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Dynamic Acceleration-Aware Risk Potential Field Car-Following Model for Heterogeneous Individuals

Dynamic Acceleration-Aware Risk Potential Field Car-Followin...

引用

IEEE Conference on Wireless Communications and networking

作者： Yuchen Liu Chengyu Wang Zhengrui Shi Luhan Wang Yawen Chen Sirui Li Xiangming Wen Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In the developing phase of connected autonomous driving, roads are populated with heterogeneous individuals, i.e., connected and unconnected vehicles, autonomous driving and human driving, resulting in intricate interactions and significant uncertainty in their motion trajectories. At present, the research of modeling under mixed traffic scenarios has the following challenges: 1) Traditional path planning ignores dynamic risks from the third-order vehicle state, i.e., acceleration; 2) Existing microscopic models lack the descriptions for the diverse decision-making patterns among heterogeneous individuals, which increases the uncertainty and complexity of the scene. In this paper, we propose a novel Risk Potential Field car-Following Model (RPFFM) tailored for heterogeneous individuals. It employs differentiated parameter adjustments, integrating forward propulsion with potential field repulsion to calculate vehicle acceleration. RPFFM achieves proactive and safe car-following by perceiving the change of dynamic obstacle acceleration through dynamic factors and converting it into the variation of the dynamic potential field. Experimental results based on the Next Generation Simulation (NGSIM) dataset demonstrate that the RPFFM surpasses both the Intelligent Driver Model (IDM) and Optimal Velocity Model (OVM) in terms of effectiveness, safety, and providing enhanced car-following comfort.

关键词： Adaptation models Uncertainty Microscopy Propulsion Road safety Safety Trajectory Vehicle dynamics Autonomous vehicles Vehicles

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Sum-Rate Maximization for Active RIS-Aided Downlink RSMA system

Sum-Rate Maximization for Active RIS-Aided Downlink RSMA Sys...

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2023 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Li, Xinhao Wang, Tao Tong, Haonan Yang, Zhaohui Mao, Yijie Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Information Science and Technology ShanghaiTech University Shanghai China

ISBN: (纸本)9781665494274

In this paper, the problem of sum-rate maximization for an active reconfigurable intelligent surface (RIS) assisted downlink rate-splitting multiple access (RSMA) transmission system is studied. In the considered model, the active RIS is deployed to overcome severe power attenuation, which is caused by the cumulative product of RIS incidence path loss and the reflection path loss. Since the active RIS can adjust both the phase and the amplitude of the incident signal simultaneously, the RIS control scheme requires delicate design to improve RSMA communication performance. To address this issue, a sum-rate maximization problem is formulated to jointly optimize the beamforming vectors, rate allocation vector, and RIS precoding matrix. To solve this non-convex sum-rate maximization problem, an iterative algorithm based on fractional programming (FP) and quadratic constraint quadratic programming (QCQP) is proposed. In particular, the proposed algorithm firstly decomposes the original problem into two subproblems, namely, 1) beamforming and rate allocation optimization and 2) active RIS precoding optimization. The corresponding variables of the two subproblems are optimized through sequential convex approximation (SCA) and block coordinate descent (BCD), respectively. Numerical results show that the proposed active RIS-aided RSMA system could increase the sum-rate by up to 45% over the conventional passive RIS-aided RSMA system with the same energy consumption. © 2023 IEEE.

关键词： Iterative methods

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Attention Interactive Graph Convolutional network for Skeleton-Based Human Interaction Recognition

Attention Interactive Graph Convolutional Network for Skelet...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Gao, Feng Xia, Hailun Tang, Zhihao Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing100876 China Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing100876 China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Skeleton-based Human interaction action recognition is a great challenge in the field of human activity analysis. The existing graph convolutional networks (GCNs) based recognition methods ignore the spatial-temporal interactive features between two persons. Therefore, we propose Attention Inter-active Graph Convolutional network(AIGCN) to explore hu-man interaction. First, we design an Interactive Attention En-coding GCN(IAE-GCN) module to extract interactive spatial structures, reflecting their influence on each other by regarding joint position encoding as semantic information. Second, we propose Interactive-Attention Mask TCN (IAM-TCN) that extracts temporal interactive features, representing temporal attentional excitation signal among the different joints of dif-ferent people along the time. Finally, we verify the effectiveness of our method on classic human interaction datasets SBU and interactive action sub-datasets of NTU-RGB+D and NTU-RGB+D 120, and experiment shows that our models achieve state-of-the-art performance. © 2022 IEEE.

关键词： Convolution

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Latency-Aware Server Deployment in Internet of Vehicles Based on Multi-Agent Reinforcement Learning

Latency-Aware Server Deployment in Internet of Vehicles Base...

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IEEE Conference on Wireless Communications and networking

作者： Xinran Liu Yaqiong Liu Junsheng Mu Guochu Shout Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The performance of the servers directly affects the efficiency of the whole intelligent Internet of Vehicles (lo V) system. A reasonable server deployment strategy can effectively reduce network latency and improve the efficiency of ***, it is necessary to study the problem of server deployment in Io *** this paper, we formulate a latency-aware server deployment problem based on Multi-Agent Reinforcement Learning (MARL) and utilize an Actor-Critic network based on Long Short-Term Memory (LSTM) encoding network to solve it. Besides, four deployment strategies (DQN, DRQN, Random and No Movement) are used as the baseline methods for performance comparison with our proposed method on two real-world datasets. The simulation results show our solution has excellent performance in reducing network latency under different scenarios.

关键词： Training Simulation Transportation Reinforcement learning Encoding Stability analysis Servers

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Precise Timing Based on Software-Defined Time-Sensitive networking in Smart City

Precise Timing Based on Software-Defined Time-Sensitive Netw...

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IEEE International Conference on Communications in China (ICCC)

作者： Mufeng Zhang Guochu Shou Hongxing Li Yaqiong Liu Yihong Hu Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Communications Beijing China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Smart city infrastructure, as entities with multisensory perception and heterogeneous data fusion, presents new temporal challenges. With the increasing reliance on applications such as Cyber-Physical systems (CPS) for real-time interaction, high-frequency financial transactions, data centers, digital twins, augmented reality, and the metaverse, there is a critical need for data with accurate timestamps. This paper proposes a precise timing scheme based on Software-Defined Time-Sensitive networking (SD-TSN), utilizing a paradigm combining SDN and TSN to address unified control and management of data timestamping across diverse smart city scenarios. By employing software timestamping at time-sensitive nodes, sub-millisecond precision is achieved, while hardware timestamping achieves sub-microsecond precision. Leveraging standardized TSN networks economically and effectively fulfills the timing precision requirements for data timing needs in different scenarios.

关键词： Accuracy Smart cities Metaverse Diversity reception Data integration Software Hardware

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Clock Drift and Time Error Compensation with Neural network for Time Synchronization in TSN

Clock Drift and Time Error Compensation with Neural Network ...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Yiheng Yang Chenlong Yao Guochu Shou Yaqiong Liu Zhifu Li Hongxing Li Yihong Hu Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

TSN implements time synchronization through IEEE 802.1AS protocol, and the performance of time synchronization is related to the frequency accuracy and stability of each TSN clock. However, the problems of clock drift and timestamp granularity error (TSGE) will degrade the network time synchronization performance. This paper analyzes the causes of time errors caused by network time synchronization, and puts forward the overall scheme to improve the performance of time synchronization. An intelligent compensation algorithm for clock drift is designed, which includes measuring node clock correlation data, training neural network, using neural network to make prediction and compensating time stamp. Experimental results show that the proposed algorithm is stable and accurate in time synchronization.

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Continuous Trajectory Tracking Across Sensors at Intersection using Dynamic Target Set within the Overlapping Area

Continuous Trajectory Tracking Across Sensors at Intersectio...

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International Conference on Intelligent Transportation

作者： Xianning Cheng Jiayin Deng Zhiqun Hu Zhaoming Lu Xiangming Wen Beijing Key Laboratory for Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

Across-sensor tracking is an important technology to realize global perception in the field of next-generation intelligent transportation. However, identifying targets within an overlapping area and merging trajectories from different sensors remain challenging. Previous methods require high labor costs to manually identify an overlapping area and neglect the problem of noise and occlusion when data present inconsistency. To address these issues, this paper proposes a novel across-sensor tracking method at intersection. The proposed method adaptively identifies the target set for the overlapping area based on the Gaussian product of target states from two sensors and fuses trajectories based on Arithmetic Average algorithm. Additionally, the paper improves the birth model of the Poisson Multi-Bernoulli Mixture filter in order to handle complex intersection situations. Extensive simulation and real-world experiments evaluate the method's effectiveness, and its superior performance compared to previous methods.

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Image Segmentation Semantic Communication over Internet of Vehicles

Image Segmentation Semantic Communication over Internet of V...

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IEEE Conference on Wireless Communications and networking

作者： Qiang Pan Haonan Tong Jie Lv Tao Luo Zhilong Zhang Changchuan Yin Jianfeng Li Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

In this paper, the problem of semantic-based efficient image transmission is studied over the Internet of Vehicles (IoV). In the considered model, a vehicle shares massive amount of visual data perceived by its visual sensors to assist other vehicles in making driving decisions. However, it is hard to maintain a high reliable visual data transmission due to the limited spectrum resources. To tackle this problem, a semantic communication approach is introduced to reduce the transmission data amount while ensuring the semantic-level accuracy. Particularly, an image segmentation semantic communication (ISSC) system is proposed, which can extract the semantic features from the perceived images and transmit the features to the receiving vehicle that reconstructs the image segmentations. The ISSC system consists of an encoder and a decoder at the transmitter and the receiver, respectively. To accurately extract the image semantic features, the ISSC system encoder employs a Swin Transformer based multi-scale semantic feature extractor. Then, to resist the wireless noise and reconstruct the image segmentation, a semantic feature decoder and a reconstructor are designed at the receiver. Simulation results show that the proposed ISSC system can reconstruct the image segmentation accurately with a high compression ratio, and can achieve robust transmission performance against channel noise, especially at the low signal-to-noise ratio (SNR). In terms of mean Intersection over Union (mIoU), the ISSC system can achieve an increase by 75%, compared to the baselines using traditional coding methods.

关键词： Wireless communication Image segmentation Visualization Wireless sensor networks Image communication Semantics Feature extraction

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Beam Tracking Based on Multi-Point Radar Sensing and DNN Prediction

Beam Tracking Based on Multi-Point Radar Sensing and DNN Pre...

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IEEE International Conference on network Infrastructure and Digital Content (IC-NIDC)

作者： Kean Chen Danpu Liu Zhilong Zhang Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

Millimeter-Wave (mmWave) communication based on massive MIMO and beamforming has become a hotspot owing to its advantage on capacity. However, a rapidly varying mm Wave channel in high mobility scenario will lead to frequent beam switching and high signaling overhead. Integrated Sensing and Communication (ISAC) gives a new idea to the above challenges by using radar sensing to assist beam management. Nevertheless, most of the sensing -assisted beam tracking are limited to the scenario where Line-of-Sight (LOS) paths exist, and fail when a blockage occurs. To address this issue, a beam tracking scheme based on multi-point radar sensing and Deep Neural network (DNN) is proposed in this paper, where the sensing information from multiple radars are aggregated and a DNN is utilized to predict the beam direction by learning the channel nonlinear correlation in a specific environment. The simulation results indicate that the proposed scheme operates effectively in blockage scenario, and surpasses the baselines in the field of spectral efficiency.

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Graph Optimization Based Localization Algorithm by Fusing VLP and VO 15

Graph Optimization Based Localization Algorithm by Fusing VL...

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15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Guo, Minan Jia, Bowen Feng, Chunyan Zhu, Zhiyu Yang, Yang Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China China Electronics Standardization Institute Beijing100176 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China

ISBN: (纸本)9798350324662

Visible light positioning (VLP) has attracted considerable interest due to its high accuracy and low cost. However, VLP typically requires several light emitting diodes (LEDs) for positioning, which may lead to positioning failure in sparse LED layout or parts of the links are blocked. To address this challenge, we propose a novel graph optimization-based localization algorithm by fusing VLP and visual odometry (VO), termed GO-VLO. In the considered model, a few LEDs are deployed on the ceiling and broadcast positioning information, while a receiver equipped with a camera conducts GO-VLO to achieve precise positioning with limited LEDs. GO-VLO consists of a front end to process positioning information and a back end to further optimize the positioning results. In particular, VLP and VO are first used separately at the front end, thus obtaining two respective positioning results. Then, a visible-light visual odometry fusion mechanism is proposed to fuse the positioning results of VLP and VO preliminarily. To strengthen the constraints between frames and prevent location failure when VLP is unavailable, a novel graph optimization framework is further proposed in the back end, which notably takes the covisible feature points of two successive frames into consideration. Experimental results show that our proposed method attains a positioning accuracy of 4.93 cm in two dimension and 6.20 cm in three dimension under dense LED layout. © 2023 IEEE.

关键词： pose graph SLAM visible light positioning visual positioning

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