检索结果-内蒙古大学图书馆

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第1期8卷 404-412页

作者： Gou, Chao Zhou, Yuchen Xiao, Yao Wang, Xiao Yu, Hui Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Campus Shenzhen 518107 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Portsmouth Sch Creat Technol Portsmouth PO1 2DJ Hants England

As a non-invasive method, vision-based driver monitoring aims to identify risky maneuvers for intelligent vehicles and it has gained an increasing interest over recent years. However, most existing methods tend to design models for specific tasks, such as head pose or gaze estimation, which results in redundant models hampering real time applications. Besides, most driver facial monitoring methods ignore the correlation of different tasks. In this work, we propose a unified framework based on cascade learning for simultaneous facial landmark detection and head pose estimation, as well as simultaneous eye center detection and gaze estimation. In particular, built upon the key idea that facial landmark locations and 3D face model parameters are implicitly correlated, we introduce a cascade regression framework to achieve these two tasks simultaneously. After coarsely extracting the driver's eye region from the detected facial landmarks, we perform cascade regression for simultaneous eye center detection and gaze estimation. Leveraging the power of cascade learning allows our method to alternatively optimize facial landmark detection, head pose estimation, eye center localization, and gaze prediction. The comparison experiments conducted on benchmark datasets of 300-W, GI4E, BU, MPIIGaze, and driving dataset of SHRP2 demonstrate that our proposed method can achieve state-of-the-art performance with robust effectiveness on the real driver monitoring applications.

关键词： Task analysis Intelligent vehicles Three-dimensional displays Predictive models Pose estimation Monitoring Faces Driver monitoring cascade learning head pose estimation gaze estimation facial landmark detection

来源：评论

学校读者我要写书评

暂无评论

Integrated Inspection on PCB Manufacturing in Cyber-Physical-Social systems

引用

IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2023年第4期53卷 2098-2106页

作者： Wang, Yutong Wang, Jiangong Cao, Yansong Li, Shixing Kwan, Oliver Chinese Acad Sci Inst Automat State Key Lab Management & Controlof Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Dept Engn Sci Macau Peoples R China North Automat Control Technol Inst Taiyuan Peoples R China Mot G Inc Singapore 068898 Singapore

The printed circuit boards (PCBs) industry is one of the fastest-growing industries in recent decades. The PCB manufacturing process is highly complicated and severely affected by social factors, which makes it very important to conduct integrated inspection, assuring and improving the production quality. In this article, we propose an artificial systems, computational experiments, and parallel execution-based integrated inspection method in cyber-physical-social systems (CPSS) to realize smart manufacturing. In this inspection system, rather than simply performing modeling, analysis, and control, we perform descriptive intelligence to construct production processes with limited multimodal information, perform predictive intelligence to conduct defect detection and defect prediction, and perform prescriptive intelligence to achieve defect diagnosis and defect management. In this way, our inspection system could offer a learning and training platform for workers to master professional inspection skills, provide an experimentation and evaluation platform for product defect monitoring and early warnings, and supply guidance about defect management and control to improve manufacturing processes. For technical implementation, we leverage a Transformer-based foundation model to achieve knowledge reasoning and human-computer interaction. As a result, we provide an innovative solution to cope with the challenges of quality inspection in current smart manufacturing, and expect its further applications in the PCB industry.

关键词： Artificial systems computational experiments and parallel execution (ACP) method cyber-physical-social systems (CPSS) foundation models (FMs) integrated inspection

来源：评论

学校读者我要写书评

暂无评论

Vehicle dynamic dispatching using curriculum-driven reinforcement learning

引用

MECHANICAL systemS AND SIGNAL PROCESSING 2023年 204卷

作者： Zhang, Xiaotong Xiong, Gang Ai, Yunfeng Liu, Kunhua Chen, Long Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Qingdao Univ Technol Sch Mech & Automot Engn Qingdao 266520 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Waytous Inc Qingdao 266109 Peoples R China

This study focuses on optimizing resource allocation problems in complex dynamic environ-ments, specifically vehicle dispatching in closed bipartite queuing networks. We present a novel curriculum-driven reinforcement learning (RL) approach that seamlessly incorporates domain knowledge and environmental feedback, effectively addressing the challenges associated with sparse reward scenarios in RL applications. This approach involves a scalable reinforcement learning framework for dynamic vehicle fleet size. We design dense artificial rewards using domain knowledge and incorporate artificial action-reward pairs into the original experience sequence forming the basic structure of the training instances. A difficulty momentum boosting strategy is proposed to produce a series of training instances with progressively increasing difficulty, ensuring that the RL agent learns decision strategies in an organized and smooth manner. Experimental results demonstrate that the proposed method significantly surpasses existing approaches in enhancing productivity and model learning efficiency for transport tasks in open-pit mines, while confirming the superiority of a flexible and automated curriculum learning process over a rigid setting. This approach has vast potential for application in dynamic resource allocation problems across industries, such as manufacturing and logistics.

关键词： Reinforcement learning Curriculum learning Vehicle dispatching Network optimization

来源：评论

学校读者我要写书评

暂无评论

Cognitive-Based Crack Detection for Road Maintenance: An Integrated system in Cyber-Physical-Social systems

引用

IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2023年第6期53卷 3485-3500页

作者： Fan, Lili Cao, Dongpu Zeng, Changxian Li, Bai Li, Yunjie Wang, Fei-Yue Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 116600 Peoples R China Dalian Nationalities Univ Sch Sci Dalian 116600 Peoples R China Hunan Univ Coll Mech & Vehicle Engn State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Effective road maintenance can not only achieve a balance between limited resources and long-term high-efficiency performance of road but also reduce the loss of life and property caused by road damage to vehicles and pedestrians. Due to the lack of a multidimensional dynamic monitoring system and enough extremely special data, the existing road maintenance system cannot accurately assess the road surface condition and provide timely early warning of sudden road damage. In this article, the M-RM system is proposed, that is, a metaverse-enabled road maintenance system based on cyber-physical-social systems (CPSSs), which fully utilizes the social and artificial system information of CPSS, as well as the simulation, monitoring, diagnosis and prediction functions of road systems in the virtual world of the metaverse. Then, in the road damage detection of system model in the virtual world, for the virtual data of the core assets of the metaverse, we propose an adaptive and information-preserving data augmentation (AIDA) algorithm-based nonclassical receptive field suppression and enhancement, an algorithm developed from human visual cognition. This algorithm enables the generation of a large amount of scarce fidelity data and avoids the introduced noise from impairing the performance of nonaugmented data. Finally, a crack detection algorithm named pay attention twice (PAT) is proposed, which uses the generated virtual data for training, and achieves secondary attention to high-frequency targets by fusing frequency-division convolution and mixed-domain attention mechanism. The detection performance of small targets in uncertain environments is enhanced. The metaverse system built in the current research can not only be used for road maintenance but also empower the traffic metaverse by using the traffic flow prediction module embedded in the algorithm. Experimental results demonstrate that the proposed algorithm can be applied to the road damage detection task under diff

关键词： Roads Maintenance engineering Metaverse Visualization Real-time systems Monitoring Safety Brain inspired crack detection data augmentation metaverse road maintenance visual cognition

来源：评论

学校读者我要写书评

暂无评论

How Simulation Helps Autonomous Driving: A Survey of Sim2real, Digital Twins, and Parallel Intelligence

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

引用

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 593-612页

作者： Hu, Xuemin Li, Shen Huang, Tingyu Tang, Bo Huai, Rouxing Chen, Long Hubei Univ Sch Artificial Intelligence Wuhan 430062 Peoples R China Hubei Univ Minist Educ Key Lab Intelligent Sensing Syst & Secur Wuhan 430062 Peoples R China Worcester Polytechn Inst Dept Elect & Comp Engn Worcester MA 01609 USA Beijing Huairou Acad Parallel Sensing Beijing 101499 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100864 Peoples R China Waytous Inc Beijing 100083 Peoples R China

Developing autonomous driving technologies necessitates addressing safety and cost concerns. Both academic research and commercial applications of autonomous driving vehicles require extensive simulation and real-world testing. The challenge lies in effectively transferring driving knowledge from the virtual simulation world to the reality world, known as the reality gap (RG). This gap arises due to differences in lighting, textures, vehicle dynamics, and agents' behaviors between the two environments. To address this issue, researchers have explored three main approaches: sim2real, digital twins (DTs), and parallel intelligence (PI) technologies. This article reviews these solutions and examines their applications and innovations in autonomous driving. Furthermore, we delve into the state-of-the-art algorithms, models, and involved simulators, and discuss the developmental process from sim2real to DTs and PI. The presentation also sheds light on the challenges and future perspectives in the development of sim2real, DTs, and PI in the field of autonomous driving.

关键词： Autonomous vehicles Digital twins Task analysis Metalearning Virtual reality Vehicle dynamics Solid modeling Autonomous driving sim2real digital twins parallel intelligence reality gap

来源：评论

学校读者我要写书评

暂无评论

ChatGPT-Based Scenario Engineer: A New Framework on Scenario Generation for Trajectory Prediction

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

引用

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第3期9卷 4422-4431页

作者： Li, Xuan Liu, Enlu Shen, Tianyu Huang, Jun Wang, Fei-Yue Peng Cheng Lab Shenzhen 518000 Peoples R China Changsha Univ Sci & Technol Sch Elect & Informat Engn Changsha 410001 Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Macau Univ Sci & Technol Macau 999078 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

The latest developments in parallel driving foreshadow the possibility of delivering intelligence across organizations using foundation models. As is well-known, there are limitations in scenario acquisition in the field of intelligent vehicles (IV), such as efficiency, diversity, and complexity, which hinder in-depth research of vehicle intelligence. To address this issue, this manuscript draws inspiration from scenarios engineering, parallel driving and introduces a pioneering framework for scenario generation, leveraging the ChatGPT, denoted as SeGPT. Within this framework, we define a trajectory scenario and design prompts engineering to generate complex and challenging scenarios. Furthermore, SeGPT, in combination with "Three Modes", foundation models, vehicle operating system, and other advanced infrastructure, holds the potential to achieve higher levels of autonomous driving. Experimental outcomes substantiate SeGPT's adeptness in producing a spectrum of varied scenarios, underscoring its potential to augment the development of trajectory prediction algorithms. These findings mark significant progress in the domain of scenario generation, also pointing towards new directions in the research of vehicle intelligence and scenarios engineering.

关键词： Parallel driving scenarios engineering foundation model vehicle operating system generative pre-trained transformer trajectory prediction

来源：评论

学校读者我要写书评

暂无评论

Metaverse-Enabled Intelligence for Open-Terrain Field Vehicle Fleets: Leveraging Parallel Intelligence and Edge Computing

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

引用

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第2期9卷 3111-3116页

作者： Shuai, Zhibin Hu, Zheng Gai, Jiangtao Chen, Yijie Chen, Jicheng Zhang, Hui Wang, Fei-Yue China North Vehicle Res Inst Beijing 100072 Peoples R China Chinese Scholar Tree Ridge Lab Beijing 100072 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong 999077 Peoples R China Beihang Univ Sch Transportat Sci Engn Beijing 100091 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Open-terrain field vehicle (OTFV) fleets, including mining trucks, construction machinery, and agricultural machinery, often encounter significantly more intricate scenarios and unique challenges than road vehicles. Enhancing the intelligence level of OTFV fleets can significantly enhance their operational effectiveness and improve energy efficiency. This perspective paper introduces a metaverse-enabled framework to improve the intelligence levels of OTFV fleets. The metaverse-enabled framework consists of the parallel intelligence-based vehicle fleet control and edge computing-based vehicle dynamics control levels. We first delve into the framework's specifics, covering open-terrain field metaverse, parallel intelligence, edge computing, and human-vehicle cooperation. We further discuss critical issues such as artificial general intelligence (AGI) enabled large control models, vehicle teleoperation, communication privacy, and edge scenario engineering. Additionally, we provide a detailed account of edge computing and integrated domain control within the vehicle dynamics control level, illustrating the interactions among human drivers, domain controllers, vehicle systems and open-terrain field metaverse. Ultimately, the proposed framework can potentially empower intelligence to OTFV fleets and other equipment clusters with complicated system compositions and challenging missions in complex environments.

关键词： Edge computing metaverse open-terrain field vehicle parallel intelligence vehicle fleet

来源：评论

学校读者我要写书评

暂无评论

A Self-Attention-Based Deep Reinforcement Learning Approach for AGV Dispatching systems

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systemS 2024年第6期35卷 7911-7922页

作者： Wei, Qinglai Yan, Yutian Zhang, Jie Xiao, Jun Wang, Cong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China Hong Kong Appl Sci & Technol Res Inst Co Ltd Hong Kong Peoples R China

The automated guided vehicle (AGV) dispatching problem is to develop a rule to assign transportation tasks to certain vehicles. This article proposes a new deep reinforcement learning approach with a self-attention mechanism to dynamically dispatch the tasks to AGV. The AGV dispatching system is modeled as a less complicated Markov decision process (MDP) using vehicle-initiated rules to dispatch a workcenter to an idle AGV. In order to deal with the highly dynamical environment, the self-attention mechanism is introduced to calculate the importance of different information. The invalid action masking technique is performed to alleviate false actions. A multimodal structure is employed to mix the features of various sources. Comparative experiments are performed to show the effectiveness of the proposed method. The properties of the learned policies are also investigated under different environment settings. It is discovered that the policies explore and learn the properties of different systems, and also smooth the traffic congestion. Under certain environment settings, the policy converges to a heuristic rule that assigns the idle AGV to the workcenter with the shortest queue length, which shows the adaptiveness of the proposed method.

关键词： Automated guided vehicle (AGV) dispatching deep learning reinforcement learning (RL) self-attention

来源：评论

学校读者我要写书评

暂无评论

ClusterST: Clustering Spatial-Temporal Network for Traffic Forecasting

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systemS 2023年第1期24卷 706-717页

作者： Luo, Guiyang Zhang, Hui Yuan, Quan Li, Jinglin Wang, Fei-Yue Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100876 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Innovat Ctr Parallel Vis Qingdao 266000 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa Macao Peoples R China

Traffic forecasting aims to capture complex spatial-temporal dependencies and non-linear dynamics, which plays an indispensable role in intelligent transportation systems and other domains like neuroscience, climate, etc. Most recent works rely on graph convolutional networks (GCN) to model the dependencies and the dynamics. However, the over-smoothing issue of GCN would produce indistinguishable features among nodes, leading to poor expressivity and weak capability of modeling complex dependencies and dynamics. To address this issue, we present a novel clustering spatial-temporal (ClusterST) unit, which incorporates unsupervised learning into GCN for extracting discriminative features. Specifically, we first exploit a neural network to learn a dynamic clustering, i.e., learning to partition the neighbors of each node into clusters at each time step. Two probabilistic losses are proposed to improve the separability of clusters. Then, the extracted features of different clusters can be distinguished. Based on the dynamically formed clusters, a vanilla GCN is applied to aggregate features within each cluster. By purely exploiting such a ClusterST unit, large improvements over the state-of-the-art are achieved. Furthermore, ClusterST units with a different number of clusters can be regarded as basic components to construct an inception-like ClusterST network for going deeper. We evaluate the framework on two real-world large-scale traffic datasets and observe an average improvement of $18.19\%$ and $7.62\%$ over state-of-the-art baselines, respectively. The code and models will be publicly available.

关键词： Index Terms- Traffic forecasting graph convolutional network spatial-temporal networks over-smoothing

来源：评论

学校读者我要写书评

暂无评论

ACP-Incorporated Perturbation-Resistant Neural Dynamics controller for Autonomous Vehicles

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

引用

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第4期9卷 4675-4686页

作者： Liufu, Ying Jin, Long Shang, Mingsheng Wang, Xingxia Wang, Fei-Yue Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing Key Lab Big Data & Intelligent Comp Chongqing 400714 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100098 Peoples R China

Autonomous vehicle control systems are unavoidably influenced by diverse noise perturbations from the unpredictable external environment and internal system. In this consideration, based on the model predictive control (MPC) strategy, a perturbation-resistant neural dynamics (PRND) controller equipped with the noise-suppression ability for the path-tracking control of autonomous vehicles is newly designed in this paper, under the framework of artificial systems, computational experiments, and parallel execution (ACP). In addition, theoretical analyses show that the proposed ACP-incorporated PRND controller can behave with exponential convergence and strong robustness under different noise scenarios. Lastly, computational experiments are conducted and parallelly executed on the CarSim-Simulink platform and E-Car physical platform to demonstrate the effectiveness and superiority of the proposed controller. Overall, this paper provides a new perspective for designing neural-dynamics-based controllers for autonomous vehicles, thereby guaranteeing reliable control performance and effectively resisting noise perturbations.

关键词： Autonomous vehicles Vehicle dynamics Kinematics Task analysis control systems Robustness Predictive control Model predictive control (MPC) neural dynamics autonomous vehicles artificial systems computational experiments and parallel execution (ACP)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：