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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第2期9卷 3220-3231页

作者： Wang, Xiao Tang, Ke Dai, Xingyuan Xu, Jintao Du, Quancheng Ai, Rui Wang, Yuxiao Gu, Weihao Anhui Univ Engn Res Ctr Autonomous Unmanned Syst Technol Minist Educ Hefei 230031 Peoples R China Haomo Technol Co Ltd Beijing 100192 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266114 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning ((STP)-T-4) framework. Specifically, (STP)-T-4 integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV's planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. (STP)-T-4 employs an optimization-based approach for motion planning, utilizing the predicted HDVs' trajectories as input. With the integration of SADRF, (STP)-T-4 executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed (STP)-T-4 in terms of safety and rationality. (STP)-T-4 achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%.

关键词： Autonomous driving social interactive traffic scenarios trajectory planning intention prediction Social-Aware Driving Risk Field

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Dynamic Testing for Autonomous Vehicles Using Random Quasi Monte Carlo

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第3期9卷 4480-4492页

作者： Ge, Jingwei Zhang, Jiawei Chang, Cheng Zhang, Yi Yao, Danya Tian, Yonglin Li, Li Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRist Dept Automat Beijing 100084 Peoples R China Tsinghua Berkeley Shenzhen Inst TBSI Shenzhen 518055 Peoples R China Jiangsu Prov Collaborat Innovat Ctr Modern Urban T Nanjing 210096 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Tsinghua Univ Dept Automat BNRIST Beijing 100084 Peoples R China

The substantial resource usage required to create ample scenarios for testing Autonomous Vehicles (AV) presents a bottleneck in their implementation. At present, research relies on sampling the driving behaviour of Surrounding Vehicles (SV) based on naturalistic datasets in simulation. However, these methods still generate huge amounts of scenarios, making it impossible to synthetically evaluate AV intelligence in a very small number of tests (especially in real-world situations). Simultaneously, the unknown distribution of critical scenarios leads to the problem that more critical scenarios cannot be accurately sampled. In this article, a novel optimization problem is described and a dynamic scenario sampling method is proposed to cover more critical scenarios with finite samples. First, the sampling space is constructed by extracting the behavioural model parameters of the SVs. Second, multiple rounds of sampling are carried out successively to learn the distribution of critical scenarios, which in turn gradually improves the coverage of the critical scenarios. To do this, in each round, we divide the sampling space into several subspaces using two-step sampling, sample the scenarios using Random Quasi Monte Carlo (RQMC), evaluate the criticality of the subspace, and then use the evaluation results to guide the selection of the sampling space for the next round. The purpose of RQMC is to uniformly sample in the critical subspace rather than Standard Monte Carlo (SMC). Experimental results show that our method can better narrow the gap with the distribution of critical scenarios and discover more critical scenarios when compared to the baseline method.

关键词： Autonomous vehicles Automation intelligence testing multi-rounds testing random quasi Monte Carlo

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Correlation analysis and text classification of chemical accident cases based on word embedding

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2022年第0期158卷 698-710页

作者： Jing, Sifeng Liu, Xiwei Gong, Xiaoyan Tang, Ying Xiong, Gang Liu, Sheng Xiang, Shuguang Bi, Rongshan Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Univ Sci & Technol Coll Chem Engn Qingdao 266042 Peoples R China Qingdao Acad Intelligent Ind Inst Smart Educ Syst Qingdao 266044 Peoples R China Rowan Univ Dept Elect & Comp Engn Glassboro NJ 08028 USA

Accident precursors can provide valuable clues for risk assessment and risk warning. Trends such as the main characteristics, common causes, and high-frequency types of chemical accidents can provide references for formulating safety-management strategies. However, such information is usually documented in unstructured or semistructured free text related to chemical accident cases, and it can be costly to manually extract the information. Recently, text-mining methods based on deep learning have been shown to be very effective. This study, therefore, developed a text-mining method for chemical accident cases based on word embedding and deep learning. First, the word2vec model was used to obtain word vectors from a text corpus of chemical accident cases. Then, a bidirectional long short-term memory (LSTM) model with an attention mechanism was constructed to classify the types and causes of Chinese chemical accident cases. The case studies revealed the following results: 1) Common trends in chemical accidents (e.g., characteristics, causes, high-frequency types) could be obtained through correlation analysis based on word embedding;2) The developed text-classification model could classify different types of accidents as fires, explosions, poisoning, and others, and the average p (73.1%) and r (72.5%) of the model achieved ideal performance for Chinese text classification;3) The developed text-classification model could classify the causes of accidents as personal unsafe act, personal habitual behavior, unsafe conditions of equipment or materials and vulnerabilities management strategy;p and r were 63.6% for the causes of vulnerabilities management strategy, and the average p and r are both 60.7%;4) the accident precursors of explosion, fire, and poisoning were obtained through correlation analyses of each high-frequency type of chemical accident case based on text classification;5) the text-mining method can provide site managers with an efficient tool for extracting

关键词： Text mining Correlation analysis Text classification Word embedding Deep learning Chemical accident cases

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RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts

arXiv

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arXiv 2024年

作者： Yang, Jing Wang, Xiao Zhao, Yu Liu, Yuhang Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The School of Artificial Intelligence Anhui University Hefei266114 China Qingdao Academy of Intelligent Industries Qingdao230031 China National Key Laboratory of Information Systems Engineering National University of Defense Technology China Macau University of Science and Technology 999078 China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing100098 China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing100190 China

Crowdsourcing is a critical technology in social manufacturing, which leverages an extensive and boundless reservoir of human resources to handle a wide array of complex tasks. The successful execution of these complex tasks relies on task decomposition (TD) and allocation, with the former being a prerequisite for the latter. Recently, pre-trained language models (PLMs)based methods have garnered significant attention. However, they are constrained to handling straightforward common-sense tasks due to their inherent restrictions involving limited and difficult-to-update knowledge as well as the presence of "hallucinations". To address these issues, we propose a retrieval-augmented generation-based crowdsourcing framework that reimagines TD as event detection from the perspective of natural language understanding. However, the existing detection methods fail to distinguish differences between event types and always depend on heuristic rules and external semantic analyzing tools. Therefore, we present a Prompt-Based Contrastive learning framework for TD (PBCT), which incorporates a prompt-based trigger detector to overcome dependence. Additionally, trigger-attentive sentinel and masked contrastive learning are introduced to provide varying attention to trigger and contextual features according to different event types. Experiment results demonstrate the competitiveness of our method in both supervised and zero-shot detection. A case study on printed circuit board manufacturing is showcased to validate its adaptability to unknown professional domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Evaluation and Spatial-Temporal Difference Analysis of Urban Water Resource Utilization Efficiency Based on Two-Stage DEA Model

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2022年第5期9卷 1282-1296页

作者： Xie, Qiwei Ma, Hewen Zheng, Xiaolong Wang, Xiao Wang, Fei-Yue Beijing Univ Technol Res Base Beijing Modern Mfg Dev Beijing 100124 Peoples R China Hubei Univ Fac Math & Stat Wuhan 430062 Peoples R China 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 100190 Peoples R China

In the present study, a two-stage data envelopment analysis (DEA) model and spatial econometric method were employed to evaluate and analyze the utilization efficiency of urban water resources and spatial-temporal differences in cities of China. The traditional DEA model was enhanced by adopting the Shannon entropy in the first stage. After selecting variables based on the previous step and the Bayes information criterion (BIC), redundant variables were removed. In the meanwhile, a comprehensive efficiency score (CES) was generated to rank the efficiency. Finally, spatial econometric analysis was applied to explore the spatial-temporal differences of urban water resource utilization efficiency. Results demonstrate that: 1) according to the calculations and analysis, communities should concentrate on increasing investment in equipment and technology that can help enhance water consumption efficiency, while overlooking some minor aspects, such as per capita gross domestic product (PCGDP);2) most cities have poor water resource utilization efficiency (low CES). However, both input and output have much room for the improvement;3) Lhasa, Beijing, Haikou, and Shanghai have high CES, indicating that the utilization efficiency of water resources is not entirely dependent on economic development;and 4) through performing the Lagrange multiplier (LM) test, the spatial error model (SEM) test is passed at the significant level of 5%. Moreover, the water resource utilization efficiency of a city may be enhanced with the economic development in neighboring cities.

关键词： Water resources Biological system modeling Urban areas Economics Econometrics Analytical models Water pollution Bayes information criterion (BIC) cities data envelopment analysis (DEA) Shannon entropy spatial econometric utilization efficiency of urban water resources

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LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGVs and USVs Based on Foundation Models and Scenarios Engineering

LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGV...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Yonglin Tian Fei Lin Xinyuan Zhang Jingwei Ge Yutong Wang Xingyuan Dai Yisheng Lv Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems and State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Macau China University of Chinese Academy of Sciences Beijing China Department of Automation Tsinghua University Beijing China

ISBN: (数字)9798350379167

ISBN: (纸本)9798350379174

Intelligent logistics with autonomous vehicles is promising to improve the efficiency of rapidly expanding delivery systems. However, complex environments, diverse packages, and irregular human interaction have imposed challenges to the application of unmanned delivery vehicles. Combining various forms of vehicles and developing knowledge-driven delivery services have been the key to further facilitating the upgrading of logistics systems. In this paper, we propose an integrated framework named LogisticsVISTA (Logistics on Vehicles with Intelligent systems for Transport Automation) comprised of UAVs (Unmanned Aerial Vehicles), UGVs (Unmanned Ground Vehicles), and USVs (Unmanned Surface Vehicles) to provide timely and flexible 3D delivery services. In LogisticsVISTA, we design Large language models (LLMs) and large multimodal models (LMMs) for task planning, active perception, autonomous decision, and human-machine interaction. Besides, virtual-real integrated parallel scenarios are constructed to provide the playing ground for embodied agents efficiently and flexibly.

关键词： Human computer interaction Training Three-dimensional displays Foundation models Transportation Transforms Real-time systems Resource management Logistics Testing

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An Edge Computing Architecture Based on Unikernel

An Edge Computing Architecture Based on Unikernel

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2022 Australian and New Zealand control Conference

作者： Chen, Shichao Xu, Ruijie Sun, Wenqiao Chinese Acad Sci State Key Lab Management & Control Complex Syst I Ctr Natl Railway Intelligent Transportat Syst Eng China Acad Railway Sci Corp Ltd Beijing Peoples R China Chinese Acad Sci Beijing Univ Chem Technol State Key Lab Management & Control Complex Syst I Coll Informat Sci & Technol Beijing Peoples R China China Acad Railway Sci Corp Ltd Transportat & Econ Res Inst Beijing Peoples R China China Acad Railway Sci Corp Ltd Ctr Natl Railway Intelligent Transportat Syst Eng Beijing Peoples R China

ISBN: (数字)9781665498876

ISBN: (纸本)9781665498876

In the era of big data, more and more applications of smart devices are computing-intensive, thus raising the strong demand for task offloading to cloud data centers. However, it gives rise to network delay and privacy data leak issues. Edge computing can effectively solve latency, bandwidth occupation and data privacy problems, but the deployment of applications are also limited by hardware architectures and resources, i.e., computing and storage resources. Therefore, the combination of virtualization technology and edge computing become important in order to realize the rapid deployment of intelligent application in an edge server or an edge node by virtualization technology. The traditional virtual machine (VM) is no longer suitable for resource-constrained devices. Container technique including Docker can effectively solve these problems, but it also depends on an operating system. Unikernel is the state-of-art virtualization technology. In this paper, we combine Unikernel with edge computing to explore its application in an edge computing system. An application architecture of edge computing based on Unikernel is proposed. It is suitable for application in edge computing.

关键词： unikernel docker virtualization technology edge computing

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Singular perturbation-based saturated adaptive control for underactuated Euler-Lagrange systems

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ISA TRANSACTIONS 2022年 119卷 74-80页

作者： Sun, Tairen Zhang, Xuexin Yang, Hongjun Pan, Yongping Jiangsu Univ Sch Elect Informat & Engn Zhenjiang 212013 Jiangsu Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

This paper proposes a saturated smooth adaptive controller for regulating a certain type of underactuated Euler-Lagrange systems (UELSs) with modeling uncertainties and control saturations based on a singular perturbation approach. Compared with relevent literature, the advantages of the proposed controller include: (1) it renders the UELS semiglobally asymptotically track the desired position without the violation of control input constraints;(2) high-order derivatives of positions are not required in its implementation. The Hoppensteadt's Theorem is employed to show that the proposed saturated controller renders the UELS semiglobally asymptotically stable about the desired set point with the satisfaction of control input constraints. The control effectiveness is validated by simulations on a two-link compliant robot arm. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Underactuated system Euler-Lagrange system Compliant robot Nonlinear control Adaptive control control saturation Output regulation

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Mixture Correntropy-Based Kernel Extreme Learning Machines

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2022年第2期33卷 811-825页

作者： Zheng, Yunfei Chen, Badong Wang, Shiyuan Wang, Weiqun Qin, Wei Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Xidian Univ Sch Life Sci & Technol Xian 710071 Peoples R China

Kernel-based extreme learning machine (KELM), as a natural extension of ELM to kernel learning, has achieved outstanding performance in addressing various regression and classification problems. Compared with the basic ELM, KELM has a better generalization ability owing to no needs of the number of hidden nodes given beforehand and random projection mechanism. Since KELM is derived under the minimum mean square error (MMSE) criterion for the Gaussian assumption of noise, its performance may deteriorate under the non-Gaussian cases, seriously. To improve the robustness of KELM, this article proposes a mixture correntropy-based KELM (MC-KELM), which adopts the recently proposed maximum mixture correntropy criterion as the optimization criterion, instead of using the MMSE criterion. In addition, an online sequential version of MC-KELM (MCOS-KELM) is developed to deal with the case that the data arrive sequentially (one-by-one or chunk-by-chunk). Experimental results on regression and classification data sets are reported to validate the performance superiorities of the new methods.

关键词： Kernel Optimization Learning systems Robustness Support vector machines Mean square error methods Extreme learning machine (ELM) kernel method mixture correntropy online learning

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Constrained Tensor Representation Learning for Multi-View Semi-Supervised Subspace Clustering

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IEEE TRANSACTIONS ON MULTIMEDIA 2022年 24卷 3920-3933页

作者： Tang, Yongqiang Xie, Yuan Zhang, Chenyang Zhang, Wensheng Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai 200062 Peoples R China 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 101408 Peoples R China

Multi-view subspace clustering is an effective method to partition data into their corresponding categories. Nevertheless, existing multi-view subspace clustering approaches generally operate in a purely unsupervised manner, while ignoring the valuable weakly supervised information that can be readily obtained in many practical applications. In this paper, we consider the weakly supervised form of sample pair constraints, and devote to promoting the performance of multi-view subspace clustering with the aid of such prior knowledge. To achieve this goal, inspired by the intrinsic block diagonal structure of ideal low-rank representation (LRR), we propose a novel regularization to integrate must-link, cannot-link and normalization constraints into a unified formulation. The proposed regularization can be regarded as a general description for sample pairwise constraints, and thus provides a flexible framework for multi-view semi-supervised subspace clustering task. Furthermore, we devise a contrained tensor representation learning (CTRL) model that takes advantage of our proposed regularization to facilitate the learning of the desired representation tensor. An efficient optimization algorithm based on alternating direction minimization strategy is carefully designed to solve the proposed CTRL model. Extensive experiments on eight challenging real-world datasets are conducted, and the results validate the effectiveness of our designed pairwise constraints regularization, as well as the superiority of the proposed CTRL model.

关键词： Representation learning Tensors Correlation Clustering algorithms Minimization Task analysis Optimization Multi-view learning pairwise constraint semi-supervised clustering subspace learning tensor singular value decomposition (t-SVD)

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