| 杨彪 工学博士、副教授 |
15895060792 | |
E-MAIL: yb6864171@cczu.edu.cn |
教育背景: |
2005.09-2009.07 南京工业大学 自动化学院自动化专业 工学学士 2009.09-2014.11东南大学 仪器科学与工程学院仪器科学与技术 工学博士 |
研究领域: |
人工智能模式识别具身智能 |
学术成果: |
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[1]PCIP Towards Real-Time Pedestrian Crossing Intention Prediction with Adaptive Fusion and Temporal Dynamics Encoding[J]. IEEE Transactions on Intelligent Transportation Systems, 2026. [2] MMFlow: Multimodal pedestrian trajectory prediction based on Mambaformer and one-step Mean Flow[J]. Expert Systems with Applications, 2026: 132057. [3] Graph Attention Transformer with Multi-Task Learning for Motion Prediction in Autonomous Driving[J]. IEEE Transactions on Artificial Intelligence, 2026. [4] Privacy-aware pedestrian trajectory prediction with dual-channel destination guidance and multi-factor aggregation federated learning[J]. Transportmetrica B: Transport Dynamics, 2025, 13(1): 2601598. [5] Mul-vmamba: multimodal semantic segmentation using selection-fusion-based vision-mamba[J]. Knowledge-Based Systems, 2025: 115119. [6] Adaptive Progressive Transformer-Based Trajectory Prediction Under Fine-Grained Trajectory-Scene Interaction Constraint[J]. IEEE Transactions on Automation Science and Engineering, 2025, 22: 24498-24509. [7] Egocentric Pedestrian Trajectory Prediction With Agent-Wise Motion Fusion for Internet of Vehicles[J]. IEEE Internet of Things Journal, 2025. [8] A cvae combined with diffusion mechanism to pedestrian trajectory prediction[J]. IEEE Robotics and Automation Letters, 2025. [9] Visually multimodal depression assessment based on key questions with weighted multi-task learning[J]. Signal Processing: Image Communication, 2025, 135: 117279. [10] A CVAE Combined With Diffusion Mechanism to Pedestrian Trajectory Prediction[J]. IEEE Robotics and Automation Letters, 2025. [11] Interpretable Multi-Task Prediction Neural Network for Autonomous Vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2025. [12]Asymmetric multimodal guidance fusion network for realtime visible and thermal semantic segmentation[J]. Engineering Applications of Artificial Intelligence, 2025, 142: 109881. [13] Hybrid Attention-based Multi-task Vehicle Motion Prediction Using Non-Autoregressive Transformer and Mixture of Experts[J]. IEEE Transactions on Intelligent Vehicles, 2024. [14] Probabilistic trajectory prediction of vulnerable road user using multimodal inputs[J]. IEEE Transactions on Intelligent Transportation Systems, 2024. [15] Dynamic subclass-balancing contrastive learning for long-tail pedestrian trajectory prediction with progressive refinement[J]. IEEE Transactions on Automation Science and Engineering, 2024. [16] Real-time pedestrian crossing anticipation based on an action–interaction dual-branch network[J]. IEEE Transactions on Intelligent Transportation Systems, 2024. [17] UDA-KB: Unsupervised domain adaptation RGB-Thermal semantic segmentation via knowledge bridge[C]//Chinese Conference on Pattern Recognition and Computer Vision (PRCV). Singapore: Springer Nature Singapore, 2024: 61-74. [18] Explainable pedestrian crossing intention prediction based on multi-task mutual guidance network[J]. IEEE Transactions on Intelligent Vehicles, 2024. [19] MMPF: multimodal purification fusion for automatic depression detection[J]. IEEE Transactions on Computational Social Systems, 2024, 11(6): 7421-7434. [20] Unlocking human-like facial expressions in humanoid robots: A novel approach for action unit driven facial expression disentangled synthesis[J]. IEEE Transactions on Robotics, 2024, 40: 3850-3865. [21] Meta-IRLSOT++: A meta-inverse reinforcement learning method for fast adaptation of trajectory prediction networks[J]. Expert Systems with Applications, 2024, 240: 122499. [22] A federated pedestrian trajectory prediction model with data privacy protection[J]. Complex & Intelligent Systems, 2024, 10(2): 1787-1799. [23] Fast adaptation trajectory prediction method based on online multisource transfer learning[J]. IEEE Transactions on Automation Science and Engineering, 2024. [24] Faster pedestrian crossing intention prediction based on efficient fusion of diverse intention influencing factors[J]. IEEE Transactions on Transportation Electrification, 2024, 10(4): 9071-9087. [25] FRPNet: An improved Faster-ResNet with PASPP for real-time semantic segmentation in the unstructured field scene[J]. Computers and Electronics in Agriculture, 2024, 217: 108623. [26] TPPO: a novel trajectory predictor with pseudo oracle[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024, 54(5): 2846-2859. [27] A multi-task learning network with a collision-aware graph transformer for traffic-agents trajectory prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(7): 6677-6690. [28] DPCIAN: A novel dual-channel pedestrian crossing intention anticipation network[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 25(6): 6023-6034. [29] Uncertainty-aware label contrastive distribution learning for automatic depression detection[J]. IEEE Transactions on Computational Social Systems, 2023, 11(2): 2979-2989. [30] Diverse local facial behaviors learning from enhanced expression flow for microexpression recognition[J]. Knowledge-Based Systems, 2023, 275: 110729. [31] Pedestrians crossing intention anticipation based on dual‐channel action recognition and hierarchical environmental context[J]. IET Intelligent Transport Systems, 2023, 17(2): 255-269. [32] Multi-granularity scenarios understanding network for trajectory prediction[J]. Complex & Intelligent Systems, 2023, 9(1): 851-864. [33] Continual learning-based trajectory prediction with memory augmented networks[J]. Knowledge-Based Systems, 2022, 258: 110022. [34] Stability analysis of delayed-feedback control effect in the continuum traffic flow of autonomous vehicles without V2I communication[J]. Physica A: Statistical Mechanics and Its Applications, 2022, 605: 127975. [35] TrajGAT: A map-embedded graph attention network for real-time vehicle trajectory imputation of roadside perception[J]. Transportation research part C: emerging technologies, 2022, 142: 103787. [36] IRLSOT: Inverse reinforcement learning for scene‐oriented trajectory prediction[J]. IET Intelligent Transport Systems, 2022, 16(6): 769-781. [37] Facial expression recognition through cross-modality attention fusion[J]. IEEE Transactions on Cognitive and Developmental Systems, 2022, 15(1): 175-185. [38] A novel graph-based trajectory predictor with pseudo-oracle[J]. IEEE transactions on neural networks and learning systems, 2021, 33(12): 7064-7078. [39] Crossing or not? Context-based recognition of pedestrian crossing intention in the urban environment[J]. IEEE transactions on intelligent transportation systems, 2021, 23(6): 5338-5349. [40] An adversarial learned trajectory predictor with knowledge-rich latent variables[C]//Chinese Conference on Pattern Recognition and Computer Vision (PRCV). Cham: Springer International Publishing, 2020: 42-53. [41] Counting crowds using a scale-distribution-aware network and adaptive human-shaped kernel[J]. Neurocomputing, 2020, 390: 207-216. [42] Vision-based in situ monitoring of plankton size spectra via a convolutional neural network[J]. IEEE Journal of Oceanic Engineering, 2019, 45(2): 511-520. [43] Anomalous behaviors detection in moving crowds based on a weighted convolutional autoencoder-long short-term memory network[J]. IEEE Transactions on Cognitive and Developmental Systems, 2018, 11(4): 473-482. [44] Counting challenging crowds robustly using a multi-column multi-task convolutional neural network[J]. Signal Processing: Image Communication, 2018, 64: 118-129. |
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研究项目: |
2026.01-2029.12，国家自然科学基金面上项目，编号：62576052，主持 2020.05-2023.04，中国博士后科学基金面上项目，编号：2021M701042，主持 2020.05-2023.04，江苏省博士后科学基金面上项目，主持 2022.07-2025.06，江苏省科技厅面上项目，编号：BK20221380，主持 2023.01-2025.01，常州市社会发展项目，编号：CE20235037，主持 2020.05-2022.05，常州市应用基础研究计划，编号：CJ20200083，主持 2017.04-2019.03，江苏省教育厅面上项目，编号：18KJB520003，主持 2015.07-2018.06，江苏省科技厅青年项目，编号：BK20150271，主持 2016.01-2018.12，国家自然科学基金青年项目，编号：61501060，主持 |
