Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network

2021-07-02 · Maosen Li, Siheng Chen, Yanning Shen, Genjia Liu, Ivor W. Tsang, Ya zhang

This paper considers predicting future statuses of multiple agents in an online fashion by exploiting dynamic interactions in the system. We propose a novel collaborative prediction unit (CoPU), which aggregates the predictions from multiple collaborative predictors according to a collaborative graph. Each collaborative predictor is trained to predict the status of an agent by considering the impact of another agent. The edge weights of the collaborative graph reflect the importance of each predictor. The collaborative graph is adjusted online by multiplicative update, which can be motivated by minimizing an explicit objective. With this objective, we also conduct regret analysis to indicate that, along with training, our CoPU achieves similar performance with the best individual collaborative predictor in hindsight. This theoretical interpretability distinguishes our method from many other graph networks. To progressively refine predictions, multiple CoPUs are stacked to form a collaborative graph neural network. Extensive experiments are conducted on three tasks: online simulated trajectory prediction, online human motion prediction and online traffic speed prediction, and our methods outperform state-of-the-art works on the three tasks by 28.6%, 17.4% and 21.0% on average, respectively.

📄 PDF Abstract BibTeX arXiv:2107.00894

Code (0)

등록된 구현이 없습니다.

Tasks

Graph Neural NetworkHuman motion predictionmotion predictionPrediction Trajectory Prediction

DECAMP: Towards Scene-Consistent Multi-Agent Motion Prediction with Disentangled Context-Aware Pre-Training

2025-09-12 · Jianxin Shi, Zengqi Peng, Xiaolong Chen, Tianyu Wo 외 arxiv

Trajectory prediction is a critical component of autonomous driving, essential for ensuring both safety and efficiency on the road. However, traditional approaches often struggle with the scarcity of labeled data and exh…

Representation Learning Trajectory Prediction Autonomous Driving Motion Forecasting

Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting

2022-07-11 · Bohan Tang, Yiqi Zhong, Chenxin Xu, Wei-Tao Wu 외

In multi-modal multi-agent trajectory forecasting, two major challenges have not been fully tackled: 1) how to measure the uncertainty brought by the interaction module that causes correlations among the predicted trajec…

regression Task 2 Trajectory Forecasting

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework

2025-07-30 · Peng-Yi Wu, Pei-Cing Huang, Ting-Yu Chen, Chantung Ku 외 arxiv

Accurate and interpretable prediction of estimated glomerular filtration rate (eGFR) is essential for managing chronic kidney disease (CKD) and supporting clinical decisions. Recent advances in Large Multimodal Models (L…

Collaborative Uncertainty in Multi-Agent Trajectory Forecasting

2021-10-26 · NeurIPS 2021 12 · Bohan Tang, Yiqi Zhong, Ulrich Neumann, Gang Wang 외

Uncertainty modeling is critical in trajectory forecasting systems for both interpretation and safety reasons. To better predict the future trajectories of multiple agents, recent works have introduced interaction module…

Trajectory Forecasting

Collaborative Trajectory Prediction via Late Fusion

2026-04-24 · Nadya Abdel Madjid, Murad Mebrahtu, Zakhar Yagudin, Bilal Hassan 외 arxiv

Predicting future trajectories of surrounding traffic agents is critical for safe autonomous navigation and collision avoidance. Despite all advances in the trajectory forecasting realm, the prediction models remains vul…

Trajectory Forecasting Trajectory Prediction Collision Avoidance