IEEE Transactions on Intelligent Transportation Systems最新文献

IEEE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY IEEE 智能交通系统学会

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-11-05 DOI: 10.1109/TITS.2024.3461528

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IEEE Intelligent Transportation Systems Society Information 电气和电子工程师学会智能交通系统协会信息

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-11-05 DOI: 10.1109/TITS.2024.3480817

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IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-11-05 DOI: 10.1109/TITS.2024.3480168

Simona Sacone

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Fine-Grained Satisfaction Analysis of In-Vehicle Infotainment Systems Using Improved Kano Model and Cumulative Prospect Theory 利用改进的卡诺模型和累积前景理论对车载信息娱乐系统进行细粒度满意度分析

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-14 DOI: 10.1109/TITS.2024.3473534

Dianfeng Zhang;Yanfeng Li;Yanlai Li

{"title":"Fine-Grained Satisfaction Analysis of In-Vehicle Infotainment Systems Using Improved Kano Model and Cumulative Prospect Theory","authors":"Dianfeng Zhang;Yanfeng Li;Yanlai Li","doi":"10.1109/TITS.2024.3473534","DOIUrl":"https://doi.org/10.1109/TITS.2024.3473534","url":null,"abstract":"In-vehicle infotainment system (IVIS) plays an increasingly important role in vehicle intelligence. New energy vehicles have sufficient power, and their IVIS is developing in the direction of large size and multiple functions. By using online comments, thirteen attributes of IVIS were extracted through data clustering. Then, the attribute types and their corresponding contributions to the improvement of satisfaction and the reduction of dissatisfaction were determined by structured data transformation, correlation analysis, dual satisfaction analysis, and fine-grained satisfaction analysis using Kano model. Then, satisfaction and dissatisfaction influence indexes (IFIs) were calculated by combining attention, correlation coefficient and sentimental intensity. An improved cumulative prospect theory was then adopted to calculate the overall IFIs of different attributes. Sensitivity analyses of risk aversion and attention tendency were then conducted, and specific satisfaction optimization suggestions were then discussed. The results of the study have a guiding role in the optimization design of IVIS and the effectiveness of marketing promotion. The research methods contribute theoretically to improving the identification accuracy of online comments, coordinating the influence of sentimental tendency, integrating the analysis of attention’s influence and testing the effect of risk attitudes.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15547-15561"},"PeriodicalIF":7.9,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579223","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}

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Heuristic-Based Multi-Agent Deep Reinforcement Learning Approach for Coordinating Connected and Automated Vehicles at Non-Signalized Intersection 基于启发式的多代理深度强化学习方法，用于在非信号灯路口协调互联车辆和自动驾驶车辆

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-11 DOI: 10.1109/TITS.2024.3407760

Zihan Guo;Yan Wu;Lifang Wang;Junzhi Zhang

{"title":"Heuristic-Based Multi-Agent Deep Reinforcement Learning Approach for Coordinating Connected and Automated Vehicles at Non-Signalized Intersection","authors":"Zihan Guo;Yan Wu;Lifang Wang;Junzhi Zhang","doi":"10.1109/TITS.2024.3407760","DOIUrl":"https://doi.org/10.1109/TITS.2024.3407760","url":null,"abstract":"One typical application of connected and automated vehicles (CAVs) is to coordinate multiple CAVs at a non-signalized intersection in mixed traffic, and it may take advantage of multi-agent deep reinforcement learning (MDRL) approaches to improve the overall coordination efficiency. This study proposes a heuristic-based MDRL algorithm (H-QMIX) developed based on a value-based MDRL algorithm, QMIX. This algorithm incorporates a heuristic-based action mask module to guide CAVs efficiently and safely through intersections, composed of a stimulative passing sequence and safety restrictions on CAVs’ action space in the junction area. Compared with other MDRL algorithms (e.g., IPPO, QMIX), the H-QMIX algorithm demonstrates improved training performance in terms of safety and efficiency in two case studies, where the first requires all CAVs to affix their routes, and another allows CAVs to choose random routes. Concerning the model’s generalization ability, the trained models with the maximal episodic return are then transferred to a more practical scenario with a certain vehicle-to-vehicle (V2V) communication delay in a zero-shot manner. The simulation results illustrate that H-QMIX is robust to a certain communication delay. The code for this paper is available at: \u0000<uri>https://github.com/flammingRaven/heuristic_based_qmix</uri>\u0000.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"16235-16248"},"PeriodicalIF":7.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587639","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}

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Differential Image-Based Scalable YOLOv7-Tiny Implementation for Clustered Embedded Systems 用于集群嵌入式系统的基于差分图像的可扩展 YOLOv7-Tiny 实现

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-09 DOI: 10.1109/TITS.2024.3419095

Sunghoon Hong;Daejin Park

{"title":"Differential Image-Based Scalable YOLOv7-Tiny Implementation for Clustered Embedded Systems","authors":"Sunghoon Hong;Daejin Park","doi":"10.1109/TITS.2024.3419095","DOIUrl":"https://doi.org/10.1109/TITS.2024.3419095","url":null,"abstract":"Convolutional neural networks (CNNs) for powerful visual image analysis are gaining popularity in artificial intelligence. The main difference in CNNs compared to other artificial neural networks is that many convolutional layers are added, which improve the performance of visual image analysis by extracting the feature maps required for image classification. However, algorithm optimization is required to run applications that require low-latency in edge compute modules with limited processing resources. In this paper, we propose a novel algorithm optimization method for fast CNNs by using continuous differential images. The main idea is to reduce computation variably by using the differential value of the input in each convolutional layer. Also, the proposed method is compatible with all types of CNNs, and the performance is better when the pixel value difference of continuous images is low. We use the DarkNet framework to evaluate our algorithm using fast convolution and half convolution approaches on a clustered system. As a result, when the input frame rate is 10 fps, FLOPs are reduced by about 4.92 times compared to the original YOLOv7-tiny. By reducing the FLOPs of the convolutional layer, the inference speed increases to about 4.86 FPS, performing 1.57 times faster than the original YOLOv7-tiny. In the case of parallel processing that used two edge compute modules for using half convolution approach, FLOPs reduced more, and the response speed improved. In addition, faster Object detection implementation is possible by additionally expanding up to 7 compute modules in a scalable clustered embedded system as much as the user wants.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"16036-16047"},"PeriodicalIF":7.9,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587500","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}

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Dynamic Spatiotemporal Straight-Flow Network for Efficient Learning and Accurate Forecasting in Traffic 用于高效学习和准确交通预测的动态时空直流网络

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-07 DOI: 10.1109/TITS.2024.3443887

Canyang Guo;Feng-Jang Hwang;Chi-Hua Chen;Ching-Chun Chang;Chin-Chen Chang

{"title":"Dynamic Spatiotemporal Straight-Flow Network for Efficient Learning and Accurate Forecasting in Traffic","authors":"Canyang Guo;Feng-Jang Hwang;Chi-Hua Chen;Ching-Chun Chang;Chin-Chen Chang","doi":"10.1109/TITS.2024.3443887","DOIUrl":"https://doi.org/10.1109/TITS.2024.3443887","url":null,"abstract":"To achieve accurate traffic forecasting, previous research has employed inner and outer aggregation for information aggregation, and attention mechanisms for heterogeneous spatiotemporal dependency learning, which results in inefficient model learning. While learning efficiency is critical due to the need for updating frequently the model to alleviate the impact of concept drift, limited work has focused on improving it. For efficient learning and accurate forecasting, this study proposes the dynamic spatiotemporal straight-flow network (DSTSFN). Breaking the aggregation paradigms employing both inner and outer aggregation, which may be redundant, the DSTSFN designs a straight-flow network that employs bipartite graphs to learn directly the dependencies between the source and target nodes for outer aggregation only. Instead of the attention mechanisms, the dynamic graphs/networks, which outdo static ones by possessing time-varying dependencies, are designed in the DSTSFN to distinguish the dependency heterogeneity, making the model relatively streamlined. Additionally, two learning strategies based on respectively the curriculum and transfer learning are developed to further improve the learning efficiency of the DSTSFN. Our study could be the first work designing the learning strategies for the multi-step traffic predictor based on dynamic spatiotemporal graphs. The learning efficiency and forecasting accuracy are demonstrated by experiments, which show that the DSTSFN can outperform not only the state-of-the-art (SOTA) predictor for accuracy by achieving a 2.27% improvement in accuracy and requiring only 8.98% of the average training time, but also the SOTA predictor for efficiency by achieving a 9.26% improvement in accuracy and requiring 91.68% of the average training time.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"18899-18912"},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579171","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}

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Exploring Diversity-Based Active Learning for 3D Object Detection in Autonomous Driving 探索基于多样性的主动学习，用于自动驾驶中的 3D 物体检测

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-07 DOI: 10.1109/TITS.2024.3463801

Jinpeng Lin;Zhihao Liang;Shengheng Deng;Lile Cai;Tao Jiang;Tianrui Li;Kui Jia;Xun Xu

{"title":"Exploring Diversity-Based Active Learning for 3D Object Detection in Autonomous Driving","authors":"Jinpeng Lin;Zhihao Liang;Shengheng Deng;Lile Cai;Tao Jiang;Tianrui Li;Kui Jia;Xun Xu","doi":"10.1109/TITS.2024.3463801","DOIUrl":"https://doi.org/10.1109/TITS.2024.3463801","url":null,"abstract":"3D object detection has recently received much attention due to its great potential in autonomous vehicle (AV). The success of deep learning based object detectors relies on the availability of large-scale annotated datasets, which is time-consuming and expensive to compile, especially for 3D bounding box annotation. In this work, we investigate diversity-based active learning (AL) as a potential solution to alleviate the annotation burden. Given limited annotation budget, only the most informative frames and objects are automatically selected for human to annotate. Technically, we take the advantage of the multimodal information provided in an AV dataset, and propose a novel acquisition function that enforces spatial and temporal diversity in the selected samples. We benchmark the proposed method against other AL strategies under realistic annotation cost measurements, where the realistic costs for annotating a frame and a 3D bounding box are both taken into consideration. We demonstrate the effectiveness of the proposed method on the nuScenes dataset and show that it outperforms existing AL strategies significantly.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15454-15466"},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579170","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}

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A Heterogeneous Graph Convolution Based Method for Short-Term OD Flow Completion and Prediction in a Metro System 基于异构图卷积的地铁系统短期外径流量完成与预测方法

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-07 DOI: 10.1109/TITS.2024.3467094

Jiexia Ye;Juanjuan Zhao;Furong Zheng;Chengzhong Xu

{"title":"A Heterogeneous Graph Convolution Based Method for Short-Term OD Flow Completion and Prediction in a Metro System","authors":"Jiexia Ye;Juanjuan Zhao;Furong Zheng;Chengzhong Xu","doi":"10.1109/TITS.2024.3467094","DOIUrl":"https://doi.org/10.1109/TITS.2024.3467094","url":null,"abstract":"Short-term OD flow (i.e. the number of passenger traveling between stations) prediction is crucial to traffic management in metro systems. The delayed effect in latest complete OD flow collection and complex spatiotemporal correlations of OD flows in high dimension make it challengeable to predict short-term OD flow. Existing methods need to be improved due to not fully utilizing the real-time passenger mobility data and not sufficiently modeling the implicit correlation of the mobility patterns between stations. In this paper, we propose a Completion based Adaptive Heterogeneous Graph Convolution Spatiotemporal Predictor. The novelty is mainly reflected in two aspects. The first is to model real-time mobility evolution by establishing the implicit correlation between observed OD flows and the prediction target OD flows in high dimension based on a key data-driven insight: the destination distributions of the passengers departing from a station are correlated with other stations sharing similar attributes (e.g. geographical location, region function). The second is to complete the latest incomplete OD flows by estimating the destination distribution of unfinished trips through considering the real-time mobility evolution and the time cost between stations, which is the base of time series prediction and can improve the model’s dynamic adaptability. Extensive experiments on two real world metro datasets demonstrate the superiority of our model over other competitors with the biggest model performance improvement being nearly 4%. In addition, the data complete framework we propose can be integrated into other models to improve their performance up to 2.1%.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15614-15627"},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579180","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}

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IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-04 DOI: 10.1109/TITS.2024.3460988

Simona Sacone

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