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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Li, Bai Zhang, Youmin Zhang, Yue Jia, Ning Ge, Yuming Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'An University of Technology Xi'an710048 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China Department of Mechanical Industrial and Aerospace Engineering Concordia Institute of Aerospace Design and Innovation Concordia University Montreal Canada Division of Systems Engineering Center for Information and Systems Engineering Boston University BostonMA02215 United States Institute of Systems Engineering Tianjin University Tianjin300072 China China Academy of Information and Communications Technology Beijing100191 China

ISBN: (纸本)9781538644522

In this paper, we propose a cooperative motion planning method for a group of connected and automated vehicles (CAVs) crossing a lane-free intersection without using explicit traffic signaling. This multi-vehicle motion planning task is formulated as a centralized optimal control problem. However, the solution to this optimal control problem is numerically intractable due to the high dimensionality of the collision-avoidance constraints and the nonlinearity of the vehicle kinematics. A two-stage strategy is proposed for generating online solutions: at Stage 1, the CAVs are requested to reach a standard formation before entering the intersection;at Stage 2, the vehicles cross the intersection. As the motion planning sub-problem at Stage 2 begins with a standard configuration, the optimal solution to this standard sub-problem can be computed offline in advance and applied online directly. On the other hand, the formation reconfiguration sub-problem at Stage 1 is easy to solve online. Through dividing the entire dynamic process into two periods, the difficulties in the original optimal control problem are significantly reduced so that the real-time performance is achieved. © 2018 IEEE.

关键词： Motion planning

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Advances in Vision-Based Lane Detection：Algorithms,Integration,Assessment,and Perspectives on ACP-Based Parallel Vision

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IEEE/CAA Journal of Automatica Sinica 2018年第3期5卷 645-661页

作者： Yang Xing Chen Lv Long Chen Huaji Wang Hong Wang Dongpu Cao Efstathios Velenis Fei-Yue Wang Advanced Vehicle Engineering Centre Cranfield University Bedford MK43 0AL U.K Vehicle Intelligence Pioneers Ltd Qingdao 266000 China School of Data and Computer Science Sun Yat-Sen University Guangzhou 510275 China Mechanical and Mechatronics Engineering with the University of Waterloo 200 University Avenue West Waterloo ON N2L 3G1 Canada State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China ieee

Lane detection is a fundamental aspect of most current advanced driver assistance systems(ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous visionbased lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system,and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.

关键词： Advanced driver assistance systems(ADASs) ACP theory benchmark lane detection parallel vision performance evaluation

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A Real-time Underwater Robotic Visual Tracking Strategy Based on Image Restoration and Kernelized Correlation Filters

A Real-time Underwater Robotic Visual Tracking Strategy Base...

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第30届中国控制与决策会议

作者： Shihan Kong Xi Fang Xingyu Chen Zhengxing Wu Junzhi Yu University of Chinese Academy of Sciences Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Mechanical Engineering and Automation Beihang university

In this paper, a real-time underwater robotic visual tracking strategy(RUTS) based on underwater image restoration and Kernelized Correlation Filters(KCF) is developed for underwater robots. A real-time and unsupervised advancement scheme(RUAS), which is utilized in this strategy, performs robustly in restoring underwater images. The KCF, as a high-speed and accurate tracking method on land, is employed in this strategy. To handle the conflict between tracking speed and accuracy, we propose a tracking strategy based on KCF in video sequence restored by RUAS,comparing Histogram of Oriented Gradient(HOG) descriptors and raw pixels gray(RPG) descriptors. We define an index Ac to describe the tracking accuracy and regard the number of frames per second as computing speed. Results of contrast experiments show that the RPG, a much simpler descriptor, can achieve tracking accuracy as precise as HOG,accompanied by an increase of tracking speed up to 36%. Finally, experiments of the KCF-based tracker with RPG on different underwater objects demonstrate the feasibility of the formed RUTS.

关键词： Underwater robotic vision real-time underwater tracking underwater image restoration KCF

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Hierarchical Connectome Modes and Critical State Jointly Maximize Human Brain Functional Diversity

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Physical Review Letters 2019年第3期123卷 038301-038301页

作者： Rong Wang Pan Lin Mianxin Liu Ying Wu Tao Zhou Changsong Zhou State Key Laboratory for Strength and Vibration of Mechanical Structures Shaanxi Engineering Laboratory for Vibration Control of Aerospace Structures School of Aerospace Engineering Xi’an Jiaotong University Xi’an 710049 China Department of Physics Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong) Institute of Computational and Theoretical Studies Hong Kong Baptist University Kowloon Tong Hong Kong College of Science Xi’an University of Science and Technology Xi’an 710054 China Key Laboratory of Cognitive Science College of Biomedical Engineering South-Central University for Nationalities Wuhan 430074 China Complex Lab University of Electronic Science and Technology of China Chengdu 611731 People’s Republic of China Research Centre HKBU Institute of Research and Continuing Education Shenzhen 518057 China Beijing Computational Science Research Center Beijing 100084 China Department of Physics Zhejiang University Hangzhou 310058 China

The brain requires diverse segregated and integrated processing to perform normal functions in terms of anatomical structure and self-organized dynamics with critical features, but the fundamental relationships between the complex structural connectome, critical state, and functional diversity remain unknown. Herein, we extend the eigenmode analysis to investigate the joint contribution of hierarchical modular structural organization and critical state to brain functional diversity. We show that the structural modes inherent to the hierarchical modular structural connectome allow a nested functional segregation and integration across multiple spatiotemporal scales. The real brain hierarchical modular organization provides large structural capacity for diverse functional interactions, which are generated by sequentially activating and recruiting the hierarchical connectome modes, and the critical state can best explore the capacity to maximize the functional diversity. Our results reveal structural and dynamical mechanisms that jointly support a balanced segregated and integrated brain processing with diverse functional interactions, and they also shed light on dysfunctional segregation and integration in neurodegenerative diseases and neuropsychiatric disorders.

关键词： Scaling laws of complex systems Self-organized criticality Brain network

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Reinforcement Learning-Based Predictive control for Autonomous Electrified Vehicles

Reinforcement Learning-Based Predictive Control for Autonomo...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Liu, Teng Yang, Chao Hu, Chuanzheng Wang, Hong Li, Li Cao, Dongpu Wang, Fei-Yue State Key Laboratory of Automotive Simulation and Control Jilin University China Vehicle Intelligence Pioneers Inc. Qingdao Shandong266109 China Jiangsu XCMG Construction Machinery Research Institute Ltd. Xuzhou Jiangsu221004 China Mechanical and Mechatronics Engineering Department Waterloo University N2L 3G1 Canada Faculty of Department of Automation Tsinghua University China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781538644522

This paper proposes a learning-based predictive control technique for self-driving hybrid electric vehicle (HEV). This approach is a hierarchical framework. The higher-level is a human-like driver model, which is applied to predict accelerations in the car following situation to replicate a human driver's demonstrations. The lower-level is a reinforcement learning (RL)-based controller, which enforces the battery and fuel consumption constraints to improve energy efficiency of HEV. In addition, we present induced matrix norm (IMN) to handle cases that the training data cannot provide sufficient information on how to operate in current driving situation. Simulation results illustrate that the proposed method can reproduce human driver's driving style and promote fuel economy. © 2018 IEEE.

关键词： Reinforcement learning

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End-to-End Driving Activities and Secondary Tasks Recognition Using Deep Convolutional Neural Network and Transfer Learning

End-to-End Driving Activities and Secondary Tasks Recognitio...

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IEEE Intelligent Vehicles Symposium

作者： Yang Xing Jianlin Tang Hong Liu Chen Lv Dongpu Cao Efstathios Velenis Fei-Yue Wang Advanced Vehicle Engineering Centre Cranfield University JIANGSU XCMG CONSTRUCTION MACHINERY RESEARCH INSTITUTE LTD School of Mechanical and Mechatronics Engineering University of Waterloo State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781538644539

Drivers' decision and their corresponding behaviors are important aspects that can affect the driving safety, and it is necessary to understand the driver behaviors in real-time. In this study, an end-to-end driving-related tasks recognition system is proposed. Specifically, seven common driving activities are identified, which are normal driving, right mirror checking, rear mirror checking, left mirror checking, using in-vehicle video device, texting, and answering mobile phone. Among these, the first four activities are regarded as normal driving tasks, while the rest three are divided into distraction group. The images are collected using a consumer range camera, namely, Kinect. In total, five drivers are involved in the naturalistic data collection. Before training the identification model, the raw images are first segmented using a Gaussian mixture model (GMM) to extract the driver region from the background. Then, a pre-trained deep convolutional neural network (CNN) model is trained to classify the behaviors, which directly takes the processed RGB images as the input and outputs the identified label. In this work, the AlexNet is selected as the pre-trained CNN model. Then, to reduce the training cost, the transfer learning mechanism is applied to the CNN model. An average of 79% detection accuracy is achieved for the seven driving tasks. The proposed integration model can be used as a low-cost driver distraction and dangerous tasks recognition modeL.

关键词： Driver Behavior Religious Missions Transfer (Psychology) Automobile driving driving safety end-to-end Mirrors Drivers Neural network

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Robust control of Twin Rotor MIMO System with Quantized Output

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IFAC-PapersOnLine 2017年第1期50卷 4849-4854页

作者： Margun A. Furtat I. Kremlev A. Control Systems and Informatics Department ITMO University 49 Kronveksky av. Saint Petersburg 197101 Russian Federation Control of Complex Systems Lab. Institute for Problems of Mechanical Engineering V.O. 61 Bolshoy str. Saint Petersburg 199178 Russian Federation

The paper is devoted to the design and experimental application of robust controller for MIMO plants under parametric uncertainties, external disturbances and quantized output measurement. Only quantized output is assumed to be measured. controller synthesis is based on consecutive compensator approach. Experimental research is performed with use of helicopter like twin rotor MIMO laboratory bench. Experiments show efficiency of the proposed control method in comparison with PID controller. © 2017

关键词： disturbance MIMO system parametric uncertainties quantizer Robust control

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Visual Place Recognition in Long-term and Large-scale Environment based on CNN Feature

Visual Place Recognition in Long-term and Large-scale Enviro...

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IEEE Intelligent Vehicles Symposium

作者： Jianliang Zhu Yunfeng Ai Bin Tian Dongpu Cao Sebastian Scherer School of Artificial Intelligence University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Department of Mechanical & Mechatronics Engineering University of Waterloo Robotics Institute Carnegie Mellon University

ISBN: (纸本)9781538644539

With the universal application of camera in intelligent vehicles, visual place recognition has become a major problem in intelligent vehicle localization. The traditional solution is to make visual description of place images using hand-crafted feature for matching places, but this description method is not very good for extreme variability, especially for seasonal transformation. In this paper, we propose a new method based on convolutional neural network (CNN), by putting images into the pre-trained network model to get automatically learned image descriptors, and through some operations of pooling, fusion and binarization to optimize them, then the similarity result of place recognition is presented with the Hamming distance of the place sequence. In the experimental part, we compare our method with some state-of-the-art algorithms, FABMAP, ABLE-M and SeqSLAM, to illustrate its advantages. The experimental results show that our method based on CNN achieves better performance than other methods on the representative public datasets.

关键词： Hamming distance Intelligent vehicles binarization Recognition (Psychology) Visualization network model Cameras images

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Robust medical instrument segmentation challenge 2019

arXiv

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arXiv 2020年

作者： Roβ, Tobias Reinke, Annika Full, Peter M. Wagner, Martin Kenngott, Hannes Apitz, Martin Hempe, Hellena Filimon, Diana Mindroc Scholz, Patrick Tran, Thuy Nuong Bruno, Pierangela Arbelaéz, Pablo Bian, Gui-Bin Bodenstedt, Sebastian Bolmgren, Jon Lindström Bravo-Sánchez, Laura Chen, Hua-Bin González, Cristina Guo, Dong Halvorsen, Pål Heng, Pheng-Ann Hosgor, Enes Hou, Zeng-Guang Isensee, Fabian Jha, Debesh Jiang, Tingting Kirtac, Kadir Jin, Yueming Kletz, Sabrina Leger, Stefan Li, Zhixuan Maier-Hein, Klaus H. Ni, Zhen-Liang Riegler, Michael A. Schoeffmann, Klaus Shi, Ruohua Speidel, Stefanie Stenze, Michael Twick, Isabell Wang, Gutai Wang, Jiacheng Wang, Liansheng Wang, Lu Zhang, Yujie Zhou, Yan-Jie Zhu, Lei Wiesenfarth, Manuel Kopp-Schneider, Annette Müller-Stich, Beat P. Maier-Hein, Lena Heidelberg Germany University of Heidelberg Germany Heidelberg Germany Department of General Heidelberg University Hospital Visceral and Transplant Surgery Heidelberg Germany caresyntax Berlin Germany University of Chinese Academy Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China SimulaMet Oslo Norway Tromsø Norway Oslo Norway School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Partner Site Dresden Heidelberg Germany Faculty of Medicine and University Hospital Carl Gustav Carus Technische Universität Dresden Dresden Germany Dresden Germany Universidad de Los Andes Bogotá Colombia Peking University Peking China Department of Computer Science School of Informatics Xiamen University Xiamen China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong German Cancer Research Center Division of Biostatistics Heidelberg Germany Institute of Information Technology Klagenfurt University Austria

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and roboticassisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization;algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts). Copyright © 2020, The Authors. All rights reserved.

关键词： Endoscopy

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Siamese-ResNet: Implementing Loop Closure Detection based on Siamese Network

Siamese-ResNet: Implementing Loop Closure Detection based on...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Qiu, Kai Ai, Yunfeng Tian, Bin Wang, Bin Cao, Dongpu School of Aritificial Intelligence 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 Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China School of Software Engineering University of Science and Technology of China HeFei230026 China Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada

ISBN: (纸本)9781538644522

Deep learning has made significant breakthroughs in the tasks of image classification, detection, segmentation, etc. However, the application of deep learning in robotics is still scarce. SLAM is a fundamental problem in robotics and loop closure detection is an important part of SLAM. This paper attempts to use supervised learning methods to solve the loop closure detection problem in vision SLAM. We proposed Siamese-ResNet network, which combines Siamese network with ResNet to detect loop closure. To show the effectiveness of Siamese-ResNet, we evaluate Siamese-ResNet and FabMap2.0 on several open published datasets, like TUM SLAM dataset and FabMap SLAM dataset. Compared with FabMap2.0, Siamese-ResNet shows higher accuracy, better robustness and shorter time-consuming. © 2018 IEEE.

关键词： Robotics

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