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2023 IEEE International Conference on Unmanned systems, ICUS 2023

作者： Lang, Ping Tian, Daxin Duan, Xuting Zhou, Jianshan State Key Lab of Intelligent Transportation System Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beijing China School of Transportation Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China

ISBN: (纸本)9798350316308

As a novel computing paradigm, the Vehicular Edge Computing (VEC) network provides an additional avenue for onboard task execution for connected and automated vehicles (CAVs) in unmanned systems. In a VEC network, CAVs can offload tasks to different external servers for execution and obtain results. However, existing task offloading studies only focus on the one-to-one offloading of onboard tasks to edge servers, ignoring the requirements of parallel task execution and security. In this paper, we propose a blockchain-based multi-destination cooperative task offloading architecture in a VEC network. Based on this architecture, we analyze the task offloading and blockchain models and formulate a multi-destination cooperative task offloading optimization problem. Finally, we compare the performance of different multi-destination task offloading methods in simulations and demonstrate the effectiveness of the proposed architecture and optimization. © 2023 IEEE.

关键词： Blockchain

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Cross-Layer Feature Pyramid Transformer for Small Object Detection in Aerial Images

arXiv

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arXiv 2024年

作者： Du, Zewen Hu, Zhenjiang Zhao, Guiyu Jin, Ying Ma, Hongbin The National Key Lab of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing100081 China

Object detection in aerial images has always been a challenging task due to the generally small size of the objects. Most current detectors prioritize novel detection frameworks, often overlooking research on fundamental components such as feature pyramid networks. In this paper, we introduce the Cross-Layer Feature Pyramid Transformer (CFPT), a novel upsampler-free feature pyramid network designed specifically for small object detection in aerial images. CFPT incorporates two meticulously designed attention blocks with linear computational complexity: the Cross-Layer Channel-Wise Attention (CCA) and the Cross-Layer Spatial-Wise Attention (CSA). CCA achieves cross-layer interaction by dividing channel-wise token groups to perceive cross-layer global information along the spatial dimension, while CSA completes cross-layer interaction by dividing spatial-wise token groups to perceive cross-layer global information along the channel dimension. By integrating these modules, CFPT enables cross-layer interaction in one step, thereby avoiding the semantic gap and information loss associated with element-wise summation and layer-by-layer transmission. Furthermore, CFPT incorporates global contextual information, which enhances detection performance for small objects. To further enhance location awareness during cross-layer interaction, we propose the Cross-Layer Consistent Relative Positional Encoding (CCPE) based on inter-layer mutual receptive fields. We evaluate the effectiveness of CFPT on two challenging object detection datasets in aerial images, namely VisDrone2019-DET and TinyPerson. Extensive experiments demonstrate the effectiveness of CFPT, which outperforms state-of-the-art feature pyramid networks while incurring lower computational costs. The code will be released at https://***/duzw9311/CFPT. Copyright © 2024, The Authors. All rights reserved.

关键词： Aerial photography

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Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization

Learning Instructions with Unlabeled Data for Zero-Shot Cros...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Gu, Yuxian Ke, Pei Zhu, Xiaoyan Huang, Minlie The CoAI group Tsinghua University Beijing China Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

Training language models to learn from human instructions for zero-shot cross-task generalization has attracted much attention in NLP communities. Recently, instruction tuning (IT), which fine-tunes a pre-trained language model on a massive collection of tasks described via human-craft instructions, has been shown effective in instruction learning for unseen tasks. However, IT relies on a large amount of human-annotated samples, which restricts its generalization. Unlike labeled data, unlabeled data are often massive and cheap to obtain. In this work, we study how IT can be improved with unlabeled data. We first empirically explore the IT performance trends versus the number of labeled data, instructions, and training tasks. We find it critical to enlarge the number of training instructions, and the instructions can be underutilized due to the scarcity of labeled data. Then, we propose Unlabeled Data Augmented Instruction Tuning (UDIT) to take better advantage of the instructions during IT by constructing pseudo-labeled data from unlabeled plain texts. We conduct extensive experiments to show UDIT's effectiveness in various scenarios of tasks and datasets. We also comprehensively analyze the key factors of UDIT to investigate how to better improve IT with unlabeled data. The code is publicly available at https://***/thu-coai/UDIT. © 2022 Association for Computational Linguistics.

关键词： Zero-shot learning

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Predictor-Based Time Delay Control of A Hex-Jet Unmanned Aerial Vehicle

arXiv

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arXiv 2025年

作者： Liang, Junning Zheng, Haowen Zhang, Yuying Gao, Yongzhuo Dong, Wei Lyu, Ximin School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China

Turbojet-powered VTOL UAVs have garnered increased attention in heavy-load transport and emergency services, due to their superior power density and thrust-to-weight ratio compared to existing electronic propulsion systems. The main challenge with jet-powered UAVs lies in the complexity of thrust vectoring mechanical systems, which aim to mitigate the slow dynamics of the turbojet. In this letter, we introduce a novel turbojet-powered UAV platform named Hex-Jet. Our concept integrates thrust vectoring and differential thrust for comprehensive attitude control. This approach notably simplifies the thrust vectoring mechanism. We utilize a predictor-based time delay control method based on the frequency domain model in our Hex-Jet controller design to mitigate the delay in roll attitude control caused by turbojet dynamics. Our comparative studies provide valuable insights for the UAV community, and flight tests on the scaled prototype demonstrate the successful implementation and verification of the proposed predictor-based time delay control technique. Copyright © 2025, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Remaining Useful Life Prediction for a Multi-stack Solid Oxide Fuel Cell System with Degradation Interactions

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CSEE Journal of Power and Energy systems 2022年第4期8卷 1207-1214页

作者： Xiaojuan Wu Liangfei Xu Yang Huang Danan Yang Junhao Wang Houjun Wang Xi Li School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 610054China State Key Lab of Automotive Safety and Energy School of Vehicle and MobilityTsinghua UniversityBeijing 100084China School of Artificial Intelligencei and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science and TechnologyWuhan 430074China

Due to the constraints of manufacturing and materials,high-power plants cannot rely on only one solid oxide fuel cell stack.A multi-stack system is a solution for a highpower system,which consists of multiple fuel cell stacks.A short lifetime is one of the main challenges for the fuel cell before largescale commercial applications,and prognostic is an important method to improve the reliability of fuel *** from the traditional prognostic approaches applied to single-stack fuel cell systems,the key problem in multi-stack prediction is how to solve the correlation of multi-stack degradation,which can directly affect the accuracy of *** response to this difficulty,a standard Brownian motion is added to the traditional Wiener process to model the degradation of each stack,and then the probability density function of the remaining useful life(RUL)of each stack is ***,a Copula function is adopted to reflect the dependence between life distributions,so as to obtain the remaining useful life for the whole multi-stack system.1 The simulation results show that compared with the traditional prediction model,the proposed approach has a higher prediction accuracy for multi-stack fuel cell systems.

关键词： Degradation interaction multi-stack remaining useful life(RUL) solid oxide fuel cell(SOFC)

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PDDepth: Pose Decoupled Monocular Depth Estimation for Roadside Perception System

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IEEE Transactions on Circuits and systems for Video technology 2025年

作者： Wang, Huanan Zhang, Xinyu Chen, Zhengxian Jun, Li Liu, Huaping School of Vehicle and Mobility Tsinghua University Beijing100084 China State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing100084 China

Accurate depth information is crucial for roadside perception in Cooperative Vehicle Infrastructure systems. Beyond existing radar and LiDAR solutions, monocular depth estimation using surveillance cameras is emerging as a superior approach due to its cost-effectiveness and dense depth output. Unlike onboard cameras, roadside cameras are relatively fixed in position. Many existing monocular depth estimation methods, which do not independently model camera pose, tend to overfit to training data and produce suboptimal results when confronted with slight variations in camera poses, which may be caused by external forces within the same camera or across different cameras. To address this issue, a pose decoupled monocular depth estimation method specifically designed for roadside perception systems is proposed. This method separates depth estimation into two components: a pose-dependent modeling portion that recovers ground depth based on the current camera pose, and a pose-agnostic portion that estimates pixel height relative to the ground plane. Additionally, a knowledge distillation framework is introduced to improve the robustness of the proposed method against variations in roadside cameras. To validate the method, we propose the first open source dataset for roadside monocular depth estimation, DAIR-MDE, and a roadside instance segmentation dataset, DAIR-Ins, both derived from the DAIR dataset. The proposed method demonstrates significant advances over the state-of-the-art methods on DAIR-MDE. The proposed dataset and source code are publicly available at https://***/441599828/PDDepth. ©1991-2012 IEEE.

关键词： Roadsides

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SGOR: Outlier Removal by Leveraging Semantic and Geometric Information for Robust Point Cloud Registration

arXiv

引用

arXiv 2024年

作者： Zhao, Guiyu Guo, Zhentao Ma, Hongbin The National Key Lab of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing100081 China

In this paper, we introduce a new outlier removal method that fully leverages geometric and semantic information, to achieve robust registration. Current semantic-based registration methods only use semantics for point-to-point or instance semantic correspondence generation, which has two problems. First, these methods are highly dependent on the correctness of semantics. They perform poorly in scenarios with incorrect semantics and sparse semantics. Second, the use of semantics is limited only to the correspondence generation, resulting in bad performance in the weak geometry scene. To solve these problems, on the one hand, we propose secondary ground segmentation and loose semantic consistency based on regional voting. It improves the robustness to semantic correctness by reducing the dependence on single-point semantics. On the other hand, we propose semantic-geometric consistency for outlier removal, which makes full use of semantic information and significantly improves the quality of correspondences. In addition, a two-stage hypothesis verification is proposed, which solves the problem of incorrect transformation selection in the weak geometry scene. In the outdoor dataset, our method demonstrates superior performance, boosting a 22.5 percentage points improvement in registration recall and achieving better robustness under various conditions. Our code is available. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Resilient Event-Triggered Model Predictive Control for Adaptive Cruise Control Under Sensor Attacks

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 807-809页

作者： Zhijian Hu Rong Su Kai Zhang Zeyuan Xu Renjie Ma School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technologyand the Center of Ultra-Precision Optoelectronic Instrument EngineeringHarbin Institute of TechnologyHarbin 150001China Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China Department of Chemical and Biomolecular Engineering National University of SingaporeSingapore 117585Singapore State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150001China IEEE

Dear Editor,This letter addresses the resilient model predictive control(MPC)problems for adaptive cruise control(ACC)systems under sensor *** the light of vulnerabilities of ACC systems to sensor attacks,an intrusion detection mechanism is proposed at the controller side to distinguish abnormal ***,the robust control gains are derived to design the terminal region constraint for MPC.

关键词： Trigger Event constraint

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Towards Intercontinental Teleoperation: A Cloud-Based Framework for Ultra-Remote Human-Robot Dual-Arm Motion Mapping 1

引用

16th International Conference on intelligent Robotics and Applications, ICIRA 2023

作者： Lv, Honghao Zhou, Huiying Wang, Ruohan Wu, Haiteng Pang, Zhibo Yang, Geng State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot Hangzhou Shenhao Technology Hangzhou China Department of Automation Technology ABB Corporate Research Vasteras Sweden Stockholm Sweden

ISBN: (数字)9789819964987

ISBN: (纸本)9789819964970

Teleoperated robots offer a unique combination of robotic strength and precision, coupled with human judgment and ingenuity, to enhance productivity and safety in complex, unstructured, and hazardous industrial environments. However, teleoperation over ultra-remote distances has posed a significant challenge, with control performance being compromised by the inherent trade-off between distance and latency. This study presents a novel cloud-based human-robot motion mapping framework for ultra-remote teleoperation. An intuitive dual-arm human-robot motion mapping framework based on capturing human motion has been implemented, and private network connectivity is established using the Google Cloud platform for the efficient transfer of control data required for human-robot motion mapping. Moreover, a novel feedforward control framework is proposed to mitigate the negative impact of high network latency on teleoperation control performance. To demonstrate the feasibility of the proposed approach, an intercontinental teleoperation system between China and Sweden was developed, and a series of practical applications were demonstrated by capturing the motion of an operator in Sweden to control the dual-arm robot located over 7800 km away in China. The proposed feedforward control framework for intercontinental teleoperation has the potential utility for improving human-robot interaction performance in ultra-remote teleoperation scenarios with high network latency. More demonstration videos can be found at https://***/intercontinental-teleoperation/. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Remote control

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Unsupervised Semantic Segmentation with Contrastive Translation Coding 6th

Unsupervised Semantic Segmentation with Contrastive Translat...

引用

6th International Conference on Cognitive systems and Signal Processing, ICCSIP 2021

作者： Chen, Runfa Sun, Hanbing Wang, Ling State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China Department of Air Defense Early Warning Command System Air Force Early Warning Academy Wuhang China

ISBN: (纸本)9789811692468

The proposed new method constructs a contrastive learning task through unsupervised image-to-image translation and indirectly extracts domain-invariant features by maximizing mutual information. Specifically, by reusing the semantic segmentation network for image-to-image translation, the probability space of the segmentation output is projected into the RGB space, thereby constructing positive and negative samples to maximize the mutual information between the input image and the semantic segmentation RGB projection map. Meanwhile, the output space of the target domain is transferred to the source domain, and then the robust domain-invariant semantic features are extracted. We develop two adversarial transfer methods and a three-stage training paradigm of pre-training, cross-domain transfer, and self-supervised training. Experiments on benchmarks demonstrate that this method is reasonable and feasible. Comprehensive ablation studies and analyses are also carried out to reveal the advantages and disadvantages of the two designed transfer schemes and the effect and significance of color-invariant semantic information for unsupervised semantic segmentation tasks. The contrastive learning task, which is constructed through unsupervised cross-domain image-to-image translation, provides a new insight for cross-domain transfer learning to overcome the problem of domain shift. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Semantic Segmentation

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