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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Zhiqiang Han Jun Gong Yu Qiao School of Vehicle and Transportation Engineering Taiyuan University of Science and Technology Taiyuan China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

The utilization of intelligent vehicles for detecting road anomalies has become a relatively common phenomenon. The existing road disease detection workload is large, the robustness is not high, and small diseases are easily ignored, which brings huge challenges to road disease detection. A road disease detection model named RDDS based on YOLOv5 is proposed. Based on the YOLOv5 algorithm, we incorporate Transformer encoder blocks into the head part to form Transformer Prediction Heads (TFE), expanding their number to four. This enhancement aims to improve the model's capability to detect small targets and road diseases of different scales. In addition, we introduce the convolutional block attention model (CBAM) to make the network pay more attention to important channels and spatial locations, thereby improving the performance and generalization ability of the model. Aiming at the problems of low resolution and insufficient data volume in current road disease data sets, we propose a new road disease detection data set RDDSC. Finally, we conducted experiments on the RDDSC data set. The experimental results showed that compared with the original YOLOv5, the RDDS parameters increased slightly, while the detection accuracy was significantly improved. Compared with existing detection algorithms, RDDS has obvious advantages in road disease detection.

关键词： YOLO Accuracy Roads Predictive models Transformers Prediction algorithms Robustness Maintenance Spatial resolution Diseases

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Feedback linearization and equivalent-disturbance compensation control strategy for piezoelectric stage

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Nanotechnology and Precision engineering 2024年第2期7卷 49-59页

作者： Tao Huang Yingbin Wang Zhihong Luo Huajun Cao Guibao Tao Mingxiang Ling State Key Laboratory of Mechanical Transmissions for Advanced Equipment Chongqing UniversityChongqing 400044China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing 400044China Hongdu Aviation Industry Group NanchangJiangxi 330000China Robotics and Microsystems Center Soochow UniversitySuzhou 215123China

Piezoelectric stages use piezoelectric actuators and flexure hinges as driving and amplifying mechanisms,*** systems have high positioning accuracy and high-frequency responses,and they are widely used in various precision/ultra-precision positioning ***,the main challenge with these devices is the inherent hysteresis nonlinearity of piezoelectric actuators,which seriously affects the tracking accuracy of a piezoelectric *** by this challenge,in this work,we developed a Hammerstein model to describe the hysteresis nonlinearity of a piezoelectric *** particular,in our proposed scheme,a feedback-linearization algorithm is used to eliminate the static hysteresis *** addition,a composite controller based on equivalent-disturbance compensation was designed to counteract model uncertainties and external *** analysis of the stability of a closed-loop system based on this feedback-linearization algorithm and composite controller was performed,and this was followed by extensive comparative experiments using a piezoelectric stage developed in the *** experimental results confirmed that the feedback-linearization algorithm and the composite controller offer improved linearization and trajectory-tracking performance.

关键词： Piezoelectric stage Hysteresis nonlinearity Feedback linearization Equivalent-disturbance compensation

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Robust State Estimation for Legged Robots with Dual Beta Kalman Filter

arXiv

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arXiv 2024年

作者： Zhang, Tianyi Cao, Wenhan Liu, Chang Zhang, Tao Li, Jiangtao Li, Shengbo Eben The School of Vehicle and Mobility Tsinghua University Beijing China The State Key Laboratory of Intelligent Green Vehicle and Mobility Tsinghua University Beijing China The Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing China SunRising AI Ltd The School of Vehicle and Mobility College of Artificial Intelligence Tsinghua University Beijing China

Existing state estimation algorithms for legged robots that rely on proprioceptive sensors often overlook foot slippage and leg deformation in the physical world, leading to large estimation errors. To address this limitation, we propose a comprehensive measurement model that accounts for both foot slippage and variable leg length by analyzing the relative motion between foot contact points and the robot’s body center. We show that leg length is an observable quantity, meaning that its value can be explicitly inferred by designing an auxiliary filter. To this end, we introduce a dual estimation framework that iteratively employs a parameter filter to estimate the leg length parameters and a state filter to estimate the robot’s state. To prevent error accumulation in this iterative framework, we construct a partial measurement model for the parameter filter using the leg static equation. This approach ensures that leg length estimation relies solely on joint torques and foot contact forces, avoiding the influence of state estimation errors on the parameter estimation. Unlike leg length which can be directly estimated, foot slippage cannot be measured directly with the current sensor configuration. However, since foot slippage occurs at a low frequency, it can be treated as outliers in the measurement data. To mitigate the impact of these outliers, we propose the β-Kalman filter (β-KF), which redefines the estimation loss in canonical Kalman filtering using β-divergence. This divergence can assign low weights to outliers in an adaptive manner, thereby enhancing the robustness of the estimation algorithm. These techniques together form the dual β-Kalman filter (Dual β-KF), a novel algorithm for robust state estimation in legged robots. Experimental results on the Unitree GO2 robot demonstrate that the Dual β-KF significantly outperforms state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Kalman filters

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Reinforcement Learning-based Fault-Tolerant Control for Unmanned Aerial vehicles

Reinforcement Learning-based Fault-Tolerant Control for Unma...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Guoqi Wang Xudong Wang Yang Li Ziwei Gao Zhaoke Ning National Engineering Research Center of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle Engineering Hunan University Changsha China School of Aeronautics and Astronautics Sichuan University Chengdu China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

This paper focuses on the fault-tolerant control (FTC) problem for unmanned aerial vehicles (UAVs) subject to possible multiple actuator failures, which is a tough problem to solve with traditional FTC methods since they usually require accurate mathematical models. To address the limitation of traditional FTC methods, a model-free FTC approach is proposed based on reinforcement learning (RL). Subsequently, the proposed approach is applied to construct fault-tolerant controller for UAVs without any knowledge of the quadrotor dynamic information. Then, an end-to-end control policy that can tolerant actuator failures is obtained, which can map the state of the UAVs directly to the control commands of the four rotors after learning. Finally, the effectiveness of the proposed fault-tolerant approach is demonstrated by using the flexible modular quadrotor simulator.

关键词： Fault tolerance Actuators Trajectory tracking Fault tolerant systems Autonomous aerial vehicles Stability analysis Mathematical models

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MMG-HCI: A Non-contact Non-intrusive Real-Time Intelligent Human-Computer Interaction System 1st

MMG-HCI: A Non-contact Non-intrusive Real-Time Intelligent H...

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Gong, Peixian Wang, Chunyu Zhang, Lihua Institute of AI and Robotics Fudan University Shanghai China Ji Hua Laboratory Foshan China Shanghai Engineering Research Center of AI and Robotics Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Beijing China Engineering Research Center of AI and Unmanned Vehicle Systems of Jilin Province Changchun China

ISBN: (纸本)9783030930455

With the continuous development of science and technology, human-computer interaction (HCI) technology has been paid more and more attention by researchers. Traditional HCI methods, such as torch-screen, speech recognition and so on, have the problem of inconvenient application in some scenarios. The research of HCI technology based on millimeter-wave radar makes up for the deficiency of current HCI, and plays a very important role in its development. The main tasks of this paper are as follows: 1, Design of non-contact and non-invasive real-time human activity recognition system MMG-HCI. By analyzing the working features of millimeter-wave radar and combining them with the principle of computer multi-process synchronization, we designed a real-time system which integrates millimeter-wave radar data acquisition, point cloud data visualization, deep learning model inference and HCI. 2, Construction of millimeter-wave radar gesture dataset. Since there is no suitable open-source dataset at present, we collect and build our own dataset through the real-time system. 3, Training graph neural network (GNN) for HAR based on millimeter-wave radar. In order to classify the mmWave radar point cloud more accurately, we use the best performance MMPointGNN as the deep learning inference model of a real-time system. Experiments and demonstration show that we have designed an efficient, accurate, non-contact and non-invasive real-time HCI system. © 2021, Springer Nature Switzerland AG.

关键词： Human computer interaction

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Two Tasks, One Goal: Uniting Motion and Planning for Excellent End To End Autonomous Driving Performance

arXiv

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arXiv 2025年

作者： Liu, Lin Song, Ziying Pan, Hongyu Yang, Lei Jia, Caiyan Luo, Y. Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence Beijing Jiaotong University China Horizon Robotics China School of Information Technology and Electrical Engineering The University of Queensland Australia School of Vehicle and Mobility Tsinghua University China

End-to-end autonomous driving has made impressive progress in recent years. Former end-to-end autonomous driving approaches often decouple planning and motion tasks, treating them as separate modules. This separation overlooks the potential benefits that planning can gain from learning out-of-distribution data encountered in motion tasks. However, unifying these tasks poses significant challenges, such as constructing shared contextual representations and handling the unobservability of other vehicles’ states. To address these challenges, we propose TTOG, a novel two-stage trajectory generation framework. In the first stage, a diverse set of trajectory candidates is generated, while the second stage focuses on refining these candidates through vehicle state information. To mitigate the issue of unavailable surrounding vehicle states, TTOG employs a self-vehicle data-trained state estimator, subsequently extended to other vehicles. Furthermore, we introduce ECSA (equivariant context-sharing scene adapter) to enhance the generalization of scene representations across different agents. Experimental results demonstrate that TTOG achieves state-of-the-art performance across both planning and motion tasks. Notably, on the challenging open-loop nuScenes dataset, TTOG reduces the L2 distance by 36.06%. Furthermore, on the closed-loop Bench2Drive dataset, our approach achieves a 22% improvement in the driving score (DS), significantly outperforming existing baselines. Copyright © 2025, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Understanding driver cognition and decision-making behaviors in high-risk scenarios: A drift diffusion perspective

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Accident Analysis and Prevention 2025年 220卷 108123页

作者： Huang, Heye Li, Zheng Cheng, Hao Wang, Haoran Jiang, Junkai Li, Xiaopeng Zgonnikov, Arkady Department of Civil and Environmental Engineering University of Wisconsin-Madison WI53706 United States School of Vehicle and Mobility Tsinghua University Beijing100084 China Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai200092 China Department of Cognitive Robotics Delft University of Technology Delft2628 Netherlands

Ensuring safe interactions between autonomous vehicles (AVs) and human drivers in mixed traffic systems remains a major challenge, particularly in complex, high-risk scenarios. This paper presents a cognition-decision framework that integrates individual variability and commonalities in driver behavior to quantify risk cognition and model dynamic decision-making. First, a risk sensitivity model based on a multivariate Gaussian distribution is developed to characterize individual differences in risk cognition. Then, a cognitive decision-making model based on the drift diffusion model (DDM) is introduced to capture common decision-making mechanisms in high-risk environments. The DDM dynamically adjusts decision thresholds by integrating initial bias, drift rate, and boundary parameters, adapting to variations in speed, relative distance, and risk sensitivity to reflect diverse driving styles and risk preferences. By simulating high-risk scenarios with lateral, longitudinal, and multidimensional risk sources in a driving simulator, the proposed model accurately predicts cognitive responses and decision behaviors during emergency maneuvers. Specifically, by incorporating driver-specific risk sensitivity, the model enables dynamic adjustments of key DDM parameters, allowing for personalized decision-making representations in diverse scenarios. Comparative analysis with IDM, Gipps, and MOBIL demonstrates that DDM more precisely captures human cognitive processes and adaptive decision-making in high-risk scenarios. These findings provide a theoretical basis for modeling human driving behavior and offer critical insights for enhancing AV-human interaction in real-world traffic environments. © 2025 Elsevier Ltd

关键词： Automobile driver simulators

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Design and Analysis of Individually Demagnetized Power Converter for Switched Reluctance Motors

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 8439-8449页

作者： Liu, Hemiao Chen, Hao Wang, Xing Gorbounov, Yassen Wheeler, Patrick Anuchin, Alecksey Demidova, Galina Korovkin, Nikolay Liudmila, Sakhno Shenzhen Research Institute China University of Mining and Technology Shenzhen515100 China China University of Mining and Technology School of Electrical Engineering Xuzhou221116 China Xinjiang Institute of Engineering Ürümqi830000 China International Joint Research Center of Central and Eastern European Countries on New Energy Electric Vehicle Technology and Equipment Xuzhou221008 China International Cooperation Joint Laboratory of New Energy Power Generation and Electric Vehicles of Jiangsu Province Colleges and Universities Xuzhou221008 China Xuzhou Key Laboratory of New Energy Electric Vehicle Technology and Equipment Xuzhou221008 China Mining Production University of Mining and Geology "St. Ivan Rilski " Department of Automation Sofia1700 Bulgaria The University of Nottingham Power Electronics Machines and Control Group NottinghamNG7 2RD United Kingdom Electric Drives Department Moscow111250 Russia Saint-Petersburg National Research University of Information Technologies Mechanics and Optics Faculty of Control Systems and Robotics Engineer Saint Petersburg197101 Russia Peter the Great St. Petersburg Polytechnic University Institute of Energy Saint Petersburg195251 Russia

Switched reluctance motors (SRMs) are capable of operating at high speeds and high loads. The large back EMF results in the excitation winding current being demagnetized more slowly, which, in turn, reduces the efficiency and output of the motor system. This study proposes a novel individually demagnetization power converter (IDPC) specifically designed for SRMs to effectively use the demagnetization energy through an independent demagnetization circuit. Compared with traditional converters, the IDPC can accept direct current (dc) and high-frequency ac inputs while achieving fast demagnetization and energy recovery. The topology and working principle of the IDPC are analyzed in detail in this article using a three-phase 12/8 SRM as the research object. In addition, the influence of the winding demagnetization voltage on the negative torque during the demagnetization phase was studied, as well as the functional implementation and logic control strategy of the IDPC. Simulation and experimental results confirm the validity of the proposed topology and high-frequency ac drive characteristics, as well as their advantages in terms of efficiency, output capacity, and dynamic performance. © 2015 IEEE.

关键词： Reluctance motors

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Improved YOLOv5s Network for Traffic Object Detection with Complex Road Scenes

Improved YOLOv5s Network for Traffic Object Detection with C...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Yi Cao Yuning Wang Huijie Fan Faculty of Vehicle Engineering and Mechanics Dalian University of Technology Da Lian China Unit 32681 PLA Tieling China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

Aiming at the problems of low target detection accuracy and prone to false detection and missed detection in complex traffic scenes, a road target detection algorithm based on improved YOLOv5s is proposed. Firstly, a feature extraction layer based on Spatial Pyramid Pooling(SPP) is adopted for increasing shallow feature receptive fields named as Multi-scale SPP module (M-SPP). M-SPP is designed to fully integrate feature information of different scales for improving feature extraction ability of our network. Secondly, a Global Context Attention Module(GCAM) is proposed, in focusing on important features in channel and spatial dimensions, allowing the network to focus on more useful information. Finally, experimental results show that detection accuracy of the improved YOLOv5s model we proposed on the UA-DETRAC dataset, compared with the original algorithm, improved by 1.83% and 2.54% respectively. Our improved model abtain good detection accuracy while ensuring real-time performance, and solves problem of missed detection and false detection of small targets in complex road scenes to a certain extent.

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Nonlinear Bayesian Filtering with Natural Gradient Gaussian Approximation

arXiv

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arXiv 2024年

作者： Cao, Wenhan Zhang, Tianyi Sun, Zeju Liu, Chang Yau, Stephen S.-T. Li, Shengbo Eben School of Vehicle and Mobility Tsinghua University Beijing China State Key Laboratory of Intelligent Green Vehicle and Mobility Tsinghua University Beijing China Department of Mathematical Sciences Tsinghua University Beijing China Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing China The Department of Mathematical Sciences Tsinghua University Beijing China School of Vehicle and Mobility College of Artificial Intelligence Tsinghua University Beijing China

Practical Bayes filters often assume the state distribution of each time step to be Gaussian for computational tractability, resulting in the so-called Gaussian filters. When facing nonlinear systems, Gaussian filters such as extended Kalman filter (EKF) or unscented Kalman filter (UKF) typically rely on certain linearization techniques, which can introduce large estimation errors. To address this issue, this paper reconstructs the prediction and update steps of Gaussian filtering as solutions to two distinct optimization problems, whose optimal conditions are found to have analytical forms from Stein’s lemma. It is observed that the stationary point for the prediction step requires calculating the first two moments of the prior distribution, which is equivalent to that step in existing moment-matching filters. In the update step, instead of linearizing the model to approximate the stationary points, we propose an iterative approach to directly minimize the update step’s objective to avoid linearization errors. For the purpose of performing the steepest descent on the Gaussian manifold, we derive its natural gradient that leverages Fisher information matrix to adjust the gradient direction, accounting for the curvature of the parameter space. Combining this update step with moment matching in the prediction step, we introduce a new iterative filter for nonlinear systems called Natural Gradient Gaussian Approximation filter, or NANO filter for short. We prove that NANO filter locally converges to the optimal Gaussian approximation at each time step. Furthermore, the estimation error is proven exponentially bounded for nearly linear measurement equation and low noise levels through constructing a supermartingale-like property across consecutive time steps. Real-world experiments demonstrate that, compared to popular Gaussian filters such as EKF, UKF, iterated EKF, and posterior linearization filter, NANO filter reduces the average root mean square error by approximate

关键词： Extended Kalman filters

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