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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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An overview on the designs of distributed observers in LTI multi-agent systems

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Science China(Technological Sciences) 2021年第11期64卷 2337-2346页

作者： PEI YingQing GU HaiBo LIU KeXin LU JinHu Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China

With the rapid development of information technologies and cloud computing, sensor networks play an increasingly important role in our society. Over the past few decades, distributed observer theory has attracted unprecedented attention due to its wide potential applications in different areas. Meanwhile, various approaches and algorithms have been proposed and investigated. The design of distributed observers is one of the frontier topics of system and control research, which has the significant theoretical values and broad application prospects. This paper attempts to review the representative models and the corresponding approaches for distributed observer design in linear time-invariant(LTI) systems. Firstly, the research backgrounds and main advances of distributed observer designs are briefly reviewed. Then, recent results of distributed observer designs for discrete-time and continuous-time LTI multi-agent systems(MASs) are introduced in detail, respectively. Finally, the prospects and the future work directions of the design of distributed observers are put forward. The main purpose of this paper is to promote the emerging topic on the designs of distributed observers, with focuses on the interdisciplinary interest from technological sciences.

关键词： multi-agent system linear time-invarient system distributed observer consensus

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Motion Forecasting for Autonomous Vehicles: A Survey

arXiv

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arXiv 2025年

作者： Shi, Jianxin Chen, Jinhao Wang, Yuandong Sun, Li Liu, Chunyang Xiong, Wei Wo, Tianyu Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Information Engineering College Capital Normal University Beijing100048 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China Didi Chuxing Beijing100094 China

In recent years, the field of autonomous driving has attracted increasingly significant public interest. Accurately forecasting the future behavior of various traffic participants is essential for the decision-making of Autonomous Vehicles (AVs). In this paper, we focus on both scenario-based and perception-based motion forecasting for AVs. We propose a formal problem formulation for motion forecasting and summarize the main challenges confronting this area of research. We also detail representative datasets and evaluation metrics pertinent to this field. Furthermore, this study classifies recent research into two main categories: supervised learning and self-supervised learning, reflecting the evolving paradigms in both scenario-based and perception-based motion forecasting. In the context of supervised learning, we thoroughly examine and analyze each key element of the methodology. For self-supervised learning, we summarize commonly adopted techniques. The paper concludes and discusses potential research directions, aiming to propel progress in this vital area of AV technology. © 2025, CC BY.

关键词： Self-supervised learning

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Viewing Pattern Assisted Proactive Partial Caching for 360-degree Videos in MEC Networks

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IEEE Internet of Things Journal 2025年

作者： Yin, Guoxiao Zhou, Xiaotian Zhang, Haixia Li, Dongyang Yuan, Dongfeng Shandong University School of Control Science and Engineering Jinan250061 China Shandong University Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Shandong Jinan China College of Oceanography and Space Informatics Qingdao266580 China Shandong University School of Qilu Transportation Jinan250002 China

Caching 360-degree videos at the network edge can reduce user content request latency and mitigate transmission congestion in backbone networks. Given the fact that user only views a part of content of 360-degree scope at any time, caching the entire video is resource inefficient. To address this, we focus a Multi-access Edge computing (MEC)-based 360-degree video service system, where the edge server only caches a portion of each video that is most likely falling in the Field of View (FoV) of users. To minimize the average video request latency of all users in the system, we formulate a large-scale 0-1 knapsack problem, which is NP-hard. To tackle it, we proposed a heuristic algorithm where the user viewing patterns extracted from the historical request information are taken into account. Specifically, we first design a cascading cache space allocation method to assign the total cache space of edge server to each segment of videos. After that, the original problem is decomposed into several small-scale yet individual tile caching subproblems with compressed solution space. Then, they are solved by using the dynamic programming algorithm with moderate complexity. To further enhance the caching performance, the PSO-based algorithm is designed to fine tune the parameters involved in the proposed caching algorithm. In addition, we introduce a content-based method to calculate the request probability of the newly generated videos. The effectiveness of the proposed algorithm is evaluated through simulations based on a real world dataset, where the results demonstrate a substantial improvement in both video request latency and cache hit rate compared to the benchmark methods. © 2014 IEEE.

关键词： Heuristic algorithms

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Trajectory Tracking of AUV under Current Disturbance Based on Adaptive LADRC

Trajectory Tracking of AUV under Current Disturbance Based o...

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Automation, Robotics and Computer engineering (ICARCE), International Conference on

作者： Daxiong Ji Xinwei Wang Minghui Xu Qiang Zhang Sanjay Sharma Robert Sutton Institute of Marine Electronics & Intelligent Systems Ocean Collage Zhejiang University Donghai Lab Zhoushan China Intelligent Autonomous Control Systems School of Engineering Computing and Mathematics University of Plymouth Plymouth UK

In consideration of the trajectory tracking problem of autonomous underwater vehicle (AUV) under external current disturbance, an adaptive linear active disturbance rejection control (LADRC) method is proposed. Unlike the conditional LADRC using fixed parameters for extended state observer, the proposed controller makes use of the error of estimation and automatically adjusts the parameter online. As shown in the simulation, the adaptive LADRC estimates the external current disturbance effectively, and tracks the 3D trajectory better compared with traditional LADRC.

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Image Reconstruction for Lensless Imaging Using a Phase Congruency Perception Model

Image Reconstruction for Lensless Imaging Using a Phase Cong...

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Chinese Automation Congress (CAC)

作者： Yibin Tian Dajiang Lu Hong Deng Zhiyuan Zhang Xiaopin Zhong Zongze Wu College of Mechatronics and Control Engineering Shenzhen University Shenzhen China R&D Division Litemaze Technology Hangzhou China School of Computing and Information Systems Singapore Management University Singapore Singapore

Lensless imaging systems capture images without the need for traditional lenses. Instead, they employ flat and thin micro-optical devices, such as masks, diffusers, Fresnel zone plates, and other diffractive optical elements, to capture information-encoded images of imaging targets, and rely on advanced computational methods to reconstruct regular images from the encoded ones. Typical image reconstructions in lensless imaging utilize compressive sensing and sophisticated iterative optimization algorithms. Recently such iterative methods have been replaced by deep neural networks. However, in both approaches the cost (or loss) functions usually are defined on certain simple mathematical distance metric, without considering the inherent perceptive properties of images. In this report, a new method is proposed to construct cost functions for image reconstruction in lensless imaging. It is based on a visual perception model of image phase congruency, a measure of the consistency of the phase information across different spatial orientations and scales in images. The proposed method is demonstrated to provide improved image reconstruction quality using more than 200 encoded images from a virtual lensless imaging system with a Fresnel zone plate, as well a small number of encoded images from a physical one with an optical diffuser.

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Parks-McClellan Algorithm-Based Active Damping Strategy Applied to Grid-Connected Inverters

Parks-McClellan Algorithm-Based Active Damping Strategy Appl...

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IEEE Annual Southern Power Electronics Conference (SPEC)

作者： José de Arimatéia O. Filho Gabriel Lucas Bressanini Tiago Davi Curi Busarello Luis De Oro Arenas Paulo Fernando Silva Helmo Kelis Morales Paredes Group of Automation and Integratd Systems (GASI) São Paulo State University (Unesp) Sorocaba Brazil Department of Control Automation and Computing Engineering Federal University of Santa Catarina (UFSC) Blumenau Brazil

In recent years, grid-connected inverters have gained wide prominence, primarily attributed to the surge in distributed energy resources. To ensure the reliable operation of these inverters, it is crucial to adequately damp the resonance peak of the LCL filter at the inverter output. By achieving this, the grid-connected inverter guarantees the system's stability while minimizing the harmonic content in the current injected into the distribution grid. To address this issue, this paper proposes the utilization of digital differentiators in conjunction with optimized wideband differentiators, which are designed using the Parks-McClellan (PM) algorithm. These differentiators are applied specifically to the LCL filter capacitor voltage to dampen the resonance peak effectively. Simulation results univocally demonstrate the effectiveness of the proposed approach.

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Adaptive neural tracking control for nonlinear servo system with unknown time-varying parameters and uncertainties

Adaptive neural tracking control for nonlinear servo system ...

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第42届中国控制会议

作者： Faxiang Zhang Guanbin Gao Jing Na Jiande Wu Faculty of Mechanical and Electrical Engineering and Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University

This paper solves the neural network(NN) tracking control problem for uncertain nonlinear servo system with timevarying parameters and *** address the uncertain nonlinear function with unknown time-varying parameters(i.e.,unknown nonlinear spatiotemporal function),the time-varying parameter extraction method is used to separate the time-varying puameters from uncertain nonlinear spatiotemporal function,which yields an unknown state-based boundary *** the tools of NN and adaptive technology,an adaptive neural tracking controller is designed,which guarantees the uniformly ultimately bounded(UUB) performance of the resulting closed-loop *** effectiveness of the designed method is verified by simulations.

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The Impact of Recommendation Systems on Opinion Dynamics: Microscopic Versus Macroscopic Effects

The Impact of Recommendation Systems on Opinion Dynamics: Mi...

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IEEE Conference on Decision and control

作者： Nicolas Lanzetti Florian Dörfler Nicolò Pagan Department of Electrical Engineering and Information Technology Automatic Control Laboratory ETH Zürich Zürich Switzerland Department of Informatics Social Computing Group University of Zürich Zürich Switzerland

Recommendation systems are widely used in web services, such as social networks and e-commerce platforms, to serve personalized content to the users and, thus, enhance their experience. While personalization assists users in navigating through the available options, there have been growing concerns regarding its repercussions on the users and their opinions. Examples of negative impacts include the emergence of filter bubbles and the amplification of users' confirmation bias, which can cause opinion polarization and radicalization. In this paper, we study the impact of recommendation systems on users, both from a microscopic (i.e., at the level of individual users) and a macroscopic (i.e., at the level of a homogenous population) perspective. Specifically, we build on recent work on the interactions between opinion dynamics and recommendation systems to propose a model for this closed loop, which we then study both analytically and numerically. Among others, our analysis reveals that shifts in the opinions of individual users do not always align with shifts in the opinion distribution of the population. In particular, even in settings where the opinion distribution appears unaltered (e.g., measured via surveys across the population), the opinion of individual users might be significantly distorted by the recommendation system.

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MC-CapsNet : Low-level Pooling and High-level Fusion of Multi-columns Capsule Network

MC-CapsNet : Low-level Pooling and High-level Fusion of Mult...

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2021 International Conference on Neuromorphic computing, ICNC 2021

作者： Li, Ke Duan, Shukai Wang, Lidan Wang, Yining Wang, Shu College of Artificial Intelligence Southwest University Brain-inspired Computing and Intelligent Control of Chongqing Key Lab National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing Brain Science Collaborative Innovation Center Chongqing China College of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (纸本)9781665412872

Capsule network is a new type of neural network structure. The biggest feature of capsule network is 'vector in vector out', which replaces the previous 'scalar in scalar out'. The pristine capsule network completely discards the pooling layers and uses a convolutional kernel size of 9 × 9 to increase the perceptual field However, since the original capsule network has only two feature extraction layers, it cannot extract high level abstract features of objects, which are very important for image classification. Moreover, the original capsule network completely discards the use of pooling layers. Based on the above problems, we propose a multi-column capsule network (MC-CapsNet) with low-level pooling and high-level fusion. Unlike the traditional capsule network, MC-CapsNet has the following features: (a)MC-CapsNet consists of two columns of capsule network: the primary column of capsule network extracts high-level impalpable features;the other column of capsule network extracts subordinate shallow features (b)Pooling operation is added to the initial two convolutional layers of the two columns of capsule network (c)Using the high-level theoretical features extracted from the first capsule network to fuse features with the low-level surface layers extracted from the second capsule network to achieve feature complementarily (d)We add the elements of the digital capsule layers in the two-column capsule network to increase the probability of object features being detected by the capsule. The experimental results show that the classification accuracy of MC-CapsNet reaches 87.96% and 94.42% on the CIFAR10 and FashionMNIST datasets. © 2021 IEEE.

关键词： Convolution

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