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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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Visual Explanation of Object Detectors via Saliency Maps

Visual Explanation of Object Detectors via Saliency Maps

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Jin Xiao Wenrui Liu Xiaoguang Hu Hao Jiang Weipeng Wang Li Yahang State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Beihang University Beijing China The Third Academy of CASIC Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

In recent years, rapid progress of deep learning has resulted in object detection algorithms based on deep neural networks nearly completely replacing traditional methods. This shift has led to significant improvements in both accuracy and speed of object detection. However, deep neural networks that support decision-making inobject detection are complex black boxes with hidden internal logic and operations. This lack of transparency results in deep learning models being unexplainable, which limits their practical deployment. In this paper, we analyze the current state and future trends of interpretable technology in object detection and propose an effective object detection interpretable algorithm. This algorithm aims to explain the underlying logic of AI decision-making and provide support for the high performance and credibility of object detection algorithms based on deep neural networks through quantitative evaluation indicators.

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Optimal consensus control for partially unknown multi-agent systems with switching communication topology*

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IFAC-PapersOnLine 2023年第2期56卷 1210-1216页

作者： Wenpeng He Xin Chen Menglin Zhang Yipu Sun Akinori Sekiguchi Jinhua She School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China School of Engineering Tokyo University of Technology Hachioji Tokyo 192-0982 Japan

Since the local neighborhood tracking error system for switching multi-agent systems (MASs) is time-varying, the existing optimal consensus method for fixed MASs cannot be used in switching MASs. This paper proposes a new approach to solve the optimal consensus problems of linear switching MASs. Firstly, a new augment local neighborhood tracking error (ALNTE) system, which is based on the joint communication topology, is developed to construct the control object, and the open-loop estimation method is used to estimate the ALNTE for individuals. Secondly, based on the ALNTE system, the performance index is designed, and the value iteration method is used to solve the coupled Hamilton-Jacobi-Bellman equations and obtain the optimal control. Finally, theoretical analysis is given to prove the learning stability and control stability of the value iteration method, and a numerical simulation is used to verify the effectiveness of the proposed method.

关键词： Switching topology multi-agent systems (MASs) optimal control value iteration augment local neighborhood tracking error (ALNTE)

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Exploring Data Geometry for Continual Learning

Exploring Data Geometry for Continual Learning

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhi Gao Chen Xu Feng Li Yunde Jia Mehrtash Harandi Yuwei Wu Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Electrical and Computer Systems Eng. Monash University and Data61 Australia

Continual learning aims to efficiently learn from a non-stationary stream of data while avoiding forgetting the knowledge of old data. In many practical applications, data complies with non-Euclidean geometry. As such, the commonly used Euclidean space cannot gracefully capture non-Euclidean geometric structures of data, leading to in-ferior results. In this paper, we study continual learning from a novel perspective by exploring data geometry for the non-stationary stream of data. Our method dynamically expands the geometry of the underlying space to match growing geometric structures induced by new data, and pre-vents forgetting by keeping geometric structures of old data into account. In doing so, making use of the mixed cur-vature space, we propose an incremental search scheme, through which the growing geometric structures are en-coded. Then, we introduce an angular-regularization loss and a neighbor-robustness loss to train the model, capa-ble of penalizing the change of global geometric structures and local geometric structures. Experiments show that our method achieves better performance than baseline methods designed in Euclidean space.

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Temperature Compensation Method of Fluxgate Sensor Based on Polynomial Fitting

arXiv

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arXiv 2024年

作者： Yang, Ruiping Liu, Huan Ge, Jian Chugo, Daisuke Dong, Haobin 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 Faculty of Engineering Kwansai Gakuin University Osaka669-1330 Japan

Fluxgate sensors are widely used in the field of low frequency and weak vector magnetic field measurement because of their good performance, such as high resolution and low power consumption. However, during the long-term continuous observation, the drift errors of the fluxgate sensor will occur due to the variable ambient temperature. This paper proposes a temperature compensation method for fluxgate sensors based on polynomial fitting. First, a physical model of the temperature & fluxgate sensor was established on the COMSOL Multiphysics simulation platform, and the influence of temperature on the measurement performance of the fluxgate sensor was analyzed. Second, according to the existing temperature-magnetic field data, a temperature compensation model of the fluxgate sensor was constructed. And compared it with other temperature compensation method, the result shows that the proposed temperature compensation method is relatively simple and can better achieve real-time compensation for sensor application scenarios. Finally, to verify the effectiveness of the proposed method, numerous laboratory experiments were *** temperature drift is reduced from more than 500 nT before compensation to about 1 nT. The results show that the proposed method has a good temperature compensation effect on the data measured by the fluxgate sensor within a variable temperature background. Copyright © 2024, The Authors. All rights reserved.

关键词： Simulation platform

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Trajectory Planning and Tracking of Hybrid Flying-Crawling Quadrotors

arXiv

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arXiv 2023年

作者： Hu, Dongnan Xia, Ruihao Jin, Xin Tang, Yang Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China

Hybrid Flying-Crawling Quadrotors (HyFCQs) are transformable robots with the ability of terrestrial and aerial hybrid motion. This article presents a trajectory planning and tracking framework designed for HyFCQs. In this framework, a terrestrial-aerial path-searching method with the crawling limitation of HyFCQs is proposed to guarantee the dynamical feasibility of trajectories. Additionally, a trajectory tracking method is proposed to address the challenges associated with the deformation time required by HyFCQs, which makes tracking hybrid trajectories at the junction between terrestrial and aerial segments difficult. Simulations and real-world experiments in diverse scenarios validate the exceptional performance of the proposed approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Antennas

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Transformer-based Joint Source Channel Coding for Textual Semantic Communication

Transformer-based Joint Source Channel Coding for Textual Se...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Liu, Shicong Gao, Zhen Chen, Gaojie Su, Yu Peng, Lu Beiing Institute of Technology Miit Key Laboratory of Complex-Field Intelligent Sensing Beijing100081 China Beijing Institute of Technology Advanced Research Institute of Multidisciplinary Science Jinan250307 China Yangtze Delta Region Academy of Beijing Institute of Technology Beijing Institute of Technology Jiaxing314000 China Institute for Communication Systems University of Surrey Guildford United Kingdom Institute of Research and Development Chengdu610000 China

ISBN: (纸本)9798350345384

The Space-Air-Ground-Sea integrated network calls for more robust and secure transmission techniques against jamming. In this paper, we propose a textual semantic transmission framework for robust transmission, which utilizes the advanced natural language processing techniques to model and encode sentences. Specifically, the textual sentences are firstly split into tokens using wordpiece algorithm, and are embedded to token vectors for semantic extraction by Transformer-based encoder. The encoded data are quantized to a fixed length binary sequence for transmission, where binary erasure, symmetric, and deletion channels are considered for transmission. The received binary sequences are further decoded by the transformer decoders into tokens used for sentence reconstruction. Our proposed approach leverages the power of neural networks and attention mechanism to provide reliable and efficient communication of textual data in challenging wireless environments, and simulation results on semantic similarity and bilingual evaluation understudy prove the superiority of the proposed model in semantic transmission. © 2023 IEEE.

关键词： Semantics

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SDN routing optimization based on improved Reinforcement learning

SDN routing optimization based on improved Reinforcement lea...

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2020 International Conference on Cyberspace Innovation of Advanced Technologies, CIAT 2020

作者： Lu, Xiaoye Chen, Junyan Lu, Liaochuo Huang, Xuefeng Lu, Xiantao Guangxi Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781450387828

In recent years, the application of new network architecture SDN combined with reinforcement learning in the field of artificial intelligence is more and more extensive, considering the diversity and complexity of traffic transmission. This paper proposes a DDPG-EREP(Enhanced and renewable experience pool) algorithm that dynamically plans the experience pool capacity and sampling size according to the current iteration number, and can update the experience in time, and applies it to SDN routing optimization. In addition, good results have been achieved through experiments, including better training speed and better routing optimization of SDN. © 2020 ACM.

关键词： Reinforcement learning

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Disturbance Observer-Based Control of Weight on Bit in Drilling Process with Formation Variation

Disturbance Observer-Based Control of Weight on Bit in Drill...

引用

Asian Control Conference

作者： Shipeng Chen Wangnian Li Chengda Lu Yang Zhou Weihua Cao Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China CCTEG Xi'an Research Institute (Group) Co. Ltd. Xi'an China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In deep geological drilling, encountering various formations is inevitable. Formation changes can lead to fluc-tuations in the weight on bit due to the bit-rock interaction. This paper proposes a disturbance observer-based weight on bit control system to mitigate the adverse effects of formation changes on system performance. First, an axis-torsion coupled dynamic model is established, and the relationship between the two dimensions is analyzed. The validity of the model is confirmed through comparison with field data collected from a geothermal well drilling operation. Then, a disturbance observer is designed to estimate and compensate for the disturbance at the control input, thereby reducing weight on bit fluctuations caused by formation variations. Simulation results considering formation variation during drilling verify the effectiveness of the proposed method.

关键词： Drilling Simulation System performance Geology Process control Control systems Disturbance observers

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Optimizing Bounding Box Regression in complex Backgrounds

Optimizing Bounding Box Regression in Complex Backgrounds

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Chinese Automation Congress (CAC)

作者： Jun Chen Haiyan Zhang Anjun Yu Yiwei Wang 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 Automation China University of Geosciences Wuhan China Jiangxi Ganyue Expressway Co. Ltd. Nanchang China School of Automation China University of Geosciences Wuhan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In the context of electrical power operation sites, the automated and accurate detection of whether workers are properly equipped with safety gear, such as safety clothing, helmets, and safety ropes for high-altitude operations, is crucial for ensuring the safety of personnel. Traditional object detection models often struggle to accurately identify these critical pieces of safety equipment in complex environments, due to variable object sizes, overlapping objects, and substantial background noise. To specifically address these challenges, we propose a novel loss function-Enhanced Alignment Intersection over Union (EAIoU) loss. This loss function is designed to enhance the model's ability to discriminate between closely spaced and overlapping safety gear in real-time, significantly improving robustness against scale variations and complex backgrounds. In experiments based on the YOLOv8s architecture, our model achieved a mean Average Precision (mAP) of 92.1 % on a dataset specific to electrical power operations, markedly outperforming traditional IoU loss functions. Additionally, EAIoU demonstrated improved performance on the COCO dataset, indicating its generalization capability across various complex scenarios. This research not only enhances the accuracy and real-time performance of safety equipment detection at electric power operation sites but also opens new avenues for advanced object detection technology in complex, multi-object environments.

关键词： Accuracy Gears Shape Object detection Robustness Real-time systems Safety Power systems Personnel Object recognition

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