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A Digital Twin and Consensus Empowered Cooperative Control Framework for Platoon-Based Autonomous Driving

清华大学学报自然科学版（英文版） 2025年第3期30卷 1096-1111页

作者： Jiayu Cao Supeng Leng Kai Xiong Xiaosha Chen School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110China College of Computer Science and Technology and also with College of Data Science Taiyuan University of TechnologyTaiyuan 030024China

Platoon-based autonomous driving is indispensable for traffic automation,but it confronts substantial constraints in rugged terrains with unreliable links and scarce communication *** paper proposes a novel hierarchical Digital Twin(DT)and consensus empowered cooperative control framework for safe driving in harsh ***,leveraging intra-platoon information exchange,one platoon-level DT is constructed on the leader and multiple vehicle-level DTs are distributed among platoon *** leader first makes critical platoon-driving decisions based on the platoon-level ***,considering the impact of unreliable links on the platoon-level DT accuracy and the consequent risk of unsafe decision-making,a distributed consensus scheme is proposed to negotiate critical decisions *** successful negotiation,vehicles proceed to execute critical decisions,relying on their vehicle-level ***,a Space-Air-Ground-Integrated-Network(SAGIN)enabled information exchange is utilized to update the platoon-level DT for subsequent safe decision-making in scenarios with unreliable links,no roadside units,and obstructed ***,based on this framework,an adaptive platooning scheme is designed to minimize total delay and ensure driving *** results indicate that our proposed scheme improves driving safety by 21.1％and reduces total delay by 24.2％in harsh areas compared with existing approaches.

关键词： platoon autonomous driving distributed consensus digital twin space-air-ground integrated network

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Research on Feature Extraction Algorithm based on Meta-learning 3

Research on Feature Extraction Algorithm based on Meta-learn...

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3rd International Seminar on Artificial Intelligence, Networking, and Information Technology, AINIT 2022

作者： Jin, Yanliang Fan, Baorong Gao, Yuan Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Shanghai University Shanghai China

ISBN: (数字)9781510662971

ISBN: (纸本)9781510662964

Feature extraction is an important research topic in the field of image *** autonomous driving, it is of great importance to extract the feature information of the picture obtained by the vehicle camera for the agent to better understand the environment information. In order to improve the quality of feature extraction, this paper combines meta-learning and deep learning-based feature extraction methods, and proposes a Meta-VAE-WGAN-GP (MVWP) feature extraction algorithm, and applies it to automatic driving. Firstly, aiming at the problem of parameter centralization in Wasserstein generative adversarial network (WGAN) and the problem of gradient explosion and gradient disappearance caused by improper manual parameter adjustment, a generative adversarial network based on gradient penalty and Wasserstein distance (WGAN-GP) was proposed, and it was combined with VAE. The VAE-WGAN-GP model is ***, aiming at the problem that the feature extraction model needs to be trained from scratch every time it is faced with a new task, and the training time is too long, the MVWP model is formed by combining meta-learning with VAE-WGAN-GP (VWP) mentioned above. Finally, the experimental results show that compared with VAE, VAE-WGAN and VWP, the training speed of MVWP model is increased by about 6 times, the reconstruction loss is reduced by 55.9%, 37.8% and 20.2%, respectively, and the reconstructed images are clearer. © 2023 SPIE.

关键词： Feature extraction

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Multi-scale feature fusion for facial expression recognition

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Neural Computing and Applications 2025年第17期37卷 11399-11420页

作者： Guo, Jiatao Peng, Junjie Huang, Yansong Chen, Gan Cai, Zesu Tan, Shuhua School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China National Engineering Laboratory for Logistics Information Technology YTO Express Co. Ltd. Shanghai China

As an important computer vision task that can be used in many areas, facial expression recognition (FER) has been widely studied which much progress has been obtained especially when deep learning (DL) approaches have been introduced in FER. However, existing methods are restricted to extracting localized information or utilizing attention mechanism to focus on the region most relevant to the emotion, possibly resulting in errors in facial expression interpretation. Moreover, the inconsistency of image sizes in the wild FER datasets further increases the difficulty of the FER task. To address these issues, we propose a multi-scale feature fusion (MSFF) for FER. Specifically, our method first extracts features from facial images using multi-scale feature extraction to obtain rich multi-dimensional information. Meanwhile, extensive feature extraction at different scales is achieved by expanding the receptive field of the model, which provides the basis for more detailed analysis of facial expressions. Eventually, features at different scales are fused to enhance their synergistic effect. Subsequently, the adaptive extraction of comprehensive and valuable information from the fused features is performed, optimizing the performance of expression recognition. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on in-the-wild datasets. It is promising for FER in realistic and complex scenarios. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature extraction

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A Lightweight Model for Encrypted Traffic Classification through Sequence Modeling 3

A Lightweight Model for Encrypted Traffic Classification thr...

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3rd International Seminar on Artificial Intelligence, Networking, and Information Technology, AINIT 2022

作者： Jin, Yanliang Chen, Yantao Gao, Yuan Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Shanghai University Shanghai China

ISBN: (数字)9781510662971

ISBN: (纸本)9781510662964

With the increasing awareness of privacy protection in recent years, various encryption techniques are gradually applied to network traffic, which makes encrypted traffic classification an indispensable part of network management. Recent studies show that the approaches based on deep learning are compelling for the traffic classification task. However, most of them take the encrypted payload as input, which not only requires high computational overhead to make classification, but also limits the performance improvement due to the unavailability of the plaintext. In this paper, we treat the encrypted traffic as sequences and solve the classification task from the perspective of sequence modeling, which only depends on several sequence fields obtained from the traffic header. We properly design a lightweight model and name it TGA by its structure, which consists of a temporal convolutional network (TCN), a gated recurrent unit (GRU) and the attention mechanism. TGA first mines short-term features from the input by employing the TCN, and next captures the long-term dependencies by applying the GRU, and finally focuses on valuable features through dynamic assignment of attention weights. Through these three steps, TGA is expected to obtain the most effective but lightest temporal features. Experimental results on the public dataset demonstrate that TGA shows superiority in terms of classification accuracy and time efficiency, especially the number of parameters is decreased to at most 30% of the state-of-the-art models. © 2023 SPIE.

关键词： Classification (of information)

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Mode Converters of LP51 Core Mode Coupling Based on Long-Period Fiber Gratings

Mode Converters of LP51 Core Mode Coupling Based on Long-Per...

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2024 Asia communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical communications, IPOC 2024

作者： Chen, Siyu Fan, Xiaolong Mou, Chengbo Liu, Yunqi The Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China

We demonstrate the inscription of multi-cascaded long-period fiber grating in a few-mode fiber using a CO2 laser. The mode conversion from LP01-LP11 mode, LP11-LP21 mode, LP21-LP31 mode, LP31-LP41 mode, and LP41-LP51 ... 详细信息

ISBN: (纸本)9798350379266

关键词： Carbon dioxide lasers

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Multiclass Malicious URL Attack Type Detection Via Capsule-based Neural Network 3

Multiclass Malicious URL Attack Type Detection Via Capsule-b...

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3rd International Seminar on Artificial Intelligence, Networking, and Information Technology, AINIT 2022

作者： Jin, Yanliang Yu, Xiaoqi Gao, Yuan Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Shanghai University Shanghai China

ISBN: (数字)9781510662971

ISBN: (纸本)9781510662964

Despite the variety of cybercrimes, malicious Uniform Resource Locators (URLs) remain one of the most common threats to cybersecurity and bring huge economic losses every year. How to detect malicious URLs accurately has attracted great interests from both academia and industry. However, few focus on the multiclass malicious URL attack type detection and existing methods cannot provide robust performance due to the diversity of obfuscation strategies. In this paper, we propose a capsule-based deep neural network for malicious URL detection and classification, using character-level information from the URL string sequences. To be specific, our method transforms an input URL into character-level embedding representation firstly, then passes it into the designed convolution module to extract local features of different sizes and the local features are fed into the designed capsule module to retain the spatial hierarchical relationship of the URL string, extract accurate feature representation and output the accurate classification result finally. The experimental results on a public dataset constructed by four different classes of URLs show that compared with other baseline methods, our capsule-based method can achieve better detection and classification results, with F1-score of benign URL, malware URL, defacement URL and phishing URL at 98.94%, 95.81%, 99.63% and 94.04%, respectively. Due to the excellent performance of our capsule-based method for the detection of malicious URLs, it could be deployed in the main-stream web browsers to identify URL attack types and intercept malicious URLs effectively to protect vulnerable users against cyberattacks. © 2023 SPIE.

关键词： Classification (of information)

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Sensing Characteristics of Long Period Fiber Grating Written in Graded Index Fiber by CO2Laser 22

Sensing Characteristics of Long Period Fiber Grating Written...

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22nd International Conference on Optical communications and Networks, ICOCN 2024

作者： Hang, S. Liu, Yunqi Yuehui, M. Mou, Chengbo Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China

We present a long period fiber grating, which can measure temperature, curvature, refractive index, torsion, and the corresponding sensitivities are 500.71 pm/°C, -16.53 nm m-1, -2776.19 nm/RIU, and -193.65 pm/(r... 详细信息

ISBN: (纸本)9798350367652

关键词： graded index few mode fiber Long period fiber grating optical fiber sensor

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Demonstration of an ECL Wavelength Stabilization System for Coherent Free-Space Optical communication

Demonstration of an ECL Wavelength Stabilization System for ...

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2024 Asia communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical communications, IPOC 2024

作者： Yu, Zongbi Li, Hao Qu, Yuanzhe Mo, Weiwei Song, Yingxiong Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China

We propose an ECL wavelength stabilization system for coherent free-space optical communication. Experimental results demonstrate that the system achieves wavelength stability of 0.0010 nm, and a Doppler frequency shi... 详细信息

ISBN: (纸本)9798350379266

关键词： Space optics

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Mode Converter Based on the Long-Period Fiber Grating Inscribed in Graded Index Few Mode Fiber

Mode Converter Based on the Long-Period Fiber Grating Inscri...

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2024 Asia communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical communications, IPOC 2024

作者： Su, Hang Liu, Yunqi Ma, Yuehui Mou, Chengbo The Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China

A mode converter based on the long-period fiber grating is investigated, which is inscribed in graded-index few-mode fiber. The coupling from the fundamental mode to higher-order core mode (LP21) has been achieved. &#... 详细信息

ISBN: (纸本)9798350379266

关键词： graded-index few-mode fiber long-period fiber grating mode converter

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High-Order-Mode Multi-Wavelength Random Fiber Laser Based on Brillouin and Rayleigh Scattering in Few Mode Fibers 22

High-Order-Mode Multi-Wavelength Random Fiber Laser Based on...

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22nd International Conference on Optical communications and Networks, ICOCN 2024

作者： Li, Yichun Zhong, Zepeng Wei, Zizhou Zhu, Mengshi Wei, Heming Zeng, Xianglong Wen, Jianxiang Pang, Fufei Wang, Tingyun Zhang, Liang Shanghai Institute for Advanced Communication and Data Science Shanghai University Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai200444 China

We demonstrated a multi-wavelength random fiber laser with high-order spatial mode emission by utilizing stimulated Brillouin scattering gain and distributed Rayleigh scattering in few-mode fibers. © 2024 IEEE.

ISBN: (纸本)9798350367652

关键词： Stimulated Brillouin scattering

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