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

作者： Chaochen, Zhuolei Zhang, Qichao Li, Ding Li, Haoran Pang, Zhonghua North China University of Technology Beijing100144 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Hunan University Changsha410082 China

ISBN: (纸本)9789811692468

Trajectory prediction is one of the main challenges to autonomous vehicles. Except for the predicted vehicle historical trajectory information, viable solutions for this task must also consider the static geometric context, such as lane centerlines, and the dynamic interaction information among traffic participants, such as the influence of surrounding vehicles or pedestrians on the predicted vehicle. Recently, mainstream methods are mostly based on recurrent neural networks, which have achieved state-of-the-art (SOTA) performance on distance-based prediction metrics. In order to consider the influence of static geometric context and dynamic interaction information better for the vehicle trajectory prediction, we propose a two-stage prediction framework and a combined VAE-GAT model. Stage I estimates the future destination point of the predicted vehicle using a destination point estimation module based on VAE. In stage II, it produces multiple trajectories using a multiple trajectories prediction module based on GAT which consists of an LSTM-based encoder-decoder and a graph attention network. Finally, one specific trajectory is selected from multiple trajectories conditioned on the estimated destination point. The experimental results and performance comparison based on the Argoverse motion forecasting dataset are given to prove the effectiveness of the proposed method. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Line-of-sight based three-dimensional path following control for an underactuated robotic dolphin

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Science China(Information Sciences) 2021年第1期64卷 215-226页

作者： Jincun LIU Zhenna LIU Junzhi YU State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering ScienceBIC-ESATCollege of EngineeringPeking University Department of Electrical and Automation Shandong Labor Vocational and Technical College State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

This paper investigates the three-dimensional(3-D) path following control problem for an underactuated robotic dolphin. With a comprehensive consideration of the mechanical constraint and swimming principle of the robotic dolphin, a decoupling motion strategy is proposed to produce yaw and pitch maneuvers simultaneously. Then, kinematics and dynamics models for 3-D dolphin-like swimming are established,followed by simulations of the path following control. Furthermore, a novel lookahead based 3-D line-ofsight(LOS) guidance law is developed and implemented to obtain desired attitude angles with its simplicity,intuitiveness, and small computational footprint. Finally, simulation results illustrate the feasibility and effectiveness of the proposed path following control methods.

关键词： robotic dolphin path following 3-D LOS guidance law decoupling motion

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Hovering control of an Underwater Vehicle

Hovering Control of an Underwater Vehicle

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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Cao, Yaozhong Song, Dalei Wang, Zhan Wang, Yu University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China Ocean University of China Department of Automation and Measurement Qingdao266100 China Zhejiang Provincial Energy Group Company Ltd Hangzhou310000 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

This paper aims to achieve the hovering control of an underwater vehicle with uncertain parameters based on cascade PID. First, the inner loops for each channels are designed respectively. Then, the outer loops including depth controller, horizontal position controller and yaw controller are designed. The vector transformation method are used to force the vehicle to approach the target point along approximately straight line in the horizontal plane. Simulation results demonstrate the stability and validity of the proposed method. © 2022 IEEE.

关键词： controllers

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An intelligent maritime scene frame prediction based on digital twins technology 1

An intelligent maritime scene frame prediction based on digi...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Guo, Zhong-Zheng Wang, Kai Qin, Jiang-Yi Li, Xian-Bin Ran, De-Chao Shen, Yu Unmanned Systems Research Center National Innovation Institute of Defense Technology Academy of Military Science Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9781665433372

This paper proposes a scene frame prediction system based on digital twin technology. The system is mainly composed of an unmanned perception module, a communication link module, a command center module that provides intelligent computing services, and an interaction and visualization module for human operators. These modules together constitute a system for predicting and recovery the time-series matrix composed of scene frames and object frames. At the same time, in view of the current problems of lack of real-time information and long response time in the maritime search and rescue (SAR) field, this paper instantiates the above system to improve the existing SAR system. Satellites and unmanned vehicles perceive part of the scene data of the real cyber-physical system in real-time and divide them into multiple frames according to the timestamps. In addition, this paper also proposes an environmental graph-attention network (Env-GAT) to achieve efficient and reliable frame predicting. Finally, this paper verifies that the performance of the spatiotemporal attention graph network model has reached the optimal level through computational experiments. © 2021 IEEE.

关键词： Forecasting

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Mutually trustworthy human-machine knowledge automation and hybrid augmented intelligence:mechanisms and applications of cognition,management,and control for complex systems

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Frontiers of Information Technology & Electronic Engineering 2022年第8期23卷 1142-1157页

作者： Fei-Yue WANG Jianbo GUO Guangquan BU Jun Jason ZHANG The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China State Grid Cooperation of China Beijing 100031China China Electric Power Research Institute Beijing 100192China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

In this paper,we aim to illustrate the concept of mutually trustworthy human-machine knowledge automation(HM-KA)as the technical mechanism of hybrid augmented intelligence(HAI)based complex system cognition,management,and control(CMC).We describe the historical development of complex system science and analyze the limitations of human intelligence and machine *** need for using human-machine HAI in complex systems is then explained in *** concept of“mutually trustworthy HM-KA”mechanism is proposed to tackle the CMC challenge,and its technical procedure and pathway are demonstrated using an example of corrective control in bulk power grid *** is expected that the proposed mutually trustworthy HM-KA concept can provide a novel and canonical mechanism and benefit real-world practices of complex system CMC.

关键词： complex systems Human-machine knowledge automation Parallel systems Bulk power grid dispatch Artificial intelligence Internet of Minds(IoM)

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Mixed Motivation Driven Social Multi-Agent Reinforcement Learning for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1272-1282页

作者： Long Chen Peng Deng Lingxi Li Xuemin Hu State Key Laboratory of Multimodal Artificial Intelligence Systems and the State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing WAYTOUS Inc. Beijing Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Computer Science and Information Engineering Hubei University Wuhan China Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN USA School of Artificial Intelligence Hubei University Wuhan Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan China

Despite great achievement has been made in autonomous driving technologies, autonomous vehicles (AVs) still exhibit limitations in intelligence and lack social coordination, which is primarily attributed to their reliance on single-agent technologies, neglecting inter-AV interactions. Current research on multi-agent autonomous driving (MAAD) predominantly focuses on either distributed individual learning or centralized cooperative learning, ignoring the mixed-motive nature of MAAD systems, where each agent is not only self-interested in reaching its own destination but also needs to coordinate with other traffic participants to enhance efficiency and safety. Inspired by the mixed motivation of human driving behavior and their learning process, we propose a novel mixed motivation driven social multi-agent reinforcement learning method for autonomous driving. In our method, a multi-agent reinforcement learning (MARL) algorithm, called Social Learning Policy Optimization (SoLPO), which takes advantage of both the individual and social learning paradigms, is proposed to empower agents to rapidly acquire self-interested policies and effectively learn socially coordinated behavior. Based on the proposed SoLPO, we further develop a mixed-motive MARL method for autonomous driving combined with a social reward integration module that can model the mixed-motive nature of MAAD systems by integrating individual and neighbor rewards into a social learning objective for improved learning speed and effectiveness. Experiments conducted on the MetaDrive simulator show that our proposed method outperforms existing state-of-the-art MARL approaches in metrics including the success rate, safety, and efficiency. More-over, the AVs trained by our method form coordinated social norms and exhibit human-like driving behavior, demonstrating a high degree of social coordination.

关键词： Measurement Reinforcement learning Safety Driver behavior Autonomous vehicles Optimization

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Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation

arXiv

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arXiv 2025年

作者： Zhang, Dengke Tang, Quan Liu, Fagui Mei, Haiqing Chen, C.L. Philip The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Department of New Network Pengcheng Laboratory Shenzhen518000 China The China Tobacco Guangdong Industrial Corporation Guangzhou510385 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Semi-supervised semantic segmentation has witnessed remarkable advancements in recent years. However, existing algorithms are based on convolutional neural networks and directly applying them to Vision Transformers poses certain limitations due to conceptual disparities. To this end, we propose TokenMix, a data augmentation technique specifically designed for semi-supervised semantic segmentation with Vision Transformers. TokenMix aligns well with the global attention mechanism by mixing images at the token level, enhancing learning capability for contextual information among image patches. We further incorporate image augmentation and feature augmentation to promote the diversity of augmentation. Moreover, to enhance consistency regularization, we propose a dual-branch framework where each branch applies image and feature augmentation to the input image. We conduct extensive experiments across multiple benchmark datasets, including Pascal VOC 2012, Cityscapes, and COCO. Results suggest that the proposed method outperforms state-of-the-art algorithms with notably observed accuracy improvement, especially under limited fine annotations. Copyright © 2025, The Authors. All rights reserved.

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AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion

AdvDiffuser: Generating Adversarial Safety-Critical Driving ...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Yuting Xie Xianda Guo Cong Wang Kunhua Liu Long Chen School of Computer Science and Engineering Sun Yat-Sen University School of Computer Science Wuhan University State Key Laboratory for Management and Control of Complex Systems at the Institute of Automation Chinese Academy of Sciences School of Mechanical and Automotive Engineering Qingdao University of Technology

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Safety-critical scenarios are infrequent in natural driving environments but hold significant importance for the training and testing of autonomous driving systems. The prevailing approach involves generating safety-critical scenarios automatically in simulation by introducing adversarial adjustments to natural environments. These adjustments are often tailored to specific tested systems, thereby disregarding their transferability across different systems. In this paper, we propose AdvDiffuser, an adversarial framework for generating safety-critical driving scenarios through guided diffusion. By incorporating a diffusion model to capture plausible collective behaviors of background vehicles and a lightweight guide model to effectively handle adversarial scenarios, AdvDiffuser facilitates transferability. Experimental results on the nuScenes dataset demonstrate that AdvDiffuser, trained on offline driving logs, can be applied to various tested systems with minimal warm-up episode data and outperform other existing methods in terms of realism, diversity, and adversarial performance.

关键词： Training Diffusion models Intelligent robots Autonomous vehicles Testing

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Pedestrian Trajectory Prediction by Modeling the Interactions using Social LSTM Extensions

Pedestrian Trajectory Prediction by Modeling the Interaction...

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Chinese Automation Congress (CAC)

作者： Zeze Si Hongxia Zhao Haina Tang Fenghua Zhu Yisheng Lv Hao Lu State Key Laboratory for Management and Control of Complex Systems Institute of Autamation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academic of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China

In many driving situations, human mobility is an important topic in trajectory prediction. Considering the pedestrian trajectory as a sequence generative task, a prediction algorithm based on Social Long Short-Term Memory (Social LSTM) is implemented. In order to simulate the social interaction between pedestrians, Social Pooling (S-Pooling) is used to aggregate the hidden state of pedestrians, while the attention mechanism is utilized to aggregate information differently according to the importance of surrounding pedestrians. Furthermore, Convolutional Neural Networks (CNN) is introduced into Social LSTM model to consider both the interaction between people and the characteristic of scene scale in the prediction process. Experiments are carried out against baseline methods, and the results demonstrated that combining Social LSTM with attention mechanism or CNN can improve the performance of pedestrian trajectory prediction.

关键词： Automation Aggregates Predictive models Prediction algorithms Trajectory Convolutional neural networks Task analysis

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ReCUS: Reconvolution and Upsampling Network for Object Detection 7

ReCUS: Reconvolution and Upsampling Network for Object Detec...

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7th IEEE International Conference on Cloud Computing and Intelligence systems, CCIS 2021

作者： Li, Fudong Gao, Dongyang Yang, Yuequan Cao, Zhiqiang Wang, Wei Yangzhou225000 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China Affiliated Hospital of Yangzhou University Yangzhou225012 China

ISBN: (纸本)9781665441490

Most of the mainstream object detection models such as RetinaNet, SSD, YOLO, and Faster RCNN hardly achieve a good balance between detection accuracy and speed. A major reason is rich deep feature semantic information of images is not fully exploited. To solve this problem, a novel deep convolutional network structure termed as reconvolution and upsampling network (ReCUS) is proposed. In the ReCUS, a modified path aggregation network(mPAN) is added after the backbone, which is beneficial to strengthen the foreground salient feature information and weaken background information. Moreover, two new spatial pyramid pooling (SPP) modules are embedded before output heads for multi-scale fusion of local and global features. The experiments show that the effectiveness of our proposed ReCUS. Furthermore, the better detectability of the ReCUS network is demonstrated for both small scale objects and large scale objects. © 2021 IEEE.

关键词： Object detection

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