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IEEE Transactions on Intelligent Vehicles 2024年 1-17页

作者： Chang, Cheng Zhang, Jiawei Ge, Jingwei Zhang, Zuo Wei, Junqing Li, Li Wang, Fei-Yue Department of Automation Tsinghua University Beijing China DiDi Autonomous Driving Company Beijing China Department of Automation BNRist Tsinghua University Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Intelligent vehicles and autonomous driving systems rely on scenario engineering for intelligence and index (I&I), calibration and certification (C&C), and verification and validation (V&V). To extract and index scenarios, various vehicle interactions are worthy of much attention, and deserve refined descriptions and labels. However, existing methods cannot cope well with the problem of scenario classification and labeling with vehicle interactions as the core. In this paper, we propose VistaScenario framework to conduct interaction scenario engineering for vehicles with intelligent systems for transport automation. Based on the summarized basic types of vehicle interactions, we slice scenario data stream into a series of segments via spatiotemporal scenario evolution tree. We also propose the scenario metric Graph-DTW based on Graph Computation Tree and Dynamic Time Warping to conduct refined scenario comparison and labeling. The extreme interaction scenarios and corner cases can be efficiently filtered and extracted. Moreover, with naturalistic scenario datasets, testing examples on trajectory prediction model demonstrate the effectiveness and advantages of our framework. VistaScenario can provide solid support for the usage and indexing of scenario data, further promote the development of intelligent vehicles and transport automation. IEEE

关键词： Automation

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The Potential of Low-Altitude Airspace: The Future of Urban Air Transportation

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第8期9卷 5250-5254页

作者： Hailong Huang Jiangcheng Su Fei-Yue Wang Department of Aeronautical and Aviation Engineering Hong Kong Polytechnic University Hong Kong China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Engineering Macau University of Science and Technology Macau China

Many megacities, such as London, New York, Paris, Tokyo, Beijing, and Shanghai, have been born due to urban- ization. While citizens enjoy the convenience of city life, such cities suffer from drawbacks of transportation: the average commute time has been increasing over the past years. With the maturity of electric vertical take-off and landing (eVTOL) aircraft technology, decision-makers have started to look upward to the low-altitude airspace. This blue space has the potential to reshape the transportation system and shorten the average travel time in daily life. To explore this emerging area, we held a Distributed/Decentralized Hybrid Workshop on Sustain- ability for Transportation and Logistics (DHW-STL), and this letter summarizes the outcomes of our discussion. Here, we first pinpoint the pain issues of conventional transportation. Then, we present the emerging low-altitude airspace transportation option, followed by a discussion on how such a new transportation option can integrate with the conventional ground-based transportation system. Some thoughts about the pathway to further development of the integrated transportation system are shared finally.

关键词： Aircraft Transportation Urban areas Air traffic control Traffic congestion Meters Aircraft propulsion Motors Logistics Indexes

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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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Pedestrian Detection for Autonomous Vehicles Using Virtual-to-Real Augmentation

Pedestrian Detection for Autonomous Vehicles Using Virtual-t...

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2022 Chinese Automation Congress, CAC 2022

作者： Lu, Bo Huang, Min Li, Xi Nie, Yunhao Miao, Qinghai Lv, Yisheng School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665465335

Annotated data are essential to the success of training deep neural models for autonomous driving. Practically, it is both expensive and time consuming to collect and annotate plenty of data driving cross the city. It is even harder to cover the corner cases involving rare pedestrian poses with occlusions. To tackle this problem, we propose a data augmentation method for pedestrian detection. First, we take a SMPL model to generate pedestrians with a variety of poses and textures. Second, we insert the rendered pedestrians into images with real traffic background according to vanishing point. Occlusions can also be set easily by taking use of segmentation annotations of original real dataset. We evaluated the effectiveness of the generated synthetic dataset using multiple detectors. Results of three experiments shown that detectors trained with mixture of synthetic and real data outperform those trained on only real datasets. © 2022 IEEE.

关键词： Computer vision

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SWDPM: A Social Welfare-Optimized Data Pricing Mechanism

arXiv

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arXiv 2023年

作者： Yu, Yi Yao, Shengyue Wang, Fei-Yue Li, Juanjuan Lin, Yilun Urban Computing Lab Shanghai AI Laboratory Shanghai China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Macau Institute of Systems Engineering Macau University of Science and Technology China

Data trading has been hindered by privacy concerns associated with user-owned data and the infinite reproducibility of data, making it challenging for data owners to retain exclusive rights over their data once it has been disclosed. Traditional data pricing models relied on uniform pricing or subscription-based models. However, with the development of Privacy-Preserving Computing techniques, the market can now protect the privacy and complete transactions using progressively disclosed information, which creates a technical foundation for generating greater social welfare through data usage. In this study, we propose a novel approach to modeling multi-round data trading with progressively disclosed information using a matchmaking-based Markov Decision Process (MDP) and introduce a Social Welfare-optimized Data Pricing Mechanism (SWDPM) to find optimal pricing strategies. To the best of our knowledge, this is the first study to model multi-round data trading with progressively disclosed information. Numerical experiments demonstrate that the SWDPM can increase social welfare 3 times by up to 54% in trading feasibility, 43% in trading efficiency, and 25% in trading fairness by encouraging better matching of demand and price negotiation among traders. Copyright © 2023, The Authors. All rights reserved.

关键词： Markov processes

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Stress Detection Using Wearable Devices based on Transfer Learning

Stress Detection Using Wearable Devices based on Transfer Le...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Wu, Jinting Zhang, Yujia Zhao, Xiaoguang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665401265

Excessive stress will have a negative impact on people's physical and mental health, especially for some special occupations. Because stressful stimuli can trigger a variety of physiological responses, analyzing physiological signals collected by wearable devices has become an important way to evaluate the stress state in recent years. However, the number of available subjects of a target group may be small, and collecting a large amount of data when the target group changes is costly and time-consuming. To solve this problem, we propose a stress detection framework for a small target group which uses adversarial transfer learning method to learn shared knowledge about stress between different groups. In order to verify the performance of the framework, we establish a dataset consisting of 264 ordinary college students and 32 police school students, aiming to evaluate the acute stress state of police school students under video stimuli for psychological training in the future. Comprehensive experiments show that our algorithm has achieved a significant improvement in the target group compared with the baseline methods. © 2021 IEEE.

关键词： Physiology

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Omnidirectional Drift Control of an Underwater Biomimetic Vehicle-Manipulator System via Reinforcement Learning 10

Omnidirectional Drift Control of an Underwater Biomimetic Ve...

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10th IEEE Data Driven Control and Learning systems Conference, DDCLS 2021

作者： Ma, Ruichen Wang, Yu Wang, Rui Wang, Shuo University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781665424233

This paper proposes an omnidirectional drift control on an underwater biomimetic vehicle-manipulator system (UB-VMS). The UBVMS has two biomimetic propellers, they obtain propulsive force by actuating their undulating fins. A configuration of the system, the dynamics of the UBVMS and the kinematics of the fins are given respectively. Then the control problem is designed as a Markov decision process (MDP). A reinforcement learning method based on the twin delayed deep deterministic policy gradient (TD3) is proposed for this MDP. A control policy is well trained by the reinforcement learning method and tested in eight different simulations. An analysis of the simulation results is also given. © 2021 IEEE.

关键词： Reinforcement learning

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Call for V&VIV: Verification and Validation of Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2022年第3期7卷 401-406页

作者： Fei-Yue Wang Javier Ibanez-Guzman Yisheng Lv State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Expert Autonomous Systems Artificial Intelligence Renault S.A. SIVALab CNRS Common Laboratory Chateau Renault France

Presents a summary of articles included in this issue of the publication.

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Evaluating the Generalization of 3D Detectors with Hybrid Point Clouds 2

Evaluating the Generalization of 3D Detectors with Hybrid Po...

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2nd International Conference on Computer Science and management Technology, ICCSMT 2021

作者： Tian, Yonglin Wang, Xiao University of Science and Technology of China Department of Automation Hefei China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China

ISBN: (纸本)9781665420631

With the development of LiDAR technologies and deep learning algorithms, tremendous methods have been proposed for 3D detection in intelligent transportation. However, the generalization of 3D detectors under different environments is not fully evaluated due to the lack of diverse testing datasets. Two of the main obstacles in building testing benchmark of point clouds are the huge cost of the annotation of point clouds and the low flexibility in data collection. In this paper, we propose to use hybrid point clouds to evaluate the generalization of multiple 3D detectors when they are transferred from real data to hybrid data. Compared with evaluating 3D detectors with real point clouds dataset, our approach is much more flexible and demands less human labor. © 2021 IEEE.

关键词： Deep learning

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A novel underwater image synthesis method based on a pixel-level self-supervised training strategy

A novel underwater image synthesis method based on a pixel-l...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Wu, Zhiheng Wu, Zhengxing Lu, Yue Wang, Jian Yu, Junzhi Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing100871 China

ISBN: (纸本)9781665436786

With the rapid development of deep neural networks, underwater vision plays an increasingly important role in the underwater robotic operation. However, the scarce underwater datasets greatly limit the performance of deep learning on underwater visual tasks, further hindering the applications of underwater operation. To solve this problem, we propose an underwater image synthesis method, which can directly convert the natural light image into the synthetic underwater image end-to-end. Particularly, a pixel-level self-supervised training strategy is designed to maximize the structural similarity between the synthesized and real images, through training the real underwater images. Finally, extensive experiments are carried out, and the obtained results demonstrate the effectiveness and superiority of our methods by quantitative and qualitative comparisons. The proposed underwater image synthesis method offers a valuable sight for underwater vision and manipulating control. © 2021 IEEE.

关键词： Deep neural networks

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