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Adaptive graph transformer with future interaction modeling for multi-agent trajectory prediction

KNOWLEDGE-BASED SYSTEMS 2025年 316卷

作者： Chen, Xiaobo Wang, Junyu deng, fuwen Li, Zuoyong Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Peoples R China Minjiang Univ Sch Comp & Big Data Fuzhou 350121 Peoples R China

Forecasting the trajectories of traffic agents is essential for autonomous systems such as self-driving cars and social robots to guarantee safety in crowded scenarios. Capturing social interactions between agents and generating informative future features bring great challenges to accurate trajectory prediction. To this end, this paper proposes a novel multi-agent trajectory prediction model called AGTFI based on the adaptive graph transformer and future interaction modeling. First, an adaptive graph transformer (AGT) proficient at extracting node and edge features is introduced to capture the complex social interactions between traffic agents. Moreover, a two-stage prediction approach is devised where the first stage is devoted to generating pre-estimated future motion features by bidirectional corrected GRU (BCGRU) and the second stage further incorporates future social interactions into BCGRU to reduce prediction errors. Quantitative and qualitative evaluations of AGTFI on benchmark datasets, including ETH-UCY, SDD, and INTERACTION demonstrate the effectiveness of our model. Ablation studies are conducted to verify the rationale behind the model components.

关键词： Trajectory prediction Graph transformer Attention mechanism Bidirectional decoding

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基于光州世锦赛和东京奥运会亚当·皮蒂100米蛙泳技术分析

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冰雪体育创新研究 2025年第5期6卷 173-175页

作者：许碧薇邓富文广西师范大学广西桂兰541006

该研究运用文献资料法、视频观察法及对比分析法,深入剖析了亚当·皮蒂在2019年光州世锦赛与2021年东京奥运会100米蛙泳比赛中的分段表现、出发、途中游、转身及冲刺技术,并与我国选手闫子贝进行技术对比。结果显示,皮蒂以卓越的速... 详细信息

该研究运用文献资料法、视频观察法及对比分析法,深入剖析了亚当·皮蒂在2019年光州世锦赛与2021年东京奥运会100米蛙泳比赛中的分段表现、出发、途中游、转身及冲刺技术,并与我国选手闫子贝进行技术对比。结果显示,皮蒂以卓越的速度感知与出发反应著称,途中游阶段尤为突出,其高效划水次数与划频成为制胜关键。其划手与腿部动作展现出的显著“减阻”效果,相较于我国选手,动作频率上占据优势。基于此,建议加强“减阻”技术训练,并个性化调整途中游体能分配及划频、划幅策略,以提升竞技水平。

关键词：竞技游泳技术分析划频划幅蛙泳

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Transferable driver facial expression recognition based on joint discriminative correlation alignment network with enhanced feature attention

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IET INTELLIGENT TRANSPORT SYSTEMS 2023年第12期17卷 2444-2457页

作者： Chen, Xiaobo Du, Jian deng, fuwen Zhao, Feng Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai Shandong Peoples R China

Facial expression is closely related to the emotions of drivers, thus facilitating safe driving detection in advanced driving assistance system (ADAS). Recently, deep learning techniques have become prevalent for facial expression recognition. However, for driving scenarios, the facial expression recognition is mainly challenged by the problems of small sample size as well as effective feature representation. To address the above issues, this paper puts forward a transfer learning-based method for driver facial expression recognition by fully exploiting the other sources of facial expression data that may obey different distributions. Specifically, an enhanced feature attention module is firstly devised such that affluent features with multi-scales can be extracted and refined based on the spatial and channel attention mechanisms. Then, a joint correlation alignment loss is presented ensuring that the samples in the source and target domains are transformed into the shared common subspace to reduce difference of both the marginal and conditional distributions. Multiple transfer learning tasks on real-world data are carried out to evaluate the proposed method. The experimental results show that our model achieves better recognition accuracy for driver facial expression compared with several traditional and deep learning-based transfer learning algorithms.

关键词： correlation alignment driver expression recognition spatial-channel attention transfer learning

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A dynamic self-improving ramp metering algorithm based on multi-agent deep reinforcement learning

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TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH 2024年第7期16卷 649-657页

作者： deng, fuwen Jin, Jiandong Shen, Yu Du, Yuchuan Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China Tongji Univ Coll Transportat Engn Shanghai Peoples R China Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Peoples R China

We present a novel ramp metering algorithm that incorporates multi-agent deep reinforcement learning (DRL) techniques, which utilizes monitoring data from loop detectors. Our proposed approach employed a multi-agent DRL framework to generate optimized ramp metering schedules for each ramp meter in real-time, enhancing the operational efficiency of urban freeways with less investment. To simplify the implementation and training of the algorithm, we developed a simulation platform based on SUMO microscopic traffic simulator. We conducted a series of simulation experiments, including local and coordinated ramp metering scenarios with various traffic demands profiles. The simulation results indicate that the proposed DRL-based algorithm outperforms the state-of-the-practice ramp metering methods, considering a comprehensive evaluation index encompassing mainstream speed at the bottleneck and queue length on ramp. Additionally, the method exhibits robustness, scalability, and the potential for further improvement through online learning during implementation.

关键词： ramp metering deep reinforcement learning adaptive control

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Composite Nonconvex Low-Rank Tensor Completion With Joint Structural Regression for Traffic Sensor Networks Data Recovery

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第5期11卷 6882-6896页

作者： Chen, Xiaobo Wang, Kaiyuan Zhao, Feng deng, fuwen Ye, Qiaolin Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Peoples R China Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing 210037 Peoples R China Nanjing Forestry Univ State Key Lab Tree Genet & Breeding Nanjing 210037 Peoples R China

Traffic sensor networks allow convenient collection of travel data that are of great significance for intelligent transportation systems (ITSs). However, the universality of missing data impedes the application of ITS and thus accurate missing data recovery is indispensable in practice. Typically, the global low-rankness and local spatiotemporal smoothness exist in underlying traffic tensor data. In light of this, this article proposes an improved low-rank tensor completion (LRTC) model by exploiting abundant structural information from incomplete tensors. Specifically, a logarithm power composite (LPC)-norm is first proposed as a nonconvex substitute of the rank function, leading to a flexible characterization of tensor multidimensional correlation. Then, a joint structural regression (JSR) model is presented to simultaneously leverage the intrinsic temporal continuity and profile similarity of traffic data. By doing so, we construct a novel nonconvex LRTC model by integrating the global low-rankness and fine-grained spatiotemporal structure that are complementary to each other. To solve the proposed model, following the optimization framework of the alternating direction method of multipliers (ADMMs), we develop an efficient iterative algorithm where each step can be solved in a closed form. Extensive experiments on four real-world traffic data are conducted to evaluate the effectiveness of the proposed approach. The results demonstrate that compared with other tensor completion methods, our model significantly improves the recovery performance.

关键词： Tensors Data models Correlation Spatiotemporal phenomena Probabilistic logic Computational modeling Accuracy Nonconvex surrogate spatial-temporal correlation tensor completion traffic sensor networks

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Stochastic Non-Autoregressive Transformer-Based Multi-Modal Pedestrian Trajectory Prediction for Intelligent Vehicles

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第5期25卷 3561-3574页

作者： Chen, Xiaobo Zhang, Huanjia deng, fuwen Liang, Jun Yang, Jian Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Shandong Peoples R China Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Jiangsu Peoples R China Nankai Univ Coll Comp Sci Tianjin Key Lab Visual Comp & Intelligent Percept Tianjin 300350 Peoples R China

Pedestrian trajectory prediction, which aims at predicting the future positions of all pedestrians in a crowd scene given their past trajectories, is the cornerstone of autonomous driving and intelligent transportation systems. Accurate prediction and fast inference are both indispensable for real-world applications. In this paper, we propose a stochastic non-autoregressive Transformer-based multi-modal trajectory prediction model to address the two challenges. Specifically, a novel graph attention module dedicated to joint learning of social and temporal interaction is proposed to explore the complex interaction among pedestrians while integrating sparse attention mechanism, pedestrian identity, and temporal order contained in the trajectory data. By doing so, the interaction across temporal and social dimensions can be simultaneously processed to extract abundant context features for prediction. Besides, to accelerate inference speed, we put forward a stochastic non-autoregressive Transformer model with multi-modal prediction capability where each future trajectory can be inferred in a parallel fashion, therefore, resulting in diverse trajectory predictions and less computational cost. Extensive experiments and ablation studies are performed to evaluate our approach. The empirical results demonstrate that the proposed model not only produces high prediction accuracy but also infers with fast speed. The code of the proposed method will be publicly available at https://***/xbchen82/SNARTF.

关键词： Pedestrian trajectory prediction graph attention non-autoregressive transformer multi-modal prediction

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Investigation of phononic band gap structures considering interface effects

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PHYSICA B-CONDENSED MATTER 2014年 450卷 121-127页

作者： Zhu, Xingyi Zhong, Sheng Sun, Daquan Ye, Anke deng, fuwen Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai 200092 Peoples R China

Interface imperfection has a significant effect on the wave's propagation behavior in the phononic crystals. To account for the interface imperfection, a numerical simulation based on the boundary element method has been proposed to investigate the propagation of in-plane waves in the phononic crystals with imperfect interfaces. The effects of interface spring stiffness, volume fractions, density ratio, and the shape of the scatters on the band gaps are analyzed. It can be concluded that slightly interface imperfection would not affect band structures too much. However, when the interface is relatively weak, the effect of interface imperfection cannot be neglected. (C) 2014 Elsevier B.V. All rights reserved,

关键词： Phononic crystals Boundary element method Interface effect Band structure Band gap Periodic

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Preferred location strategy for self-equilibrium of free-floating bike-sharing systems 22

Preferred location strategy for self-equilibrium of free-flo...

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22nd International Conference of Hong Kong Society for Transportation Studies: Transport and Society, HKSTS 2017

作者： Du, Yuchuan deng, fuwen Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University China

ISBN: (纸本)9789881581464

Bike-sharing is becoming increasingly important with the promotion of sustainable alternatives to private cars in urban areas. Compared with traditional station-based bike-sharing, free-floating bike-sharing model is adopted more often by newly-built systems due to convenience for users. However, the model may lead to imbalance of the fleet and hence enormous cost will be spent on manual redistribution. In this paper, a novel user-based strategy, preferred parking location, is proposed to achieve self-equilibrium of bike-sharing system. More specifically, users can get pricing benefits if they return shared bicycles in specific stations. The semi-constraint mechanism is expected to decrease the refusing rate caused by fleet imbalance. A simulation-based optimization method is designed to determine the location and capacity of stations then a computational experiment is carried out on a small-scale network to evaluate the effectiveness of this strategy. The proposed strategy can be used in bike-sharing operation and system planning. © 2017 Hong Kong Society for Transportation Studies Limited. All rights reserved.

关键词： Location

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Advanced Self-Improving Ramp Metering Algorithm based on Multi-Agent Deep Reinforcement Learning

Advanced Self-Improving Ramp Metering Algorithm based on Mul...

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IEEE Intelligent Transportation Systems Conference (IEEE-ITSC)

作者： deng, fuwen Jin, Jiandong Shen, Yu Du, Yuchuan Tongji Univ Coll Transportat Engn Shanghai 201804 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China

ISBN: (纸本)9781538670248

We proposed a novel ramp metering algorithm embedding multi-agent deep reinforcement learning (DRL) techniques, based on the data of loop detectors. A multi-agent DRL framework is adopted to generate proper ramp metering scheme for each ramp meter in real time to improve the operation efficiency of urban freeway with less investment. A simulation platform is developed to simplify the implementation and training of the algorithm. A set of simulation experiments encompassing both single and multi-ramp scenarios with various traffic demand profiles - are conducted. Comparing with the state-of-the-practice ramp metering methods, the simulation results demonstrate that the proposed DRL-based algorithm outperforms in a comprehensive evaluation index considering mainstream speed at bottleneck and queue size on ramp. The method presents robustness, scalability, and the capability of further improvement by online learning during implementation.

关键词： ramp metering deep reinforcement learning adaptive control

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Pavement distress detection and classification based on YOLO network

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INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING 2021年第13期22卷 1659-1672页

作者： Du, Yuchuan Pan, Ning Xu, Zihao deng, fuwen Shen, Yu Kang, Hua Tongji Univ Minist Educ Key Lab Rd & Traff Engn Shanghai Peoples R China Tongji Univ Key Lab Embedded Syst & Serv Comp Shanghai Peoples R China

The detection and classification of pavement distress (PD) play a critical role in pavement maintenance and rehabilitation. Research on PD automation detection and measurement has been actively conducted. However, types of PD are more necessary for road managers to take effective actions. Also, lack of a unified PD dataset leads to absence of a benchmark on various methods. This study makes three contributions to address these issues. Firstly, a large-scale PD dataset is prepared. This dataset is composed of 45,788 images captured with a high-resolution industrial camera installed on vehicles, in a variety of weather and illuminance conditions. Each image is annotated with bounding box representing location and type of distress. Secondly, a deep learning-based object detection framework, the YOLO network, is adopted to predict possible distress location and category. Comprehensive detection accuracy reaches 73.64%. The processing speed reaches 0.0347s/pic, as 9 times faster than Faster R-CNN and only 70% of SSD. Finally, the applicability of model under various illumination conditions is also explored. The results reveal that the method significantly outperforms with appropriate illumination. We conclude that the proposed YOLO-based approach is able to detect PD with high accuracy, which requires no manual feature extraction and calculation during detecting.

关键词： Pavement distress object detection image classification YOLO network

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