The proposed method transforms observed data into three-dimensional spatiotemporal tensors and utilizes tensor ring decomposition for data completion. Furthermore, spatial and temporal information is incorporated into the model by utilizing the graph Laplacian matrix. To handle outliers, they are treated as unknown parameters, and the <inline-formula> <tex-math>$ell _{0}$ </tex-math></inline-formula>-norm is introduced to ensure their sparsity, thereby achieving the Spatio-Temporal Tensor Completion model with <inline-formula> <tex-math>$ell _{0}$ </tex-math></inline-formula>-norm term (STTC-<inline-formula> <tex-math>$ell _{0}$ </tex-math></inline-formula>). The solution to the model is derived using the alternating optimization framework with the alternating direction method of multipliers. Then, we discuss the convergence of the solution method. To further enhance the efficiency of our proposed method, we combine the unrolling algorithm with our iterative optimization model, creating a lightweight and efficient neural network tailored for tensor completion, called STTC-<inline-formula> <tex-math>$ell _{0}$ </tex-math></inline-formula>-NN. Extensive experiments conducted on real datasets demonstrate the superiority of our proposed method over several state-of-the-art methods across various experimental scenarios. It is worth noting that STTC-<inline-formula> <tex-math>$ell _{0}$ </tex-math></inline-formula>-NN reduces computational time by one to two orders of magnitude compared to existing methods while maintaining or even improving imputation accuracy. The code is available at <uri>https://github.com/TCCofWANG/STTC-L0-and-STTC-L0-NN</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"8707-8719"},"PeriodicalIF":7.9,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Attentive Radiate Graph for Pedestrian Trajectory Prediction in Disconnected Manifolds 非连通流形中行人轨迹预测的注意辐射图

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-10 DOI: 10.1109/TITS.2025.3555390

Peiyuan Zhu;Shengjie Zhao;Hao Deng;Fengxia Han

{"title":"Attentive Radiate Graph for Pedestrian Trajectory Prediction in Disconnected Manifolds","authors":"Peiyuan Zhu;Shengjie Zhao;Hao Deng;Fengxia Han","doi":"10.1109/TITS.2025.3555390","DOIUrl":"https://doi.org/10.1109/TITS.2025.3555390","url":null,"abstract":"Pedestrian trajectory prediction grapples with the demanding feat of modeling complex interactions and learning multimodal distribution to navigate different human-centric environments. Despite superior performance in reducing distance-based metrics, recent works tend to predict out-of-distribution trajectories, as the distribution of forthcoming paths comprises a blend of various manifolds that may be disconnected. These unrealistic trajectories can potentially jeopardize the safety of traffic participants and result in significant damage. To meet these challenges, we propose DMPred, a graph-based generator adversarial network that generates realistic multimodal trajectory predictions by better modeling the social interactions of pedestrians across different scenes in disconnected manifolds. The core of DMPred is an attentive radiate graph sequence constructed by considering the localized influence radiating from pedestrian movements, which is followed by a spatiotemporal extractor that stores and reuses potentially forgotten neighboring pedestrian information to allow for better extraction of complex interactions. Additionally, a collection of generators is utilized for forecasting, which incorporates spectral clustering on trajectories during the prior learning process of multiple generators to help reduce model redundancy and enhance flexibility for various prediction scenarios. Through extensive experiments on multiple real-world and simulation datasets, we demonstrate that DMPred obtains highly competitive results with efficacy in predicting realistic multimodal trajectories.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"7755-7769"},"PeriodicalIF":7.9,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual Cyber-Physical Network-Based Optimization of Cloudlet Formation for the Internet of Vehicles 基于双信息物理网络的车联网云形成优化

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-10 DOI: 10.1109/TITS.2025.3554847

Yun Meng;Fan Liu;Xinyi Liu;Wei Wang;Liang Dai

{"title":"Dual Cyber-Physical Network-Based Optimization of Cloudlet Formation for the Internet of Vehicles","authors":"Yun Meng;Fan Liu;Xinyi Liu;Wei Wang;Liang Dai","doi":"10.1109/TITS.2025.3554847","DOIUrl":"https://doi.org/10.1109/TITS.2025.3554847","url":null,"abstract":"The vehicular cloudlet (VC), capable of leveraging synergies to support emerging cooperative services and task offloading among neighboring vehicles, will be adopted in the Internet of Vehicles (IoV). Compared with traditional clustering methods based on link connectivity, VCs require higher transmission capacity and stability. Three major challenges must be addressed when optimally establishing the VC structure. First, the transmission capacity is affected by inherent stochastic characteristics, including channel fading and interference. Second, the mobility of vehicles introduces instability. Third, a comprehensive optimization model is required to jointly improve the stability and transmission capacity of the established VC. Therefore, this paper proposes a dual cyber-physical network (DCP) model to represent the dynamic physical network and the coupled transmission network. Generalized analytical expressions for channel quality are derived using moment-generating functions based on a Nakagami-m small-scale fading model to handle stochastic characteristics. Furthermore, a DCP association density optimization model is proposed that considers the stability of the physical topology and the transmission capacity of the channel. Symmetric non-negative matrix factorization is used to solve the optimization problem with low complexity. Simulation results confirm that our proposed method achieves higher transmission capacity and stability compared existing link connectivity-based clustering methods.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"7486-7495"},"PeriodicalIF":7.9,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0