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605 results on '"Dong, Hairong"'

3. Embodied Footprints: A Safety-guaranteed Collision Avoidance Model for Numerical Optimization-based Trajectory Planning

Author

Li, Bai, Zhang, Youmin, Zhang, Tantan, Acarman, Tankut, Ouyang, Yakun, Li, Li, Dong, Hairong, and Cao, Dongpu

Subjects
Computer Science - Robotics, Mathematics - Optimization and Control
Abstract

Optimization-based methods are commonly applied in autonomous driving trajectory planners, which transform the continuous-time trajectory planning problem into a finite nonlinear program with constraints imposed at finite collocation points. However, potential violations between adjacent collocation points can occur. To address this issue thoroughly, we propose a safety-guaranteed collision-avoidance model to mitigate collision risks within optimization-based trajectory planners. This model introduces an embodied footprint, an enlarged representation of the vehicle's nominal footprint. If the embodied footprints do not collide with obstacles at finite collocation points, then the ego vehicle's nominal footprint is guaranteed to be collision-free at any of the infinite moments between adjacent collocation points. According to our theoretical analysis, we define the geometric size of an embodied footprint as a simple function of vehicle velocity and curvature. Particularly, we propose a trajectory optimizer with the embodied footprints that can theoretically set an appropriate number of collocation points prior to the optimization process. We conduct this research to enhance the foundation of optimization-based planners in robotics. Comparative simulations and field tests validate the completeness, solution speed, and solution quality of our proposal., Comment: 15 pages, 16 figures

Published
2023

8. Neural Network and Collaborative Computing for Image-based Train Location Data Acquisition

Author

Wu, Xiaoqing, Song, Haifeng, Wang, Peng, Cheng, Jianfeng, Zhou, Datian, Dong, Hairong, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Ren, Zhang, editor, Wang, Mengyi, editor, and Hua, Yongzhao, editor

Published
2023
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9. Timetable Rescheduling for High-Speed Railways Considering Dynamical Train Groups in Case of Disturbances

Author

Zhou, Min, Liu, Xuan, Wu, Xingtang, Song, Haifeng, Dong, Hairong, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Ren, Zhang, editor, Wang, Mengyi, editor, and Hua, Yongzhao, editor

Published
2023
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13. Attention-based Supply-Demand Prediction for Autonomous Vehicles

Author

Zhang, Zikai, Li, Yidong, Dong, Hairong, You, Yizhe, and Zhao, Fengping

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

As one of the important functions of the intelligent transportation system (ITS), supply-demand prediction for autonomous vehicles provides a decision basis for its control. In this paper, we present two prediction models (i.e. ARLP model and Advanced ARLP model) based on two system environments that only the current day's historical data is available or several days' historical data are available. These two models jointly consider the spatial, temporal, and semantic relations. Spatial dependency is captured with residual network and dimension reduction. Short term temporal dependency is captured with LSTM. Long term temporal dependency and temporal shifting are captured with LSTM and attention mechanism. Semantic dependency is captured with multi-attention mechanism and autocorrelation coefficient method. Extensive experiments show that our frameworks provide more accurate and stable prediction results than the existing methods., Comment: 5 page, naive version

Published
2019

19. Spreading Dynamics Analysis for Railway Networks

Author

Wu, Xingtang, Yang, Mingkun, Wang, Hongwei, Dong, Hairong, Lü, Jinhu, Song, Haifeng, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhang, Haijun, editor, Yang, Zhi, editor, Zhang, Zhao, editor, Wu, Zhou, editor, and Hao, Tianyong, editor

Published
2021
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22. Preface

Author

Yang, Chenguang, primary, Shi, Yang, additional, Gao, Huijun, additional, and Dong, Hairong, additional

Published
2024
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29. Faded Communication-Based Coordinated Model-Free Adaptive Iterative Learning Control of Multiple HSTs Against Denial-of-Service Attacks

Author

Yu, Wei, Huang, Deqing, and Dong, Hairong

Abstract

The paper studies the faded communication-based coordinated model-free adaptive iterative learning control (MFAILC) of multiple high-speed trains (MHSTs) against periodic denial-of-service (PDoS) attacks. First, considering the nonlinearity and uncertainty of the train operation, the dynamic model of MHSTs is constructed, and then followed by the newly established linear data-relationship model. Next, the random faded channel is expressed by Rice fading model, and the PDoS attacks are introduced with the help of the random coefficients. After giving the theoretical analysis, the compensation scheme is conducted, and the research is further extended to the switching topologies. Finally, a set of numerical tests is conducted to confirm the practicability of the MFAILC approaches. Note to Practitioners—HSTs have the characteristics of high speed, high safety, etc. The practical problems that motivate this work are the complexity of train model, the instability of the networks and the urgent requirement to further improve the operation efficiency. Meanwhile, the possible application areas include the automatic operation of HSTs and the cooperative operation of train groups. Specifically, the potential of this work includes: 1) eliminating the requirement of detailed modeling of train dynamics; 2) providing a theoretical basis for reliable train operation in an unstable network environment, and 3) improving the efficiency of train group operation through cooperation. Nevertheless, the limitation of this paper is that the results have not been verified on the actual trains and railways. To extend it to be more practical, we will further investigate more practical constraints in the train operation environment, such as the constraints of the traction network, the constraints of the track adhesion condition, and also continue to optimize the controller parameters iteratively.

Published
2024
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49. Data-Driven Security Consensus Tracking of Multiple High-Speed Trains Under Random Topologies With Data Recovery Mechanism

Author

Yu, Wei, Huang, Deqing, Wang, Xiao-Lei, and Dong, Hairong

Abstract

Inspired by the challenges in improving train efficiency, this study investigates the consensus tracking of multiple high-speed trains (MHSTs) under random denial-of-service (DoS) attacks. First, a linearization method is used to linearize the nonlinear dynamic model of MHSTs with external running resistance. Second, the strategy of DoS attackers is elaborated on by using random variables obeying Bernoulli distributions. After establishing a data recovery mechanism, a security model-free adaptive control (MFAC) scheme is presented for MHSTs that is fully data-driven and independent of any model information or structural data of train groups. The effectiveness of this approach is theoretically analyzed without requiring accurate system modeling. Finally, the validity of MFAC and the impact of DoS attacks on MHSTs are evaluated through simulations involving G1868 HSTs between Guiyang North and Kaili South.

Published
2024
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