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Federated Vehicular Transformers and Their Federations: Privacy-Preserving Computing and Cooperation for Autonomous Driving

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2022年第3期7卷 456-465页

作者： Tian, Yonglin Wang, Jiangong Wang, Yutong Zhao, Chen Yao, Fei Wang, Xiao Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China North Automat Control Technol Inst Taiyuan 030006 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China

Cooperative computing is promising to enhance the performance and safety of autonomous vehicles benefiting from the increase in the amount, diversity as well as scope of data resources. However, effective and privacy-preserving utilization of multi-modal and multi-source data remains an open challenge during the construction of cooperative mechanisms. Recently, Transformers have demonstrated their potential in the unified representation of multi-modal features, which provides a new perspective for effective representation and fusion of diverse inputs of intelligent vehicles. Federated learning proposes a distributed learning scheme and is hopeful to achieve privacy-secure sharing of data resources among different vehicles. Towards privacy-preserving computing and cooperation in autonomous driving, this paper reviews recent progress of Transformers, federated learning as well as cooperative perception, and proposes a hierarchical structure of Transformers for intelligent vehicles which is comprised of Vehicular Transformers, Federated Vehicular Transformers and the Federation of Vehicular Transformers to exploit their potential in privacy-preserving collaboration.

关键词： Transformers Autonomous vehicles Collaborative work Point cloud compression Trajectory Computational modeling Vehicle dynamics Cooperative autonomous driving Federated Vehicular Transformers Federation of Vehicular Transformers Vehicular Transformers

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Simultaneously Calibration of Multi Hand-Eye Robot system Based on Graph

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第5期71卷 5010-5020页

作者： Zhou, Zishun Ma, Liping Liu, Xilong Cao, Zhiqiang Yu, Junzhi Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Australian Natl Univ Coll Engn Comp & Cybernet Canberra ACT 2601 Australia Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peking Univ Dept Adv Mfg & Robot Coll Engn State Key Lab Turbulence & Complex SystBIC ESAT Beijing 100871 Peoples R China

Precise calibration is the basis for the vision-guided robot system to achieve high-precision operations. systems with multieyes (cameras) and multihands (robots) are particularly sensitive to calibration errors. Most existing methods focus on the calibration of a single unit of the whole system, such as poses between hand and eye, or between two hands. These methods can be used to determine the pose between each unit, but the serialized incremental calibration strategy cannot avoid the error accumulation problem in a large-scale system. Instead of focusing on a single unit, this article models the multieye and multihand system calibration problem as a graph and proposes a method based on the minimum spanning tree and graph optimization. This method can automatically plan the serialized optimal calibration strategy in accordance with the system settings to get coarse calibration results initially. Then, with these initial values, the closed-loop constraints are introduced to carry out global optimization with different sensor's accuracy considered. As a general calibration method, it can be applied to different multirobot systems. Simulation experiments demonstrate the performance of the proposed algorithm under different noises and various hand-eye configurations. In addition, experiments on real robot systems are presented to further verify the proposed method.

关键词： Calibration error correction graph theory multirobot systems robot vision systems.

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An Automatic Deep Segmentation Network for Pixel-Level Welding Defect Detection

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1-10页

作者： Yang, Lei Song, Shouan Fan, Junfeng Huo, Benyan Li, En Liu, Yanhong Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Henan Peoples R China Zhengzhou Univ Robot Percept & Control Engn Lab Zhengzhou 450001 Henan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Accurate welding defect location is of great significance to modern manufacturing, which could be used for accurate quality evaluation and precise repairing decision-making basis of different products. Nevertheless, accurate welding defect location is still a challenging task due to some complex factors, such as complex backgrounds, low contrast, weak texture, and class imbalance issue. Recently, deep learning has got great development due to its strong feature expression ability, which has been widely applied into defect detection, but it still exists certain shortcomings on segmentation tasks with the class unbalanced issue or microdefects. To address these issues, with the encoder & x2013;decoder network architecture, a novel welding defect location method is proposed with an attention-guided segmentation network. To reduce the contextual information loss of the deep encoder module after multiple convolution and pooling operations, a multiscale feature fusion block is proposed to embed into a U-shaped network (U-Net) to acquire more information. On the basis, combined with a bidirectional convolutional long short-term memory (BiConvLSTM) block, an improved attention block is integrated into the skip connections between the encoder path and the decoder path to capture the global, long-range contexts and emphasize target regions, contributing to locate welding defect areas and enhance the segmentation ability on microdefects. Meanwhile, to address the foreground & x2013;background class imbalance issue, a hybrid loss function combined with binary cross-entropy (BCE) and loss functions is proposed to effectively utilize their unique excellent characteristics for accurate defect segmentation. Experiment results on the public GDXray dataset show that the proposed segmentation method could obtain a competitive segmentation performance compared with other advanced segmentation models.

关键词： Welding Feature extraction Image segmentation Inspection X-ray imaging Manufacturing Task analysis Attention model convolutional long short-term memory (ConvLSTM) defect detection feature fusion hybrid loss image segmentation

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Testing Intelligence: Accelerating the Verification and Validation of Intelligent Vehicles

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第2期8卷 1003-1016页

作者： Wang, Fei-Yue Hu, Jia Lai, Jintao Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Tongji Univ ZhongTe Distinguished Chair Cooperat Automat Key Lab Rd & Traff Engn Minist Educ Shanghai 201804 Peoples R China Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai 201804 Peoples R China

form only. Presents a summary of articles presented in this issue of the publication.

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Distributed Event-Triggered Formation control for a Multi-robotic Fish system

Distributed Event-Triggered Formation Control for a Multi-ro...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Dai, Shijie Wu, Zhengxing Tan, Min Yu, Junzhi School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing100871 China

ISBN: (纸本)9789811966125

This paper investigates the formation control problem for a multi-robotic fish system and a distributed event-triggered-based formation control framework is proposed. The framework consists of a communication topology, a distributed formation control law, and a dynamic event-triggered communication mechanism. In particular, a position-based formation control law is presented to drive the multi-robotic fish system to an anticipated configuration based on local measurements while keeping pace with a moving target. Meanwhile, a dynamic event-triggered mechanism is utilized to measure importance of the interactive information and decide the communication timing. The proposed formation control method shows its merits to greatly reduce communication consumption with a limited loss of formation control performance. Finally, simulations with quantitative comparisons are presented to verify the effectiveness of the proposed framework. This formation control framework provides a solid foundation for future marine cooperative control of the multi-robotic fish system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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Integrated Timetable Rescheduling for Multidispatching Sections of High-Speed Railways During Large-Scale Disruptions

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systemS 2022年第2期9卷 366-375页

作者： Zhou, Min Dong, Hairong Liu, Xuan Zhang, Hongjie Wang, Fei-Yue Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China China Railway Beijing Grp Co Ltd Dept Dispatching Beijing 100038 Peoples R China Chinese Acad Sci CASIA Inst Automat State Key Lab Management & Control Complex Syst S Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100049 Peoples R China

Under the condition of network operation of high-speed railways (HSRs), the influence of disruptions on the train control and dispatching at the current line and related lines is more and more significant. This article focuses on the timetable cooperative rescheduling problem with multidispatching sections of HSRs from a macroscopic perspective in the case of large disruptions. The problem is formulated as a mixed-integer linear program (MILP) model on the objectives of minimizing the weighted sum of the arrival delay time of trains, the delay time of depart trains at the handover station, and the number of delays of trains at all stations. The strategies of retiming and reordering are adopted to generate the rescheduling scheme and reduce delay propagation by making full use of three kinds of buffer time reserved in the timetable, i.e., buffer times of train operation in the station, train running in the section, and electric multiple unit (EMU) connection. A case study of the timetable rescheduling at the two adjacent dispatching sections of the Beijing-Shanghai HSRs line is conducted to evaluate the performance of the proposed integrated rescheduling approach. The relationship between computing time and the quality of rescheduling schemes is also investigated. The computational results show that the proposed approach can generate a conflict-free timetable with the minimum arrival delay time and the number of delays of all trains at all stations compared to the nonintegrated rescheduling approach and the benchmark solution of the first-come-first-serve (FCFS) approach. The propagation of delay between dispatching sections is also greatly suppressed. The results can provide support for dispatchers to making reasonable rescheduling decisions in the case of large disruptions.

关键词： Delays Dispatching Rail transportation Rails Safety Routing Real-time systems Delay propagation high-speed railways (HSRs) integrated timetable rescheduling large-scale disruptions multidispatching sections

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FIT: Frequency-Based Image Translation for Domain Adaptive Object Detection 29th

FIT: Frequency-Based Image Translation for Domain Adaptive O...

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29th International Conference on Neural Information Processing

作者： Zhang, Siqi Zhang, Lu Liu, Zhiyong Feng, Hangtao Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China

ISBN: (纸本)9783031301100;9783031301117

Domain adaptive object detection (DAOD) aims to adapt the detector from a labelled source domain to an unlabelled target domain. In recent years, DAOD has attracted massive attention since it can alleviate performance degradation due to the large shift of data distributions in the wild. To align distributions between domains, adversarial learning is widely used in existing DAOD methods. However, the decision boundary for the adversarial domain discriminator may be inaccurate, causing the model biased towards the source domain. To alleviate this bias, we propose a novel Frequency-based Image Translation (FIT) framework for DAOD. First, by keeping domain-invariant frequency components and swapping domain-specific ones, we conduct image translation to reduce domain shift at the input level. Second, hierarchical adversarial feature learning is utilized to further mitigate the domain gap at the feature level. Finally, we design a joint loss to train the entire network in an end-to-end manner without extra training to obtain translated images. Extensive experiments on three challenging DAOD benchmarks demonstrate the effectiveness of our method.

关键词： Unsupervised Domain Adaptation Object Detection Frequency Domain Image Translation Adversarial Learning

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Imaginations Generate Images for Multi-modal Machine Translation 1

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13th International Conference on Computer Engineering and Networks (CENet)

作者： Yang, Xiaona Sun, Wenli Wei, Wei Li, Yinlin Shi, Xiayang Zhengzhou Univ Light Ind Zhengzhou Peoples R China Wuhan Second Ship Design & Res Inst Wuhan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (数字)9789819992430

ISBN: (纸本)9789819992454;9789819992430;9789819992423

Multi-modal machine translation (MMT) aims at exploring better translation systems by integrating the visual annotation which presents the content described in the bilingual parallel sentence pair into the conventional only-text neural machine translation (NMT). However, existing methods heavily rely on the manual annotated images data set. The cost of manual image annotation is relatively high at this stage. In this paper, we propose the generative imagination network with transformer to automatically generate visual annotations semantic-equivalent with source and target sentences. The proposed model receives the inputs of source-target bilingual sentences and generates visual annotations for MMT. Experiments analysis demonstrate that our model can generate high-quality annotated images and prompt the performance of MMT. Additionally, we use our model to generate annotated images for a famous English-German IWSLT-2015, the experimental results show the improvement for MMT.

关键词： Multi-modal machine translation Visual annotation

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LC-LLM: Explainable lane-change intention and trajectory predictions with Large Language Models

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COMMUNICATIONS IN TRANSPORTATION RESEARCH 2025年 5卷

作者： Peng, Mingxing Guo, Xusen Chen, Xianda Chen, Kehua Zhu, Meixin Chen, Long Wang, Fei-Yue Hong Kong Univ Sci & Technol Guangzhou Intelligent Transportat INTR Syst Hub Guangzhou 511400 Peoples R China Hong Kong Univ Sci & Technol Acad Interdisciplinary Studies Div Emerging Interdisciplinary Areas EMIA Hong Kong 999077 Peoples R China Guangdong Prov Key Lab Integrated Commun Sensing & Guangzhou 511400 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

To ensure safe driving in dynamic environments, autonomous vehicles should possess the capability to accurately predict lane change intentions of surrounding vehicles in advance and forecast their future trajectories. Existing motion prediction approaches have ample room for improvement, particularly in terms of long-term prediction accuracy and interpretability. In this study, we address these challenges by proposing a Lane Change-Large Language Model (LC-LLM), an explainable lane change prediction model that leverages the strong reasoning capabilities and self explanation abilities of Large Language Models (LLMs). Essentially, we reformulate the lane change prediction task as a language modeling problem, processing heterogeneous driving scenario information as natural language prompts for LLMs and employing supervised fine-tuning to tailor LLMs specifically for lane change prediction task. Additionally, we finetune the Chain-of-Thought (CoT) reasoning to improve prediction transparency and reliability, and include explanatory requirements in the prompts during the inference stage. Therefore, our LC-LLM not only predicts lane change intentions and trajectories but also provides CoT reasoning and explanations for its predictions, enhancing its interpretability. Extensive experiments based on the large-scale highD dataset demonstrate the superior performance and interpretability of our LC-LLM in lane change prediction task. To the best of our knowledge, this is the first attempt to utilize LLMs for predicting lane change behavior. Our study shows that LLMs can effectively encode comprehensive interaction information for understanding driving behavior.

关键词： Lane change (LC) Large language models (LLMs) Intention prediction Trajectory prediction Fine-tuning Interpretability Autonomous driving

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Sustainability Opportunities and Ethical Challenges of AI-Enabled Connected Autonomous Vehicles Routing in Urban Areas

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 55-58页

作者： Guo, Rongge Vallati, Mauro Wang, Yutong Zhang, Hui Chen, Yuanyuan Wang, Fei-Yue Univ Huddersfield Sch Comp & Engn Huddersfield HD13DH England Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beihang Univ Sch Transportat Sci & Engn Beijing 100091 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Engn Macau 999078 Peoples R China

The advent of Connected Autonomous Vehicles (CAVs) paves the way to a new era of urban traffic control and management, driven by Artificial Intelligence (AI)-enabled strategies. This advancement promises significant improvements in infrastructure use optimization, traffic delay reduction, and overall sustainability. The autonomous driving capabilities of CAVs, coupled with the communication technology, allow vehicles to play an active role in urban traffic control: they can follow tailored instructions and can act as highly accurate moving sensors for traffic authorities. However, such improved capabilities come at the cost of unprecedented vulnerabilities to cyber exploitation, and with the concrete potential to increase social and economic disparities. As an extension of TIV-DHW (Distributed/Decentralized Hybrid Workshop) on ERS (Ethics, Responsibility, and Sustainability), this letter explores how the AI-enabled routing methodology can enhance urban transportation sustainability while also discussing its ethical implications and challenges.

关键词： Routing Artificial intelligence Traffic control Sustainable development Urban areas Safety Ethics Connected autonomous vehicles artificial intelligence traffic routing sustainability ethics

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