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Jiayu Guo - IEEE Xplore Author Profile

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Author details

Jiayu Guo

Publications

7

Citations

37

Publications by Year

20232025

Co-Authors:

Ying ChengJinsheng DouMingyue FengRui FengDi Feng

Show All Co-Authors (26)

Jiayu Guo

Affiliation

School of Computer Science

Shanghai Key Laboratory of Intelligent Information Processing

Fudan University, Shanghai, China

Publication Topics

Trajectory Prediction,
Attention Mechanism,
Fusion Block,
Future Trajectories,
Local Fusion,
Model Performance,
Neural Network,
Prediction Task,
Training Set,
Ability Of The Model,
Attention Block,
Auxiliary Model

Biography

Jiayu Guo received the bachelor of science degree from Wuhan University in 2020. He is currently pursuing the Ph.D. degree with ISTBI, Fudan University, China. He is interested in transformer-based models for trajectory prediction and robust prediction across different scenarios.(Based on document published on 3 January 2023).

Publications

7

Citations

37

Publications by Year

20232025

Co-Authors:

Ying Cheng
Jinsheng Dou
Mingyue Feng
Rui Feng
Di Feng

Show All Co-Authors (26)

Author's Published Works

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Showing 1-7 of 7 results

Conferences (3)

Early Access Articles (2)

Journals (2)

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Single Year
Jiayu Guo(7)
Jian Pu(4)
Yuejie Zhang(2)
Chengjun Li(2)
Mingyue Feng(2)
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China(3)
School of Computer Science, Fudan University, Shanghai, China(3)
Mogo Auto Intelligence and Telematics Information Technology Company Ltd.(1)
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University(1)
Mogo Auto Intelligence and Telematics Information Technology Company Ltd, Beijing, China(1)
IEEE Transactions on Intelligent Vehicles(2)
2023 IEEE International Conference on Multimedia and Expo (ICME)(1)
2024 IEEE International Conference on Robotics and Automation (ICRA)(1)
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(1)
IEEE Open Journal of Intelligent Transportation Systems(1)
IEEE(7)
Brisbane, Australia(1)
Hyderabad, India(1)
Yokohama, Japan(1)
Trajectory Prediction(3)
Attention Mechanism(2)
Fusion Block(2)
Future Trajectories(2)
Local Fusion(2)

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Results

Uncertainty-Aware Dynamic Fusion for Multimodal Clinical Prediction Tasks

Jiayu Guo;Ying Cheng;Wen He;Yuejie Zhang;Rui Feng;Xiaobo Zhang

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

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Multimodal fusion offers significant potential for enhancing medical diagnosis, particularly in the Intensive Care Unit (ICU), where integrating diverse data sources is crucial. Traditional static fusion models often fail to account for sample-wise variations in modality importance, which can impact prediction accuracy. To address this issue, we propose a dynamic Uncertainty-Aware Weighting (UAW) ...Show More

Uncertainty-Aware Dynamic Fusion for Multimodal Clinical Prediction Tasks

Jiayu Guo;Ying Cheng;Wen He;Yuejie Zhang;Rui Feng;Xiaobo Zhang

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

Towards Fraud Detection Via Fine-Grained Classification of User Behavior

Xinzhi Wang;Hang Yu;Jiayu Guo;Pengbo Li;Xiangfeng Luo

IEEE Transactions on Big Data

Year: 2024 | Early Access Article |

The mass volume of data in the modern business world requires fraud detection to be automated. Hence, some researchers constructed the fraud scenario into graph data and proposed graph-based fraud detection methods. These methods treat the problem of fraud detection as a binary node classification task. However, the differences between the nodes of the same class are ignored. In this paper, we try...Show More

Towards Fraud Detection Via Fine-Grained Classification of User Behavior

Xinzhi Wang;Hang Yu;Jiayu Guo;Pengbo Li;Xiangfeng Luo

IEEE Transactions on Big Data

Year: 2024 | Early Access Article |

Trajectory Grid Diffusion for Multimodal Trajectory Prediction in Autonomous Vehicles

Jincheng Wang;Jiayu Guo;Mingyue Feng;Chengjun Li;Xiangyang Xue;Jian Pu

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Early Access Article |

Trajectory prediction is a critical topic for safe autonomous driving. Recently, diffusion models have gained increasing attention as a promising approach for learning multimodal trajectory distribution. However, existing diffusion models for trajectory prediction tasks are mainly based on the modality of trajectory points, which exhibit rapidly increasing uncertainty over the temporal dimension i...Show More

Trajectory Grid Diffusion for Multimodal Trajectory Prediction in Autonomous Vehicles

Jincheng Wang;Jiayu Guo;Mingyue Feng;Chengjun Li;Xiangyang Xue;Jian Pu

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Early Access Article |

Mitigating Causal Confusion in Vector-Based Behavior Cloning for Safer Autonomous Planning

Jiayu Guo;Mingyue Feng;Pengfei Zhu;Jinsheng Dou;Di Feng;Chengjun Li;Ru Wan;Jian Pu

2024 IEEE International Conference on Robotics and Automation (ICRA)

Year: 2024 | Conference Paper |

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The utilization of vector-based deep learning techniques has great prospects in the realm of autonomous driving, particularly in the domains of prediction and planning tasks. However, the application of vector-based backbones for prediction and planning tasks may lead to the occurrence of causal confusion. Previous studies have explored the phenomenon of causal confusion, with a specific emphasis ...Show More

Mitigating Causal Confusion in Vector-Based Behavior Cloning for Safer Autonomous Planning

Jiayu Guo;Mingyue Feng;Pengfei Zhu;Jinsheng Dou;Di Feng;Chengjun Li;Ru Wan;Jian Pu

2024 IEEE International Conference on Robotics and Automation (ICRA)

Year: 2024 | Conference Paper |

Bridging the Gap: Improving Domain Generalization in Trajectory Prediction

Zhibo Wang;Jiayu Guo;Haiqiang Zhang;Ru Wan;Junping Zhang;Jian Pu

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Volume: 9, Issue: 1 | Journal Article |

Cited by: Papers (7)

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In recent years, there has been a rapid rise in interest in trajectory prediction in the field of autonomous driving. However, the domain generalization of current works is often neglected, and their performance tends to degrade when transferred to a different dataset or scenario. In this article, we present a solution to this problem that accounts for the realistic conditions necessary for autono...Show More

Bridging the Gap: Improving Domain Generalization in Trajectory Prediction

Zhibo Wang;Jiayu Guo;Haiqiang Zhang;Ru Wan;Junping Zhang;Jian Pu

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Volume: 9, Issue: 1 | Journal Article |

Class-aware Variational Auto-encoder for Open Set Recognition

Ruofan Wang;Jiayu Guo;Rui-Wei Zhao;Ling Su;Yingzi Ye;Xiaobo Zhang;Yuejie Zhang;Rui Feng

2023 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2023 | Conference Paper |

Cited by: Papers (4)

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Compared with traditional classification models trained under the closed world assumption, Open Set Recognition (OSR) requires accurate classification for known classes as well as rejection for unknown ones. By modeling the distribution of each known class, Conditional Variational Auto-encoder (CVAE) has achieved great success in OSR, even though it was originally proposed for image generation. In...Show More

Class-aware Variational Auto-encoder for Open Set Recognition

Ruofan Wang;Jiayu Guo;Rui-Wei Zhao;Ling Su;Yingzi Ye;Xiaobo Zhang;Yuejie Zhang;Rui Feng

2023 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2023 | Conference Paper |

Lane Transformer: A High-Efficiency Trajectory Prediction Model

Zhibo Wang;Jiayu Guo;Zhengming Hu;Haiqiang Zhang;Junping Zhang;Jian Pu

IEEE Open Journal of Intelligent Transportation Systems

Year: 2023 | Volume: 4 | Journal Article |

Cited by: Papers (26)

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Trajectory prediction is a crucial step in the pipeline for autonomous driving because it not only improves the planning of future routes, but also ensures vehicle safety. On the basis of deep neural networks, numerous trajectory prediction models have been proposed and have already achieved high performance on public datasets due to the well-designed model structure and complex optimization proce...Show More

Lane Transformer: A High-Efficiency Trajectory Prediction Model

Zhibo Wang;Jiayu Guo;Zhengming Hu;Haiqiang Zhang;Junping Zhang;Jian Pu

IEEE Open Journal of Intelligent Transportation Systems

Year: 2023 | Volume: 4 | Journal Article |

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