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Milestones in Autonomous Driving and Intelligent Vehicles Part I: control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors

arXiv

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arXiv 2023年

作者： Chen, Long Li, Yuchen Huang, Chao Xing, Yang Tian, Daxin Li, Li Hu, Zhongxu Teng, Siyu Lv, Chen Wang, Jinjun Cao, Dongpu Zheng, Nanning Wang, Fei-Yue The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Waytous Ltd. China BNU-HKBU United International College Zhuhai519087 China Hong Kong Baptist University Kowloon999077 Hong Kong The Department of Industrial and System Engineering Hong Kong Polytechnical University Hong Kong The School of Aerospace Transport and Manufacturing Cranfield University United Kingdom The School of Transportation Science and Engineering Beihang University China The Department of Automation Tsinghua University China The Department of Mechanical and Aerospace Engineering Nanyang Technological University Singapore The College of Artificial Intelligence Xi'an Jiaotong University China The School of Mechanical Engineering Tsinghua University China

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks and lack systematic summaries and research directions in the future. Our work is divided into 3 independent articles and the first part is a Survey of Surveys (SoS) for total technologies of AD and IVs that involves the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. This is the second part (Part I for this technical survey) to review the development of control, computing system design, communication, High Definition map (HD map), testing, and human behaviors in IVs. In addition, the third part (Part II for this technical survey) is to review the perception and planning sections. The objective of this paper is to involve all the sections of AD, summarize the latest technical milestones, and guide abecedarians to quickly understand the development of AD and IVs. Combining the SoS and Part II, we anticipate that this work will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Group Consensus for Euler-Lagrange Multi-Agent systems with Dynamic Event-Triggered control

Group Consensus for Euler-Lagrange Multi-Agent Systems with ...

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International Conference on control and Automation (ICCA)

作者： Ran Cao Long Cheng Zhe Dong State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Electrical and Control Engineering North China University of Technology Beijing China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

This paper mainly investigates the group consensus of Euler-Lagrange multi-agent systems based on the dynamical event-triggered controller. All agents in the system are divided into several groups. The agents in the same group converge to one state and those in the different groups may not coincide. To use fewer communication resources, an event-triggered controller using the dynamical event-triggered law is designed. Several mild assumptions are made to achieve the group consensus. And Lyapunov analysis is given to prove that the group consensus can be achieved under the designed control algorithm. Compared to existing results, this paper mainly designs the dynamical event-triggered law to achieve the group consensus rather than consensus or tracking, and the concerned agent is the nonlinear Euler-Lagrange system.

关键词： Nonlinear dynamical systems Directed graphs Multi-agent systems Laplace equations Symmetric matrices Network topology Torque

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Webly Supervised Knowledge Embedding Model for Visual Reasoning

Webly Supervised Knowledge Embedding Model for Visual Reason...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wenbo Zheng Lan Yan Chao Gou Fei-Yue Wang School of Software Engineering Xi'an Jiaotong University The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences School of Intelligent Systems Engineering Sun Yat-sen University

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Visual reasoning between visual image and natural language description is a long-standing challenge in computer vision. While recent approaches offer a great promise by compositionality or relational computing, most of them are oppressed by the challenge of training with datasets containing only a limited number of images with ground-truth texts. Besides, it is extremely time-consuming and difficult to build a larger dataset by annotating millions of images with text descriptions that may very likely lead to a biased model. Inspired by the majority success of webly supervised learning, we utilize readily-available web images with its noisy annotations for learning a robust representation. Our key idea is to presume on web images and corresponding tags along with fully annotated datasets in learning with knowledge embedding. We present a two-stage approach for the task that can augment knowledge through an effective embedding model with weakly supervised web data. This approach learns not only knowledge-based embeddings derived from key-value memory networks to make joint and full use of textual and visual information but also exploits the knowledge to improve the performance with knowledge-based representation learning for applying other general reasoning tasks. Experimental results on two benchmarks show that the proposed approach significantly improves performance compared with the state-of-the-art methods and guarantees the robustness of our model against visual reasoning tasks and other reasoning tasks.

关键词： Visualization Cognition Knowledge based systems Task analysis Knowledge engineering Modulation Robustness

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Dynamic fusion based federated learning for COVID-19 detection

arXiv

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arXiv 2020年

作者： Zhang, Weishan Zhou, Tao Lu, Qinghua Wang, Xiao Zhu, Chunsheng Sun, Haoyun Wang, Zhipeng Lo, Sin Kit Wang, Fei-Yue China Data61 CSIRO Australia State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China SUSTech Institute of Future Networks Southern University of Science and Technology China

Medical diagnostic image analysis (e.g., CT scan or X-Ray) using machine learning is an efficient and accurate way to detect COVID-19 infections. However, sharing diagnostic images across medical institutions is usually not allowed due to the concern of patients’ privacy. This causes the issue of insufficient datasets for training the image classification model. Federated learning is an emerging privacy-preserving machine learning paradigm that produces an unbiased global model based on the received updates of local models trained by clients without exchanging clients’ local data. Nevertheless, the default setting of federated learning introduces huge communication cost of transferring model updates and can hardly ensure model performance when data heterogeneity of clients heavily exists. To improve communication efficiency and model performance, in this paper, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections. First, we design an architecture for dynamic fusion-based federated learning systems to analyse medical diagnostic images. Further, we present a dynamic fusion method to dynamically decide the participating clients according to their local model performance and schedule the model fusion-based on participating clients’ training time. In addition, we summarise a category of medical diagnostic image datasets for COVID-19 detection, which can be used by the machine learning community for image analysis. The evaluation results show that the proposed approach is feasible and performs better than the default setting of federated learning in terms of model performance, communication efficiency and fault tolerance. Copyright © 2020, The Authors. All rights reserved.

关键词： Image analysis

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Spiking Neuron Networks based Energy-Efficient Object Detection for Mobile Robot

Spiking Neuron Networks based Energy-Efficient Object Detect...

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Chinese Automation Congress (CAC)

作者： Bo-Chao Liu Qian Yu Jing-Wen Gao Shuang Zhao Xiong-Chang Liu Yan-Feng Lu Beijing Information Science and Technology University Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Instrument Science and Optoelectronic Engineering Beijing Information Science and Technology University Beijing China The State Key Laboratory of Management and Control for Complex Systems CASIA Beijing China Science and Technology on Underwater Vehicle Technology Laboratory Harbin Engineering University Harbin China

ISBN: (纸本)9781665426480

This paper focuses on the highly energy-efficient visual detection method, which first apples spiking neuron networks based object detection method in mobile robot. Visual perception is an important part of mobile robot, but the real-time visual perception is with high energy consumption that limits the battery running time of the robotic platform. In this paper, we bring in a Spiking-YOLO algorithm for the visual detection of the mobile robot, which combines the YOLO algorithm with the third generation artificial neural network-Spiking Neural Network (SNN). The Spiking-YOLO integrates the characteristics in real-time and high precision of the YOLO, and event-driven and low-powered nature of the SNN. We introduce the Spiking-YOLO model to the Robotnik's SUMMIT-XL mobile robot to perform the task of visual detection and the experimental verification is carried out on data sets. The experimental results show that this method has excellent energy-efficient and precision on the detection task of the robot.

关键词： Visualization Energy consumption Neurons Object detection Energy efficiency Real-time systems Mobile robots

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Development of a propeller with undulating fins and its characteristics

Development of a propeller with undulating fins and its char...

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2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019

作者： Ma, Ruichen Wang, Yu Wang, Rui Wang, Shuo State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology

ISBN: (纸本)9781728137261

This paper addresses some experiments about measurements of thrust and heave force generated by a modular designed biomimetic underwater propeller. The propeller has a ribbon fin that consists of twelve rays linked by a latex membrane. Different forms of waves can be generated by the propeller. A measurement platform is constructed to obtain the thrust and heave force generated by the waves along with the input power of the propeller. Three types of waves are performed on the platform, including sinusoidal waves, fusiform sinusoidal waves and inward counter-propagating sinusoidal waves. By changing the parameters of undulating waves, a total of three hundred series of experiments are implemented. The measurement results of three types of waves are analyzed and the differences among three types of waves are compared. © 2019 IEEE.

关键词： Fins (heat exchange)

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Graph-FCN for image semantic segmentation

arXiv

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arXiv 2020年

作者： Lu, Yi Chen, Yaran Zhao, Dongbin Chen, Jianxin State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing101408 China Beijing University of Chinese Medicine Beijing100029 China

Semantic segmentation with deep learning has achieved great progress in classifying the pixels in the image. However, the local location information is usually ignored in the high-level feature extraction by the deep learning, which is important for image semantic segmentation. To avoid this problem, we propose a graph model initialized by a fully convolutional network (FCN) named Graph-FCN for image semantic segmentation. Firstly, the image grid data is extended to graph structure data by a convolutional network, which transforms the semantic segmentation problem into a graph node classification problem. Then we apply graph convolutional network to solve this graph node classification problem. As far as we know, it is the first time that we apply the graph convolutional network in image semantic segmentation. Our method achieves competitive performance in mean intersection over union (mIOU) on the VOC dataset(about 1.34% improvement), compared to the original FCN model. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

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A Blockchain-based Framework for Collaborative Production in Distributed and Social Manufacturing

A Blockchain-based Framework for Collaborative Production in...

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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Ouyang, Liwei Yuan, Yong Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Qingdao Academy of Intelligent Industries Qingdao266109 China

ISBN: (纸本)9781728128535

In recent years, novel collaborative production paradigms, such as (re-)distributed manufacturing and social manufacturing, have attracted intensive attention due to their potential of further changing the existing manufacturing modes and industrial structures. Blockchain, as a new distributed computing architecture, is expected to be successfully used in manufacturing, and help solve issues related to interoperability and collaboration, security and supervision, marketization and protocol, democratic organization and global value chain governance, and so on. In this paper, a blockchain-based framework for collaborative production is proposed, aiming at providing novel ideas for the design, implementation and optimization of collaborative production, and also inspiring the upgrading and transformation of manufacturing industries. © 2019 IEEE.

关键词： Smart contract

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Batch Normalization Masked Sparse Autoencoder for Robotic Grasping Detection

Batch Normalization Masked Sparse Autoencoder for Robotic Gr...

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2020 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhenzhou Shao Ying Qu Guangli Ren Guohui Wang Yong Guan Zhiping Shi Jindong Tan Beijing Advanced Innovation Center for Imaging Technology and Beijing Key Laboratory of Light Industrial Robot and Safety Verification Capital Normal University Beijing China The Engineering College The University of Tennessee Knoxville TN USA State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

To improve the accuracy of the grasping detection, this paper proposes a novel detector with batch normalization masked evaluation model. It is designed with a two-layer sparse autoencoder, and a Batch Normalization based mask is incorporated into the second layer of the model to effectively reduce the features with weak correlation. The extracted features from such model are more distinctive, which guarantees the higher accuracy of the grasping detection. Extensive experiments show that the proposed evaluation model outperforms the state-of- the-art, and the recognition accuracy can reach 95.51% for robotic grasping detection.

关键词： Training Redundancy Grasping Detectors Feature extraction Intelligent robots Load modeling

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A Brief Research and Lookback of the Development of Iris Recognition

A Brief Research and Lookback of the Development of Iris Rec...

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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Li, Xiaoshuang Hu, Shenhua Lv, Chengkan Qin, Haojun Zhu, Fenghua Wang, Feiyue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences Beijing China School of Electrical Engineering and Aotumation Wuhan University Wuhan China

ISBN: (纸本)9781728128535

Personal identification has been used more and more widely in information society. Iris recognition, as one of numerous identification methods, has already been applied in many situations, because the iris patterns have stable, unique and non-invasive features. We analyze the general process of iris recognition in this paper, including image preprocessing, feature extraction, classification and recognition. We also study the progress in iris recognition brought by deep learning method. It is our belief that traditional approaches play a very significant role in iris recognition field, while deep learning methods will make a great difference to iris recognition. © 2019 IEEE.

关键词： Deep learning

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