Journal of Intelligent and Connected Vehicles最新文献

Enhancing Driver Emotion Recognition Through Deep Ensemble Classification 通过深度集成分类增强驾驶员情绪识别

Journal of Intelligent and Connected Vehicles Pub Date : 2025-06-01 DOI: 10.26599/JICV.2025.9210055

Faizan Zaman;Zhigang Xu;Adil Hussain;Anees Ullah;Khalid Zaman

{"title":"Enhancing Driver Emotion Recognition Through Deep Ensemble Classification","authors":"Faizan Zaman;Zhigang Xu;Adil Hussain;Anees Ullah;Khalid Zaman","doi":"10.26599/JICV.2025.9210055","DOIUrl":"https://doi.org/10.26599/JICV.2025.9210055","url":null,"abstract":"This research addresses the challenging task of classifying drivers' emotions to increase their awareness of their driving behaviors. It recognizes the common issue of driver emotions, which often leads to the neglect of poor driving practices. By automatically detecting and identifying these behaviors, drivers can proactively obtain valuable insights to reduce potential accidents. This study proposes a comprehensive facial recognition model for drivers that uses a unified architecture comprising a convolutional neural network (CNN), a recurrent neural network (RNN), and a multilayer perceptron (MLP) classification model. Initially, a faster region-based convolutional neural network (R-CNN) was employed for accurate and efficient facial detection of drivers in live and recorded videos. Features are extracted from three CNN models and merged via advanced techniques to create an ensemble classification model. Moreover, the improved Faster R-CNN feature learning module is replaced with a new convolutional neural network module, VGG16, which maximizes the precision and effectiveness of facial detection in our system. Significant accuracy results of 89.2%, 97.20%, 99.01%, 93.65%, and 98.61% are shown in evaluations of our suggested facial detection and facial expression recognition (DFER) datasets, including the EMOTIC, CK+, FERPLUS, AffectNet, and custom datasets. These datasets were meticulously acquired in a simulated environment, necessitating the creation of several custom datasets. This research highlights the potential of deep ensemble classification in improving driver emotion recognition, thereby contributing to enhanced road safety.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"8 2","pages":"9210055-1-9210055-16"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decision-Making of Drivers Following Autonomous Vehicles: Developing a Bayesian Network on the Basis of Field Tests and Questionnaire Data 驾驶人跟随自动驾驶汽车的决策：基于现场测试和问卷调查数据的贝叶斯网络

Journal of Intelligent and Connected Vehicles Pub Date : 2025-06-01 DOI: 10.26599/JICV.2025.9210057

Fang Zong;Huan Wu;Meng Zeng;Won Kim;Qiaowen Bai;Yafeng Gong;Ruifeng Duan;Ying Guo

{"title":"Decision-Making of Drivers Following Autonomous Vehicles: Developing a Bayesian Network on the Basis of Field Tests and Questionnaire Data","authors":"Fang Zong;Huan Wu;Meng Zeng;Won Kim;Qiaowen Bai;Yafeng Gong;Ruifeng Duan;Ying Guo","doi":"10.26599/JICV.2025.9210057","DOIUrl":"https://doi.org/10.26599/JICV.2025.9210057","url":null,"abstract":"With the development of autonomous driving technology, traffic mixed with human-driven vehicles (HDVs) and autonomous vehicles (AVs) has dominated transportation systems for a long period of time. Drivers' car-following decision-making in mixed traffic needs to be considered for traffic simulation and management policy formulation. This study aims to explore the differences in drivers' decision-making mechanisms when following AVs and HDVs. Data from a questionnaire survey and a field test are collected and employed to establish a Bayesian network for car-following decision-making process analysis and inference. The influences of driving habits and recognition of AVs on car-following decisions and the correlations among the four decision variables are analyzed. The four decision variables consist of the vehicle gap and acceleration in both the acceleration and deceleration phases. The results show that there are direct correlations among the four internal decision variables. Among the external variables, overspeeding and honking have distinct impacts on decisions made while following an AV. Moreover, regardless of whether they are in an acceleration or deceleration phase, most drivers tend to make gentler decisions when following AVs than when following HDVs. On the basis of the results, we propose some strategies for the traffic management of mixed traffic that are beneficial to traffic efficiency: (1) Improving drivers' recognition of AVs; (2) embedding the external sensing devices of AVs internally to make them visually similar to HDVs; and (3) establishing dedicated lanes for AVs. The research results have important reference significance for simulating car-following behavior, designing traffic control facilities and formulating policies under mixed traffic scenarios.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"8 2","pages":"9210057-1-9210057-18"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

APM-SLAM: Visual Localization for Fixed Routes with Tightly Coupled a Priori Map APM-SLAM：基于紧耦合先验地图的固定路线视觉定位

Journal of Intelligent and Connected Vehicles Pub Date : 2025-06-01 DOI: 10.26599/JICV.2025.9210063

Linsong Xue;Qi Luo;Kai Zhang

{"title":"APM-SLAM: Visual Localization for Fixed Routes with Tightly Coupled a Priori Map","authors":"Linsong Xue;Qi Luo;Kai Zhang","doi":"10.26599/JICV.2025.9210063","DOIUrl":"https://doi.org/10.26599/JICV.2025.9210063","url":null,"abstract":"Localization along fixed routes is the fundamental function of transportation applications, including patrol vehicles, shuttles, buses, and even passenger vehicles. To achieve accurate and reliable localization, we propose a tightly coupled A Priori Map Simultaneous Localization and Mapping (APM-SLAM) system. APM-SLAM provides a comprehensive and heterogeneous framework, encompassing both mapping and localization processes. The mapping stage leverages Global Navigation Satellite System (GNSS)-aided Structure from Motion (SfM) to establish reliable a priori maps with coarse-and fine-level components. The localization process integrates coarse-to-fine matching with Maximum A Posteriori (MAP) Probability estimation to refine pose accuracy. By incorporating deep learning-based features and point descriptors, our system maintains robustness even in scenarios with significant visual variation. Unlike traditional map-based approaches, APM-SLAM models the a priori map's point structures as probabilistic distributions and incorporates them into the optimization process. Extensive experiments on public datasets demonstrate the superiority of our method in both mapping precision and localization accuracy, achieving decimeter-level translation precision. Ablation studies further validate the effectiveness of each component within our system. This work contributes to establishing maps and utilizing a priori information for localization simultaneously.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"8 2","pages":"9210063-1-9210063-15"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multimodal Large-Language Model Empowering Next-Generation Autonomous Driving Systems 支持下一代自动驾驶系统的多模态大语言模型

Journal of Intelligent and Connected Vehicles Pub Date : 2025-06-01 DOI: 10.26599/JICV.2025.9210059

Zhiqiang Hu;Mingxing Xu;Qixiu Cheng

引用次数: 0

InVDriver: Intra-Instance Aware Vectorized Query-Based Autonomous Driving Transformer InVDriver：基于实例感知的矢量化查询的自动驾驶变压器

Journal of Intelligent and Connected Vehicles Pub Date : 2025-06-01 DOI: 10.26599/JICV.2025.9210060

Bo Zhang;Heye Huang;Chunyang Liu;Yaqin Zhang;Zhenhua Xu

{"title":"InVDriver: Intra-Instance Aware Vectorized Query-Based Autonomous Driving Transformer","authors":"Bo Zhang;Heye Huang;Chunyang Liu;Yaqin Zhang;Zhenhua Xu","doi":"10.26599/JICV.2025.9210060","DOIUrl":"https://doi.org/10.26599/JICV.2025.9210060","url":null,"abstract":"End-to-end autonomous driving, with its holistic optimization capabilities, has gained increasing traction in academia and industry. Vectorized representations, which preserve instance-level topological information while reducing computational overhead, have emerged as promising paradigms. However, existing vectorized query-based frameworks often overlook the inherent spatial correlations among intra-instance points, resulting in geometrically inconsistent outputs (e.g., fragmented HD map elements or oscillatory trajectories). To address these limitations, we propose intra-instance vectorized driving transformer (InVDriver), a novel vectorized query-based system that systematically models intra-instance spatial dependencies through masked self-attention layers, thereby enhancing planning accuracy and trajectory smoothness. Across all core modules, i.e., perception, prediction, and planning, InVDriver incorporates masked self-attention mechanisms that restrict attention to intra-instance point interactions, enabling coordinated refinement of structural elements while suppressing irrelevant inter-instance noise. The experimental results on the nuScenes benchmark demonstrate that InVDriver achieves state-of-the-art performance, surpassing prior methods in both accuracy and safety, while maintaining high computational efficiency.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"8 2","pages":"9210060-1-9210060-8"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Trajectory Planning and Tracking Method Based on Deep Hierarchical Reinforcement Learning 一种基于深度层次强化学习的轨迹规划与跟踪方法

Journal of Intelligent and Connected Vehicles Pub Date : 2025-06-01 DOI: 10.26599/JICV.2025.9210056

Jiajie Zhang;Bao-Lin Ye;Xin Wang;Lingxi Li;Bo Song

{"title":"A Trajectory Planning and Tracking Method Based on Deep Hierarchical Reinforcement Learning","authors":"Jiajie Zhang;Bao-Lin Ye;Xin Wang;Lingxi Li;Bo Song","doi":"10.26599/JICV.2025.9210056","DOIUrl":"https://doi.org/10.26599/JICV.2025.9210056","url":null,"abstract":"To improve the driving efficiency of unmanned vehicles in a complex urban traffic flow environment and the safety and passenger comfort of vehicles when changing lanes, we propose a hierarchical reinforcement learning (HRL)-based vehicle trajectory planning and tracking method. First, we present a hierarchical control framework for vehicle trajectory tracking that is based on deep reinforcement learning (DRL) and model predictive control (MPC). We design an upper-level decision model based on the trust region policy optimization algorithm integrated with long short-term memory to obtain more accurate strategies. Second, to improve stability and passenger comfort, we constructed a lower controller that combines the Bezier curve fitting method and an MPC controller. Finally, the proposed method was simulated via the car learning to act (CARLA) simulator, which is based on an unreal engine. Random urban traffic-flow test scenarios were used to simulate a real urban road-traffic environment. The simulation results illustrate that the proposed method can complete the vehicle trajectory planning and tracking task well. Compared with the existing RL methods, our proposed method has the lowest collision rate of 1.5% and achieves an average speed improvement of 7.04%. Moreover, our proposed method has better comfort performance and lower fuel consumption during the driving process.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"8 2","pages":"9210056-1-9210056-9"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smart Prediction-Planning Algorithm for Connected and Autonomous Vehicle Based on Social Value Orientation 基于社会价值取向的车联网自动驾驶智能预测规划算法

Journal of Intelligent and Connected Vehicles Pub Date : 2025-03-01 DOI: 10.26599/JICV.2024.9210053

Donglei Rong;Yuefeng Wu;Wenjun Du;Chengcheng Yang;Sheng Jin;Min Xu;Fujian Wang

{"title":"Smart Prediction-Planning Algorithm for Connected and Autonomous Vehicle Based on Social Value Orientation","authors":"Donglei Rong;Yuefeng Wu;Wenjun Du;Chengcheng Yang;Sheng Jin;Min Xu;Fujian Wang","doi":"10.26599/JICV.2024.9210053","DOIUrl":"https://doi.org/10.26599/JICV.2024.9210053","url":null,"abstract":"To improve the adaptability of Connected and Automated Vehicles (CAVs) in mixed traffic, this study proposes a prediction model training indicator that comprehensively considers drivers' Social Value Orientation (SVO) and planning goals. Active Influence Factor (AIF) is used as the goal to predict the future safety loss and consistency loss of CAVs. Second, an objective function based on SVO is constructed to understand the driver's characteristics to evaluate the safety, comfort, efficiency, and consistency of candidate trajectories. The results showed that integrating SVO and consistency functions can help ensure that CAVs drive under a more stable risk potential energy field. The prediction planning model that considers SVO can improve the reliability of the CAV output trajectory to a certain extent. The prediction planning under the AIF has better accuracy and stability of the output trajectory; however, it still has strong adaptability and superiority under different sensitivity parameters. The minimum and maximum standard deviations of our model are 0.78 and 0.78 m, respectively, whereas the minimum and maximum standard deviations of the comparative model reach 2.07 and 4.56 m, respectively. The minimum standard deviation of the other comparative model reaches 1.35 m, and the maximum standard deviation reaches 4.45 m.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"8 1","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960599","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Safe and Efficient DRL Driving Policies Using Fuzzy Logic for Urban Lane Changing Scenarios 城市变道场景下基于模糊逻辑的安全高效DRL驾驶策略研究

Journal of Intelligent and Connected Vehicles Pub Date : 2025-03-01 DOI: 10.26599/JICV.2024.9210054

Ling Han;Xiangyu Ma;Yiren Wang;Lei He;Yipeng Li;Lele Zhang;Qiang Yi

{"title":"Safe and Efficient DRL Driving Policies Using Fuzzy Logic for Urban Lane Changing Scenarios","authors":"Ling Han;Xiangyu Ma;Yiren Wang;Lei He;Yipeng Li;Lele Zhang;Qiang Yi","doi":"10.26599/JICV.2024.9210054","DOIUrl":"https://doi.org/10.26599/JICV.2024.9210054","url":null,"abstract":"Lane changing is common in driving. Thus, the possibility of traffic accidents occurring during lane changes is high given the complexity of this process. One of the primary objectives of intelligent driving is to increase a vehicle's behavior, making it more similar to that of a real driver. This study proposes a decision-making framework based on deep reinforcement learning (DRL) in a lane-changing scenario, which seeks to find a driving strategy that simultaneously considers the expected lane-changing risks and gains. First, a fuzzy logic lane-changing controller is designed. It outputs the corresponding safety and lane-change gain weights by inputting relevant driving parameters. Second, the obtained weights are brought into the constructed reward function of DRL. The model parameters are designed and trained on the basis of lane-changing behavior. Finally, we conducted experiments in a simulator to evaluate the performance of our developed algorithm in urban scenarios. To visualize and validate the estimated driving intentions, lane-changing strategies were tested under four scenarios. The results show that the average improvement in travel efficiency in the four scenarios is 19%. In addition, the average accident rate in the four scenarios increased by only 4%. We combine fuzzy logic and DRL reward functions to personify the lane-changing behavior of intelligent driving. Compared with conservative strategies that prioritize only safety, this method can considerably improve the number of lane changes and travel efficiency for autonomous vehicles (AVs) on the premise of ensuring safety. The approach provides an effective and explainable method designed for facilitating intelligent driving lane-changing behavior.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"8 1","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960600","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GRU-LSTM Model Based on the SSA for Short-Term Traffic Flow Prediction 基于SSA的GRU-LSTM模型短期交通流预测

Journal of Intelligent and Connected Vehicles Pub Date : 2025-03-01 DOI: 10.26599/JICV.2024.9210051

Changxi Ma;Xiaoyu Huang;Yongpeng Zhao;Tao Wang;Bo Du

{"title":"GRU-LSTM Model Based on the SSA for Short-Term Traffic Flow Prediction","authors":"Changxi Ma;Xiaoyu Huang;Yongpeng Zhao;Tao Wang;Bo Du","doi":"10.26599/JICV.2024.9210051","DOIUrl":"https://doi.org/10.26599/JICV.2024.9210051","url":null,"abstract":"The transportation department relies on accurate traffic forecasting for effective decision-making. However, determining relevant parameters for existing traffic flow prediction models poses challenges. To address this issue, this study proposes a hybrid model, sparrow search algorithm-gated recurrent unit-long short-term memory (SSA-GRU-LSTM), which leverages the SSA to optimize the GRUs and LSTM networks. The SSA is employed to identify the optimal parameters for the GRU-LSTM model, mitigating their impact on prediction accuracy. This model integrates the predictive efficiency of the GRU, LSTM's capability in temporal data analysis, and the fast convergence and global search attributes of the SSA. Comprehensive experiments are conducted to validate the proposed method via traffic flow datasets, and the results are compared with those of baseline models. The numerical results demonstrate the superior performance of the SSA-GRU-LSTM model. Compared with the baselines, the proposed model results in reductions in the root mean square error (RMSE) of 4.632%–45.206%, the mean absolute error (MAE) of 2.608%–53.327%, the mean absolute percentage error (MAPE) of 1.324%–13.723%, and an increase in <tex>$R^{2}$</tex> of 0.5%–17.5%. Consequently, the SSA-GRU-LSTM model has high prediction accuracy and measurement stability.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"8 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960593","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Long-Term Trajectory Prediction Method Based on Highway Vehicle-Following Behavior Patterns 基于公路车辆跟随行为模式的长期轨迹预测方法

Journal of Intelligent and Connected Vehicles Pub Date : 2025-03-01 DOI: 10.26599/JICV.2024.9210045

Zhichao An;Yimin Wu;Fan Zhang;Dong Zhang;Bolin Gao;Suying Zhang;Guang Zhou;Aoning Jia

{"title":"Long-Term Trajectory Prediction Method Based on Highway Vehicle-Following Behavior Patterns","authors":"Zhichao An;Yimin Wu;Fan Zhang;Dong Zhang;Bolin Gao;Suying Zhang;Guang Zhou;Aoning Jia","doi":"10.26599/JICV.2024.9210045","DOIUrl":"https://doi.org/10.26599/JICV.2024.9210045","url":null,"abstract":"To address existing shortcomings such as short time domains and low interpretability, this study proposes a long-term trajectory prediction model for leading vehicles that considers the impact of traffic flow. Through an analysis of trailing trajectory data from the HighD natural driving dataset, fitting relationships for the following behavior patterns were derived. Building upon the intelligent driver model (IDM), three long-term trajectory prediction models were established: acceleration delta velocity (ADV), space delta velocity intelligent driver model (SDVIDM), and space velocity intelligent driver model (SVIDM). These models were then compared with the IDM model through simulations. The results indicate that when there is one vehicle ahead, under aggressive following conditions, the ADV model outperforms the IDM model, reducing the root mean square errors in acceleration, speed, and position by 79.61%, 91.26%, and 87.82%, respectively. In scenarios with two vehicles ahead and conservative short-distance following, the SDVIDM model exhibits reductions of 83.42%, 92.85%, and 92.25%, while the SVIDM model shows reductions of 82.31%, 92.47%, and 94.02%, respectively, compared to the IDM model.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"8 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960598","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0