Multi-agent interacting systems are prevalent in the world, from pure physical systems to complicated social dynamic systems. In many applications, effective understanding of the situation and accurate trajectory prediction of interactive agents play a significant role in downstream tasks, such as decision making and planning. In this paper, we propose a generic trajectory forecasting framework (named EvolveGraph) with explicit relational structure recognition and prediction via latent interaction graphs among multiple heterogeneous, interactive agents. Considering the uncertainty of future behaviors, the model is designed to provide multi-modal prediction hypotheses. Since the underlying interactions may evolve even with abrupt changes, and different modalities of evolution may lead to different outcomes, we address the necessity of dynamic relational reasoning and adaptively evolving the interaction graphs. We also introduce a double-stage training pipeline which not only improves training efficiency and accelerates convergence, but also enhances model performance. The proposed framework is evaluated on both synthetic physics simulations and multiple real-world benchmark datasets in various areas. The experimental results illustrate that our approach achieves state-of-the-art performance in terms of prediction accuracy.
Multi-Relational Knowledge Graph Enhanced Embedding for Trajectory-User Linking
the College of Information, Mechanical and Electrical Engineering, Shanghai Normal University（上海师范大学信息与机电工程学院）；Shanghai Urban-Rural Construction & Transportation Development Institute（上海城乡建设与交通发展研究院）；Zhongke Zidong Information Technology（中科紫东信息技术（北京）有限公司）
Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation
University of California, Riverside（加州大学河滨分校）；University of California, Berkeley（加州大学伯克利分校）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；Stanford University（斯坦福大学）
ART: Adaptive Relational Transformer for Pedestrian Trajectory Prediction with Temporal-Aware Relations
Department of Computer Science, Durham University（杜伦大学计算机科学系）；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）
Self-Growing Spatial Graph Network for Context-Aware Pedestrian Trajectory Prediction
Nanyang Technological University（南洋理工大学）
行人轨迹预测是一个活跃的研究领域，最近的工作是将行人社会互动及其上下文顺应性的准确模型嵌入到动态空间图中。然而，现有的作品依赖于空间的场景和动态的假设，这需要一个重大的挑战，以适应在未知的环境中的在线系统的图形结构。此外，还缺乏评估方法的关系建模预测性能的影响。为了填补这一空白，我们提出了社会轨迹推荐门控图递归邻域网络（STR-GGRNN），它使用基于上下文场景特征和行人视觉线索的数据驱动的自适应在线邻域推荐。通过在线非负矩阵分解（NMF）来构造用于预测行人轨迹的图邻接矩阵，从而实现邻域推荐。在ETH-UCY数据集上的实验结果表明，该方法的性能优于现有方法，最佳模型在ETH-UCY数据集上达到了12 cm ADE和$\sim$ 15 cm FDE。当每帧采样总共20 K个未来轨迹时，所提出的方法仅需要0.49秒。
TrajFusionNet+: Transformer-Based Prediction of Pedestrian Crossing Intention via Fusion of Trajectory Representations and Scene Graphs
Perception, Robotics and Intelligent Machines Research Group (PRIME)（感知、机器人与智能机器研究组（PRIME））；Department of Computer Science, Université de Moncton（蒙克顿大学计算机科学系）
GraphWorld: Long-Horizon Planning with World Models for End-to-End Autonomous Driving
Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院，交通数据挖掘与具身智能北京市重点实验室）；School of Artificial Intelligence (School of Software), Yanshan University（燕山大学人工智能学院（软件学院））；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）；University of Macau（澳门大学）；The University of Queensland（昆士兰大学）
