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Interaction-Aware Cut-In Trajectory Prediction and Risk Assessment in Mixed Traffic

IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1752-1762页

作者： Xianglei Zhu Wen Hu Zejian Deng Jinwei Zhang Fengqing Hu Rui Zhou Keqiu Li Fei-Yue Wang the College of Intelligence and Computing Tianjin UniversityTianjin 300350 the China Automotive Technology and Research Center Co.Ltd. Tianjin 300300China the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China the Department of Mechanical and Mechatronics Engineering University of WaterlooWaterlooON N2L3G1Canada the School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China the Macao University of Science and Technology MacaoChina the Waytous Inc. Qingdao 266000China IEEE the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by *** improve the safety of autonomous vehicles in the mixed traffic,this study proposes a cut-in prediction and risk assessment method with considering the interactions of multiple traffic *** integration of the support vector machine and Gaussian mixture model(SVM-GMM)is developed to simultaneously predict cut-in behavior and *** dimension of the input features is reduced through Chebyshev fitting to improve the training efficiency as well as the online inference *** on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles,two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax ***,the comparative analysis is performed to validate the proposed method using the naturalistic driving *** results show that the proposed method can predict the trajectory with higher precision and effectively evaluate the risk level of a cut-in maneuver compared to the methods without considering interaction.

关键词： Cut-in behavior interaction-aware mixed traffic risk assessment trajectory prediction

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Urban trip generation forecasting based on gradient boosting algorithm 1

Urban trip generation forecasting based on gradient boosting...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Li, Zhishuai Xiong, Gang Zhang, Yu Zheng, Meng Dong, Xisong Lv, Yisheng Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Municipal Institute of City Planning and Design Traffic Planning Department Beijing China

ISBN: (纸本)9781665433372

The four-step transportation model plays an important role in urban planning. The quality of the first phase, i.e. trip generation, determines the performance of the global course. The majority of trip generation forecasting models highly rely on mathematical derivation and have many predictor variables during the prediction, which leads to low accuracy of results and requires laboriously hand-crafted design of input vectors. This paper is the first to introduce the gradient boosting decision tree (GBDT) algorithm for trip generation prediction, and harmonizes such a powerful machine learning method with traditional urban planning requirements to achieve better prediction performance. Unlike the commonly used linear regression method, GBDT can automatically perform feature selection and model the non-linear relationships between input and output variables. Experimental results on real-world residential travel census in Beijing prove that the GBDT model significantly outperforms the baseline and can forecast the trip generation more accurately. © 2021 IEEE.

关键词： Decision trees

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Modeling Method and Simulation Analysis of Smart Substation Protection Scheme Based at OPNET Process Level

Modeling Method and Simulation Analysis of Smart Substation ...

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Sustainable Power and Energy Conference (iSPEC)

作者： Teng Feng Liu Han Zhihong Xiao Jialiang Lu Shenghui Yang Ruiwen He Automation and Control Design Research Center State Grid Economic and Technological Research Institute Co. Ltd. Beijing China Guangdong University of Technology Guangzhou China

ISBN: (纸本)9781665414401

Process layer network and its intelligent electronic devices (IED) are the basic equipments that constitute the relay protection in smart substation. In order to further achieve the standardization and accuracy of the process layer relay protection, a method of constructing the protection scheme based on the OPNET process level is proposed. Based on Standard IEC 61850, logic transition modeling of IED application layer at process level is achieved, and the simulation of the whole protection scheme including data acquisition by electronic transformer, operation and decision-making of protection unit and action of intelligent terminal is realized. Combining the examples of protection processes in line bay and transformer bay, the operation mechanism and working principle of the models are explained, the validity and feasibility of the protection scheme are verified. A good simulation analysis tool and platform for engineering applications are provided.

关键词： Analytical models Adaptation models Navigation Substation protection Decision making Protective relaying Transformers

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Identification of key transmission section in energy internet 4

Identification of key transmission section in energy interne...

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4th IEEE Conference on Energy Internet and Energy System Integration, EI2 2020

作者： Wang, Ziqi Wang, Yudong Li, Bihuan Luo, Jingtang Shen, Hongming State Grid Economic and Technological Research Institute Automation Control Design Research Center Beijing China Zhejiang Universit College of Electrical Engineering Hangzhou China State Grid Sichuan Economic Research Institute Planning and Reviewing Center Chengdu China

ISBN: (纸本)9781728196060

Energy Internet (EI) is a product of deep integration of different energy fields. It breaks the boundary of traditional energy industries, and promotes the interconnection of various primary and secondary energy sources. Based on the basic concept and morphological character of EI, an identification method of key transmission section is proposed. AC/DC power grids and electricity-gas combined transmission lines are analyzed to clarify the transmission characteristics of EI;the transmission betweenness and Kullback-Leibler (KL) divergence are modified to quantify the impact of electricity-gas transmission and power distribution;two kinds of sections are defined to classify transmission lines with different functions. Test results demonstrate the effectiveness of the proposed method. © 2020 IEEE.

关键词： Internet of Energy

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Stability analysis of linear systems with two additive time-varying delays via the extended reciprocally convex matrix inequality

Stability analysis of linear systems with two additive time-...

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第40届中国控制会议

作者： Chen Yan Chuan-Ke Zhang Xi-Zi Zhou School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Art and Design Wuhan University of Technology

This paper investigates the stability of linear systems with two additive time-varying delays. Firstly, an augmented Lyapunov-Krasovskii functional(LKF) is constructed by introducing a novel augmented term which contains the derivative of state vector into integral terms. Then the extended reciprocally convex matrix inequality together with the Wirtinger integral inequality is used to estimate the derivative of the proposed LKF. As a result, a stability criterion with less conservatism is established. Finally, the advantage of the stability criterion is demonstrated through a numerical example and the application of the stability criterion to stability analysis of load frequency control is illustrated.

关键词： linear system additive time-varying delays stability analysis reciprocally convex matrix inequality

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A Memristor Based Chaotic Oscillator for Weak Signal Detection and Its Circuitry Realization

Research Square

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research Square 2021年

作者： Dong, Kaifeng Xu, Kun Zhou, Youyou Zuo, Chao Wang, Leimin Zhang, Chuanke Jin, Fang Song, Junlei Mo, Wenqin Hui, Yajuan School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Wuhan Second Ship Design and Research Institute Wuhan430064 China

A new type of weak signal detection system that combines the memristor and Van der pol-Duffing chaotic system has been proposed in this paper, and the dynamic characteristics of the system are studied. It is observed that the system can change from a chaotic state to a periodic state under different driving force amplitudes. Moreover, as compared with several classical chaotic oscillators, the numerical simulation results show that the system has stronger anti-noise performance with the detectable signal-to-noise ratio reaches -163dB, and has a wider detection range. Its detection accuracy is up to 1 × 10−9. More importantly, this paper provides the circuit implementation scheme of the system, and the weak signal can be detected with our designed circuit. This may offer a possible way for weak signal detection system with good performance in anti-noise performance, detection range and accuracy. © 2021, CC BY.

关键词： Memristors

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Double Deep Q-learning for Anti-saturation Attack Problem of Warship Group

Double Deep Q-learning for Anti-saturation Attack Problem of...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Wei Li Gang Xiong Hao Tao Yunjun Han Fang Wang Xisong Dong Zhen Shen 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 Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences China China Ship Development and Design Center Wuhan China School of Control Science and Engineering (CSE) Shandong University Jinan China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

Anti-saturation attack (ASA) strategy is vital for the survival of a warship group, and attracts the focus of many researchers. In this paper, the dynamics of ASA is formulated as a Markov Decision Process (MDP) with an enhanced states space since those characters are involved, such as the formation and detection and interception areas of warship group. A reinforcement learning method (Double Deep Q-leaning, DDQN) is developed to solve the problem and deal with the curse of dimensionality whereby the cost-to-go value is calculated by a marine engagement simulation system. A heuristic defense algorithm guided by field expert knowledge is designed for comparison. The experimental results show that the DDQN method performs better in anti-saturation attack scenarios.

关键词： Industries Heuristic algorithms Digital twin Conferences Knowledge based systems Reinforcement learning Markov processes

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Emergence of steady quantum transport in a superconducting processor

arXiv

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arXiv 2024年

作者： Zhang, Pengfei Gao, Yu Xu, Xiansong Wang, Ning Dong, Hang Guo, Chu Deng, Jinfeng Zhang, Xu Chen, Jiachen Xu, Shibo Wang, Ke Wu, Yaozu Zhang, Chuanyu Jin, Feitong Zhu, Xuhao Zhang, Aosai Zou, Yiren Tan, Ziqi Cui, Zhengyi Zhu, Zitian Shen, Fanhao Li, Tingting Zhong, Jiarun Bao, Zehang Zhao, Liangtian Hao, Jie Li, Hekang Wang, Zhen Song, Chao Guo, Qiujiang Wang, H. Poletti, Dario School of Physics ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang Key Laboratory of Micro-nano Quantum Chips and Quantum Control Zhejiang University Hangzhou China Science Mathematics and Technology Cluster Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore College of Physics and Electronic Engineering Center for Computational Sciences Sichuan Normal University Chengdu610068 China Henan Key Laboratory of Quantum Information and Cryptography Zhengzhou450001 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore Centre for Quantum Technologies National University of Singapore Singapore117543 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore Singapore

Non-equilibrium quantum transport is crucial to technological advances ranging from nanoelectronics to thermal management. In essence, it deals with the coherent transfer of energy and (quasi-)particles through quantum channels between thermodynamic baths. A complete understanding of quantum transport thus requires the ability to simulate and probe macroscopic and microscopic physics on equal footing. Using a superconducting quantum processor, we demonstrate the emergence of non-equilibrium steady quantum transport by emulating the baths with qubit ladders and realising steady particle currents between the baths. We experimentally show that the currents are independent of the microscopic details of bath initialisation, and their temporal fluctuations decrease rapidly with the size of the baths, emulating those predicted by thermodynamic baths. The above characteristics are experimental evidence of pure-state statistical mechanics and prethermalisation in non-equilibrium many-body quantum systems. Furthermore, by utilising precise controls and measurements with single-site resolution, we demonstrate the capability to tune steady currents by manipulating the macroscopic properties of the baths, including filling and spectral properties. Our investigation paves the way for a new generation of experimental exploration of non-equilibrium quantum transport in strongly correlated quantum matter. © 2024, CC BY.

关键词： Statistical mechanics

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Self-supervised Detection and Pose Estimation of Logistical Objects in 3D Sensor Data

Self-supervised Detection and Pose Estimation of Logistical ...

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International Conference on Pattern Recognition

作者： Nikolas Müller Jonas Stenzel Jian-Jia Chen Plan-Based Robot Control German Research Center for Artificial Intelligence (DFKI) Osnabrück Germany Fraunhofer Institute for Material Flow and Logistics Dortmund Germany Institute of Computer Science Design Automation for Embedded Systems Technical University of Dortmund Dortmund Germany

Localization of objects in cluttered scenes with machine learning methods is a fairly young research area. Despite the high potential of object localization for full process automation in Industry 4.0 and logistical environments, 3D data sets for such applications to train machine learning models are not openly available and only few publications have been made on that topic. To the authors knowledge, this is the first publication that describes a self-supervised and fully automated deep learning approach for object pose estimation using simulated 3D data. The solution covers the simulated generation of training data, the detection of objects in point clouds using a fully convolutional voting network and the computation of the pose for each detected object instance.

关键词： Location awareness Solid modeling Three-dimensional displays Pose estimation Robot vision systems Training data Object detection

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Deep Learning-Based Object Pose Estimation: A Comprehensive Survey

arXiv

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arXiv 2024年

作者： Liu, Jian Sun, Wei Yang, Hui Zeng, Zhiwen Liu, Chongpei Zheng, Jin Liu, Xingyu Rahmani, Hossein Sebe, Nicu Mian, Ajmal The National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China The School of Architecture and Art Central South University Changsha410082 China The Department of Automation Tsinghua University Beijing100084 China The School of Computing and Communications Lancaster University LA1 4YW United Kingdom The Department of Information Engineering and Computer Science University of Trento Trento38123 Italy The Department of Computer Science The University of Western Australia WA6009 Australia

Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled training data, model compactness, robustness under challenging conditions, and their ability to generalize to novel unseen objects. A recent survey discussing the progress made on different aspects of this area, outstanding challenges, and promising future directions, is missing. To fill this gap, we discuss the recent advances in deep learning-based object pose estimation, covering all three formulations of the problem, i.e., instance-level, category-level, and unseen object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks, providing the readers with a holistic understanding of this field. Additionally, it discusses training paradigms of different domains, inference modes, application areas, evaluation metrics, and benchmark datasets, as well as reports the performance of current state-of-the-art methods on these benchmarks, thereby facilitating the readers in selecting the most suitable method for their application. Finally, the survey identifies key challenges, reviews the prevailing trends along with their pros and cons, and identifies promising directions for future research. We also keep tracing the latest works at Awesome-Object-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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