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International Conference on Intelligent Transportation

作者： Hongrui Chen Yan Zhang Xingyuan Dai Jiaming Sun Fei Cong Yanan Lu Bingji Xu Yisheng Lv School of Information Engineering China University of Geosciences (Beijing) Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China China Telecom Digital City Technology Co. Ltd Hebei China

In the traffic simulation system, the data quality and inference speed of traffic flow generation have a direct impact on the correlation between analysis results and actual traffic conditions, as well as the efficiency of utilization. Existing traffic flow generation methods often rely on abundant sensor data and involve time-consuming generation processes. To reduce time costs, in this paper, we propose an efficient Multi-Route Traffic Flow Generator (MTFG). The generation process is divided into two parts: multi-route generation and route quantity search. In the multi-route generation, we employ a Multi-Route Generator (MRG) methodology leveraging topological road network data to generate distinct traffic routes for identical origin-destination pairs. This ensures the attainment of diversified vehicle trajectories even within a sparsely equipped detector network. Subsequently, in the route quantity search, we employ an improved simulated annealing algorithm to systematically explore the traffic route quantity based on the generated multiple routes, thereby significantly amplifying the algorithm's efficacy in route generation. We validate the effectiveness of MTFG using the Malaga open dataset on the SUMO simulation platform. Compared to the strong baseline Flow Generator Algorithm (FGA) with a training time exceeding 2.5h, MTFG achieves the target metric in just 1h under the same conditions, saving over 55% of training time.

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Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2022年第3期7卷 417-440页

作者： Hu, Zhongxu Lou, Shanhe Xing, Yang Wang, Xiao Cao, Dongpu Lv, Chen Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 637460 Singapore Cranfield Univ Ctr Autonomous & Cyber Phys Syst Cranfield MK43 0AL Beds England Chinese Acad Sci Inst Automat Qingdao Acad Intelligent Ind Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China

Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the design of the automated vehicle, whereas the digitization of the human driver, who plays an important role in driving, is largely ignored. Furthermore, previous driver-related tasks are limited to specific scenarios and have limited applicability. Thus, a novel concept of a driver digital twin (DDT) is proposed in this study to bridge the gap between existing automated driving systems and fully digitized ones and aid in the development of a complete driving human cyber-physical system (H-CPS). This concept is essential for constructing a harmonious human-centric intelligent driving system that considers the proactivity and sensitivity of the human driver. The primary characteristics of the DDT include multimodal state fusion, personalized modeling, and time variance. Compared with the original DT, the proposed DDT emphasizes on internal personality and capability with respect to the external physiological-level state. This study systematically illustrates the DDT and outlines its key enabling aspects. The related technologies are comprehensively reviewed and discussed with a view to improving them by leveraging the DDT. In addition, the potential applications and unsettled challenges are considered. This study aims to provide fundamental theoretical support to researchers in determining the future scope of the DDT system

关键词： Vehicles Digital twins Data models Behavioral sciences Safety Autonomous vehicles Reliability Driver digital twin human-centric deisgn intelligent vehicles human-machine interactions cyber-physical systems

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A Vibration control Method for Hybrid-Structured Flexible Manipulator Based on Sliding Mode control and Reinforcement Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2021年第2期32卷 841-852页

作者： Long, Teng Li, En Hu, Yunqing Yang, Lei Fan, Junfeng Liang, Zize Guo, Rui Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China CRRC Zhuzhou Inst Co Ltd Zhuzhou 412001 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China State Grid Shandong Elect Power Co Jinan 250001 Peoples R China

The hybrid-structured flexible manipulator has a complex structure and strong coupling between state variables. Meanwhile, the natural frequency of the hybrid-structured flexible manipulator varies with the motion of the telescopic joint, so it is difficult to suppress the vibration quickly. In this article, the tip state signal of the hybrid-structured flexible manipulator is decomposed into elastic vibration signal and tip vibration equilibrium position signal, and a combined control method is proposed to improve tip positioning accuracy and trajectory tracking accuracy. In the proposed combined control method, an improved nominal model-based sliding mode controller (NMBSMC) is used as the main controller to output the driving torque, and an actor-critic-based reinforcement learning controller (ACBRLC) is used as an auxiliary controller to output small compensation torque. The improved NMBSMC can be divided into a nominal model-based sliding mode robust controller and a practical model-based integral sliding mode controller. Two sliding mode controllers with different structures make full use of the mathematical model and the measured data of the actual system to improve the vibration equilibrium position tracking accuracy. The ACBRLC uses the tip elastic vibration signal and the prioritized experience replay method to obtain the small reverse compensation torque, which is superimposed with the output of the NMBSMC to suppress tip vibration and improve the positioning accuracy of the hybrid-structured flexible manipulator. Finally, several groups of experiments are designed to verify the effectiveness and robustness of the proposed combined control method.

关键词： Vibrations Mathematical model Manipulator dynamics Torque Robustness Neural networks Hybrid-structured flexible manipulator reinforcement learning sliding mode control vibration control method

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BNAS: Efficient Neural Architecture Search Using Broad Scalable Architecture

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2022年第9期33卷 5004-5018页

作者： Ding, Zixiang Chen, Yaran Li, Nannan Zhao, Dongbin Sun, Zhiquan Chen, C. L. Philip Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Dalian Maritime Univ Coll Nav Dalian 116026 Peoples R China

Efficient neural architecture search (ENAS) achieves novel efficiency for learning architecture with high-performance via parameter sharing and reinforcement learning (RL). In the phase of architecture search, ENAS employs deep scalable architecture as search space whose training process consumes most of the search cost. Moreover, time-consuming model training is proportional to the depth of deep scalable architecture. Through experiments using ENAS on CIFAR-10, we find that layer reduction of scalable architecture is an effective way to accelerate the search process of ENAS but suffers from a prohibitive performance drop in the phase of architecture estimation. In this article, we propose a broad neural architecture search (BNAS) where we elaborately design broad scalable architecture dubbed broad convolutional neural network (BCNN) to solve the above issue. On the one hand, the proposed broad scalable architecture has fast training speed due to its shallow topology. Moreover, we also adopt RL and parameter sharing used in ENAS as the optimization strategy of BNAS. Hence, the proposed approach can achieve higher search efficiency. On the other hand, the broad scalable architecture extracts multi-scale features and enhancement representations, and feeds them into global average pooling (GAP) layer to yield more reasonable and comprehensive representations. Therefore, the performance of broad scalable architecture can be promised. In particular, we also develop two variants for BNAS that modify the topology of BCNN. In order to verify the effectiveness of BNAS, several experiments are performed and experimental results show that 1) BNAS delivers 0.19 days which is 2.37x less expensive than ENAS who ranks the best in RL-based NAS approaches;2) compared with small-size (0.5 million parameters) and medium-size (1.1 million parameters) models, the architecture learned by BNAS obtains state-of-the-art performance (3.58% and 3.24% test error) on CIFAR-10;and 3) the learned

关键词： Computer architecture Convolution Graphics processing units Computational modeling Training Topology Data models Broad convolutional neural network (BCNN) image classification neural architecture search (NAS) reinforcement learning (RL)

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Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion for Vessel Traffic Surveillance in Inland Waterways

arXiv

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arXiv 2023年

作者： Guo, Yu Liu, Ryan Wen Qu, Jingxiang Lu, Yuxu Zhu, Fenghua Lv, Yisheng The School of Navigation Wuhan University of Technology Wuhan430063 China The National Engineering Research Center for Water Transport Safety Wuhan430063 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

The automatic identification system (AIS) and video cameras have been widely exploited for vessel traffic surveillance in inland waterways. The AIS data could provide the vessel identity and dynamic information on vessel position and movements. In contrast, the video data could describe the visual appearances of moving vessels, but without knowing the information on identity, position and movements, etc. To further improve vessel traffic surveillance, it becomes necessary to fuse the AIS and video data to simultaneously capture the visual features, identity and dynamic information for the vessels of interest. However, traditional data fusion methods easily suffer from several potential limitations, e.g., asynchronous messages, missing data, random outliers, etc. In this work, we first extract the AIS- and video-based vessel trajectories, and then propose a deep learning-enabled asynchronous trajectory matching method (named DeepSORVF) to fuse the AIS-based vessel information with the corresponding visual targets. In addition, by combining the AIS- and video-based movement features, we also present a prior knowledge-driven anti-occlusion method to yield accurate and robust vessel tracking results under occlusion conditions. To validate the efficacy of our DeepSORVF, we have also constructed a new benchmark dataset (termed FVessel) for vessel detection, tracking, and data fusion. It consists of many videos and the corresponding AIS data collected in various weather conditions and locations. The experimental results have demonstrated that our method is capable of guaranteeing high-reliable data fusion and anti-occlusion vessel tracking. © 2023, CC BY.

关键词： Deep neural networks

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Path Planning of Mobile Robot Based on an Improved Genetic Algorithm 2

Path Planning of Mobile Robot Based on an Improved Genetic A...

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2nd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2022

作者： Chen, Zenghua Xiong, Gang Liu, Sheng Shen, Zhen Li, Yue Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Chang'an University School of Automobile Xi'an China

ISBN: (纸本)9781665492270

In order to solve the problem of premature convergence of the basic genetic algorithm when planning the robot running path, the basic genetic algorithm is improved and optimized. Different population initialization methods are used to initialize multiple populations randomly, so as to improve the diversity of populations;Improve the adaptive strategy and elite strategy of crossover and mutation operators to improve the convergence speed of the algorithm;Add the path tortuosity as the planning index in the fitness function to make the planned path smoother, and add constraints to the model to avoid obstacles;Finally, through the transformation of the coding paradigm of the above improved genetic algorithm, it can run on Flink distributed cluster to obtain faster solution speed, so as to meet the efficiency requirements of path planning in large-scale robot cluster system. The optimized algorithm is compared with the basic genetic algorithm. The simulation results show that the improved algorithm is efficient in robot path planning. © 2022 IEEE.

关键词： Genetic algorithms

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A Semisupervised End-to-End Framework for Transportation Mode Detection by Using GPS-Enabled Sensing Devices

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IEEE INTERNET OF THINGS JOURNAL 2022年第10期9卷 7842-7852页

作者： Li, Zhishuai Xiong, Gang Wei, Zebing Lv, Yisheng Anwar, Noreen Wang, Fei-Yue 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 Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent Cloud Comp Ctr Dongguan 523808 Peoples R China

As an essential component of Internet of Things, GPS-enabled devices record tremendous digital traces, which provide a great convenience for understanding human mobility. How to discover transportation modes efficiently from such valuable sources has come into the spotlight. In this article, the transportation mode detection is treated as a dense classification task, and a similarity entropy-based encoder-decoder (SEED) model is proposed. We first design an encoder-decoder backbone for end-to-end mode detection. Then, a semi-supervised learning module based on similarity entropy is proposed to exploit numerous unlabeled data. Specifically, we stack several convolutional layers as an encoder to capture hierarchical features from fixed-length trajectories, and then adopt transposed convolutional layers as a decoder. For a semi-supervised module, inspired by entropy regularization, we use the K-Means algorithm to cluster prototype vectors from the encoder's predictions. We then fine-tune the encoder by sharpening the similarity distribution between unlabeled predictions and prototypes, aiming to make the former close to one prototype only while staying away from others. A majority-voting post-processing method is used to alleviate jitter impact when inferring. The Experimental results show that SEED significantly outperforms segmentation-then-inference methods. Furthermore, the similarity entropy-based module can improve the generalization performance of the model, and the metrics such as intersection over union can be increased by 5% over baselines. All of these verify the superiority of our method.

关键词： GPS trajectory human mobility semi-supervised learning transportation mode detection (TMD)

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Measurement Invariance Investigation for Performance of Deep Learning Architectures

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IEEE ACCESS 2022年 10卷 78070-78087页

作者： Chen, Dewang Lu, Yuqi Hsu, Chih-Yu Fujian Univ Technol Sch Transportat Fuzhou 350118 Fujian Peoples R China Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Fujian Univ Technol Intelligent Transportat Syst Res Ctr Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Sch Comp Sci & Math Fuzhou 350118 Fujian Peoples R China

Models can be compared against each other. Such comparison helps to find strengths and weaknesses of different approaches and gives additional possibilities for model validation. Multi-class classification is a classification task where each image is assigned to one and only one label. Confusion matrix, Precision, Recall, and F1 Score are popular performance metrics. The common sense is that the performance of any architecture is dependent on the sizes of data set. The goodness of the architecture of deep learning models for different data sets is critical issue. This paper implements Pearson correlation coefficient and the multivariate linear regression method to assess the accuracy of deep learning architectures with five performance indicators. The five performance indicators are training loss rate, robustness, training time, number of model parameters and computation complexity. There are five image datasets used to test four deep learning models: Alexnet, GoogLeNet, ResNet, MobileNet to obtain the values of accuracy and other five indicators. The most important contribution of the article is to show that the accuracy indicator related to training loss rate and training time indicators are not dependent on the selection of the data group. According to the definition of Measurement Invariance (MI), the measurement invariance is demonstrated by the linear regression analysis and inner product of the unit normal vector of the linear regression planes.

关键词： Convolutional neural networks Training Licenses Predictive models Diseases Deep learning Neural networks Deep learning model multivariate linear regression Pearson correlation coefficient measurement invariance performance indicators

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Vibration control for nonlinear overhead crane bridge subject to actuator failures and output constraints

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NONLINEAR DYNAMICS 2020年第1期101卷 419-438页

作者： Xing, Xueyan Yang, Hongjun Liu, Jinkun Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Peoples R China

An active robust adaptive fault-tolerant control protocol is studied for reducing vibration of crane bridge system and handling actuator faults and output constraints simultaneously based on a partial differential equation model. The closed-loop system subject to environmental perturbations and actuator failures can be stabilized with proposed control laws. Furthermore, output constraints of trolley can always be ensured via employing barrier Lyapunov function (BLF), and uncertain actuator faults can also be compensated availably using developed adaptive control laws without any knowledge of actuator fault information. Finally, numerical simulation is provided for illustrating performance of the proposed control method.

关键词： Overhead crane bridge Robust adaptive tolerant control Output constraints Actuator failures Vibration restriction

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Zero-Sum Differential Game-Based Fault-Tolerant Tracking control for Nonlinear systems With Multiplicative Failures

Zero-Sum Differential Game-Based Fault-Tolerant Tracking Con...

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Chinese Automation Congress (CAC)

作者： Cong Tan Mingduo Lin Hongbing Xia Bo Zhao Yancai Xu School of Systems Science Beijing Normal University Beijing China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

A novel fault-tolerant tracking control (FTTC) approach for affine nonlinear systems is developed from the perspective of zero-sum differential games (ZSDG) to deal with unknown multiplicative actuator failures in this paper. By regarding the control input and the multiplicative actuator failure as two players of a zero-sum game, a performance index function reflecting the trajectory tracking error, the control policy and the unknown failure is established. To handle the tracking Hamilton-Jacobi-Isaacs (HJI) equation associated with the established performance index function, a critic neural network is utilized. Then, the Nash equilibrium solution can be obtained, and the FTTC is achieved. The stability of the faulty nonlinear system is examined through Lyapunov-based approach. Finally, simulation results of a spring system is provided to ensure the utility and the realizability of the ZSDG-based FTTC approach.

关键词： Actuators Fault tolerance Trajectory tracking Simulation Fault tolerant systems Stability criteria Mathematical models

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