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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第2期9卷 3092-3105页

作者： Wang, Fei-Yue Wuhan Inst Ind Innovat & Dev Wuhan 430000 Hubei Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This issue comprises 2 Perspectives, 2 Letters, and 82 Regular Papers. After Scanning the Issue, I would like to initiate a broad discussion about Intelligent Vehicles (IVs), with an emphasis on smart logistics and au... 详细信息

关键词： Simultaneous localization and mapping Intelligent vehicles Logistics

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MSDE-Net: A Multi-Scale Dual-Encoding Network for Surgical Instrument Segmentation

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第7期28卷 4072-4083页

作者： Yang, Lei Gu, Yuge Bian, Guibin Liu, Yanhong Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Minimally invasive surgery, which relies on surgical robots and microscopes, demands precise image segmentation to ensure safe and efficient procedures. Nevertheless, achieving accurate segmentation of surgical instruments remains challenging due to the complexity of the surgical environment. To tackle this issue, this paper introduces a novel multiscale dual-encoding segmentation network, termed MSDE-Net, designed to automatically and precisely segment surgical instruments. The proposed MSDE-Net leverages a dual-branch encoder comprising a convolutional neural network (CNN) branch and a transformer branch to effectively extract both local and global features. Moreover, an attention fusion block (AFB) is introduced to ensure effective information complementarity between the dual-branch encoding paths. Additionally, a multilayer context fusion block (MCF) is proposed to enhance the network's capacity to simultaneously extract global and local features. Finally, to extend the scope of global feature information under larger receptive fields, a multi-receptive field fusion (MRF) block is incorporated. Through comprehensive experimental evaluations on two publicly available datasets for surgical instrument segmentation, the proposed MSDE-Net demonstrates superior performance compared to existing methods.

关键词： Surgical instrument segmentation transformer dual-branch encoder feature fusion

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A Survey of Vehicle Dynamics Modeling Methods for Autonomous Racing: Theoretical Models, Physical/Virtual Platforms, and Perspectives

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第3期9卷 4312-4334页

作者： Zhang, Tantan Sun, Yueshuo Wang, Yazhou Li, Bai Tian, Yonglin Wang, Fei-Yue Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper presents the first survey of vehicle dynamics modeling methods for autonomous racing. Previous surveys have covered dynamics models for standard autonomous vehicles or, alternatively, concentrated on planning and control methods in autonomous racing with vehicle dynamics models briefly mentioned. However, previous surveys overlook the importance of vehicle dynamics under challenging conditions of top speeds and non-steady state driving, which are unique characteristics in autonomous racing. Recognizing the vital role of vehicle dynamics modeling in an autonomous racecar's prediction, planning, and control modules, this survey seeks to ascertain to what degree the nominal full-scale racecar dynamics can be streamlined without sacrificing accuracy for simplicity. Furthermore, this survey provides essential guidance for organizers of virtual autonomous races, helping them choose vehicle dynamics models that meet the required level of precision. This paper begins with a review of previous surveys on vehicle dynamics modeling, highlighting their limitations in the context of autonomous racing. Following this, it investigates the existing dynamics models for autonomous racing vehicles, along with a comprehensive examination of the existing physical/virtual testing platforms. The paper concludes by discussing emerging trends and offering perspectives in the field of vehicle dynamics modeling for autonomous racing, paving the way for groundbreaking research and innovations in autonomous racing.

关键词： Vehicle dynamics Surveys Tires Planning Aerodynamics Heuristic algorithms Computational modeling Autonomous racing chassis vehicle dynamics testing platform intelligent vehicles for education (IV4E)

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A Stochastic Traffic Flow Model-Based Reinforcement Learning Framework For Advanced Traffic Signal control

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systemS 2025年第1期26卷 714-723页

作者： Zhu, Yifan Lv, Yisheng Lin, Shu Xu, Jungang Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 101408 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In addressing the complex challenge of Traffic Signal control (TSC), Deep Reinforcement Learning (DRL) has emerged as a popular solution. In traditional DRL methods applied to TSC problems, deep neural networks are sensitive to minor input changes, which complicates accurate predictions. This ambiguity hampers algorithm convergence, speed, and overall performance. Additionally, existing DRL methods for TSC employ high-dimensional state spaces, escalating computational complexity. This study addresses these challenges by introducing an innovative approach, SLFMLight, that integrates a stochastic traffic flow model with DRL algorithm for TSC. Our method employs an innovative network update algorithm that integrates traffic flow prediction in Q-value learning process to enhance interpretability and accelerate algorithm convergence. Utilizing mode-based multi-actor networks to handle diverse traffic conditions, SLFMLight excels in decision-making towards complex traffic scenarios, especially in congested ones. Concise state definition improves computational efficiency. SLFMLight contributes to the advancement of intelligent traffic management by providing an effective DRL solution that improves interpretability, efficiency, and adaptability in TSC.

关键词： Stochastic processes Prediction algorithms Green products Turning Convergence Vectors Computational modeling Artificial neural networks Signal to noise ratio Markov decision processes Traffic signal control traffic networks deep reinforcement learning

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VIEA: A Visualization system for Industrial Economics Analysis Based on Trade Data

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systemS 2024年第2期11卷 2807-2818页

作者： Wu, Ziliang Liu, Shi Zhou, Yuefan Xu, Tong Pan, Jiacheng Zhou, Zhiguang Chen, Wei Wang, Fei-Yue Zhejiang Univ State Key Lab CAD & CG Hangzhou 310058 Peoples R China Hangzhou Dianzi Univ Sch Media & Design Hangzhou 310018 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

With the acceleration of economic globalization, a large amount of research studies have been conducted for the exploration of industrial economics. In the visualization community, common visualization tools present potential features of industrial economics. They hardly meet the various and complex user requirements for insightful analysis and decision-making. In this article, we design VIEA, a web-based visualization system that integrates a rich set of views and tailored interactions, enabling users to easily perceive economic features, such as geographical distributions, trade relationships, and pattern comparisons. Case studies and user studies based on real-world datasets have been conducted to demonstrate the effectiveness of our system in the exploration of industrial economics.

关键词： Index Terms- Industrial economics international trade visual analysis visualization

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Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation Surveillance

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systemS 2024年第8期25卷 9550-9562页

作者： Qu, Jingxiang Liu, Ryan Wen Gao, Yuan Guo, Yu Zhu, Fenghua Wang, Fei-Yue Wuhan Univ Technol Sch Nav Wuhan 430063 Peoples R China Wuhan Univ Technol State Key Lab Maritime Technol & Safety Wuhan 430063 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Real-time transportation surveillance is an essential part of the intelligent transportation system (ITS). However, images captured under low-light conditions often suffer poor visibility with types of degradation, such as noise interference and vague edge features, etc. With the development of imaging devices, the quality of the visual surveillance data is continually increasing, like 2K and 4K, which have more strict requirements on the efficiency of image processing. To satisfy the requirements on both enhancement quality and computational speed, this paper proposes a double domain guided real-time low-light image enhancement network (DDNet) for ultra-high-definition (UHD) transportation surveillance. Specifically, we design an encoder-decoder structure as the main architecture of the learning network. In particular, the enhancement processing is divided into two subtasks (i.e., color enhancement and gradient enhancement) via the proposed coarse enhancement module (CEM) and LoG-based gradient enhancement module (GEM), which are embedded in the encoder-decoder structure. It enables the network to enhance the color and edge features simultaneously. Through the decomposition and reconstruction on both color and gradient domains, our DDNet can restore the detailed feature information concealed by the darkness with better visual quality and efficiency. The evaluation experiments on standard and transportation-related datasets demonstrate that our DDNet provides superior enhancement quality and efficiency compared with state-of-the-art methods. Besides, the object detection and scene segmentation experiments indicate the practical benefits for higher-level image analysis under low-light environments in ITS. The source code is available at https://***/QuJX/DDNet.

关键词： Transportation Surveillance Image enhancement Real-time systems Image edge detection Laplace equations Feature extraction Intelligent transportation system (ITS) transportation surveillance low-light image enhancement ultra-high-definition (UHD) double domain guidance

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Drive Like a Machine: A Green Reflection of Red Flag Acts for Intelligent Vehicles

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IEEE INTELLIGENT TRANSPORTATION systemS MAGAZINE 2024年第1期16卷 199-202页

作者： Wang, Fei-Yue Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau Peoples R China

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

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Conversational Crowdsensing in the Age of Industry 5.0: A Parallel Intelligence and Large Models Powered Novel Sensing Approach

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systemS 2024年第6期11卷 8046-8063页

作者： Zhu, Zhengqiu Zhao, Yong Qiu, Sihang Xu, Kai Yin, Quanjun Huang, Jincai Liu, Zhong Wang, Fei-Yue Natl Univ Def Technol Coll Syst Engn Changsha Hunan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

The transition from cyber-physical-system-based (CPS-based) Industry 4.0 to cyber-physical-social-system-based (CPSS-based) Industry 5.0 brings new requirements and opportunities to current sensing approaches, especially in light of recent progress in large language models (LLMs) and retrieval augmented generation (RAG). Therefore, the advancement of parallel intelligence powered crowdsensing intelligence (CSI) is witnessed, which is currently advancing toward linguistic intelligence. In this article, we propose a novel sensing paradigm, namely conversational crowdsensing, for Industry 5.0 (especially for social manufacturing). It can alleviate workload and professional requirements of individuals and promote the organization and operation of diverse workforce, thereby facilitating faster response and wider popularization of crowdsensing systems. Specifically, we design the architecture of conversational crowdsensing to effectively organize three types of participants (biological, robotic, and digital) from diverse communities. Through three levels of effective conversation (i.e., interhuman, human-AI, and inter-AI), complex interactions and service functionalities of different workers can be achieved to accomplish various tasks across three sensing phases (i.e., requesting, scheduling, and executing). Moreover, we explore the foundational technologies for realizing conversational crowdsensing, encompassing LLM-based multiagent systems, scenarios engineering and conversational human-AI cooperation. Finally, we present potential applications of conversational crowdsensing and discuss its implications. We envision that conversations in natural language will become the primary communication channel during crowdsensing process, enabling richer information exchange and cooperative problem-solving among humans, robots, and AI.

关键词： Crowdsensing Robot sensing systems Sensors Fifth Industrial Revolution Robots Collaboration Oral communication Computer architecture Aerospace electronics Linguistics Conversational crowdsensing crowdsensing intelligence (CSI) Industry 5.0 large language models (LLMs) parallel intelligence retrieval augmented generation (RAG)

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DBG-Net: A Double-Branch Boundary Guidance Network for Polyp Segmentation

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷

作者： Zhai, Chenxu Yang, Lei Liu, Yanhong Bian, Guibin Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Henan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

High-precision segmentation of polyps is crucial for timely screening and postoperative management of polyps. Although convolutional neural network (CNN)-based methods have made some progress in polyp segmentation research, they still face several bottlenecks in complex pathological environments: 1) insufficient feature extraction capability hinders the identification of polyps under different pathological morphologies and 2) inadequate boundary refinement ability, due to low differentiation between foreground and background, leads to inaccurate edge extraction. To address these issues, this article introduces a double-branch boundary guidance network (DBG-Net). Specifically, a dual-encoding path equipped with a dual-branch adaptive fusion (DAF) module is built to facilitate effective complementary fusion of both global and local features. Subsequently, a boundary guidance mechanism (BGM), consisting of a boundary learning module (BLM) and a boundary embedding module (BEM), is introduced to refine the edges of polyp regions. Additionally, we propose a multipath attention enhancement module (MAEM) that captures rich global context through different paths to enrich feature representation. Finally, a cross-scale feature aggregation decoder (CFAD) is constructed to fully integrate the efficacious information derived from the encoding part, enabling accurate feature reconstruction. To validate the effectiveness of the proposed DBG-Net, a series of experiments were conducted on multiple datasets, from which maximum Dice and mean intersection over union (mIoU) values as high as 93.48% and 93.30% were obtained, effectively substantiating the superior performance of DBG-Net and its potential as a polyp segmentation tool in complex pathological scenarios, thus bringing hope to postoperative management in clinical practice.

关键词： Feature extraction Image segmentation Transformers Accuracy Medical diagnostic imaging Convolutional neural networks Pathology Lesions Image edge detection Decoding Boundary guidance dual-path encoding integration mechanism multiattention mechanism polyp segmentation

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Integrating Relational Knowledge With Text Sequences for Script Event Prediction

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systemS 2024年第7期35卷 9443-9454页

作者： Wang, Zikang Li, Linjing Zeng, Daniel Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Script event prediction aims to infer subsequent events given an incomplete script. It requires a deep understanding of events, and can provide support for a variety of tasks. Existing models rarely consider the relational knowledge between events, they regard scripts as sequences or graphs, which cannot capture the relational information between events and the semantic information of script sequences jointly. To address this issue, we propose a new script form, relational event chain, that combines event chains and relational graphs. We also introduce a new model, relational-transformer, to learn embeddings based on this new script form. In particular, we first extract the relationship between events from an event knowledge graph to formalize scripts as relational event chains, then use the relational-transformer to calculate the likelihood of different candidate events, where the model learns event embeddings that encode both semantic and relational knowledge by combining transformers and graph neural networks (GNNs). Experimental results on both one-step inference and multistep inference tasks show that our model can outperform existing baselines, indicating the validity of encoding relational knowledge into event embeddings. The influence of using different model structures and different types of relational knowledge is analyzed as well.

关键词： Event knowledge graph event prediction graph neural network (GNN) script learning transformer

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