CausalOrca: An ORCA-based Diagnostic Dataset for Causally-aware Multi-agent Trajectory Prediction

CausalOrca is a synthetic diagnostic dataset created through controlled simulations. It is designed to provide annotations of ground-truth causal effects and fine-grained agent categories for social interactions in multi-agent scenarios. The dataset is constructed using a modified RVO2 simulator and incorporates the ORCA optimization-based collision avoidance algorithm known for crowd simulation. With full control over scene configurations, the dataset enables the collection of motion behaviors in paired scenes before and after agent removal, generating a large set of counterfactual pairs with annotations of ground-truth causal effects. CausalOrca can serve as a valuable resource for studying and developing causally-aware neural representations of social interactions and trajectory prediction models. Please see the GitHub repository for a more detailed description of the dataset, including dataset statistics and documentation on how to use, visualize, and generate the data.CausalOrca是一款通过可控仿真构建的合成诊断数据集。该数据集旨在为多智能体场景（multi-agent scenarios）下的社交交互，提供真实因果效应（ground-truth causal effects）与细粒度智能体类别（fine-grained agent categories）的标注信息。本数据集基于改良版RVO2模拟器（RVO2 simulator）构建，集成了面向人群仿真的ORCA优化避障算法。通过对场景配置的完全可控，该数据集可采集智能体移除前后配对场景中的运动行为数据，进而生成大量带有真实因果效应标注的反事实样本对（counterfactual pairs）。CausalOrca可作为宝贵的研究资源，用于探索与开发面向社交交互的感知因果神经表征（causally-aware neural representations）以及轨迹预测模型（trajectory prediction models）。如需了解该数据集的详细信息，包括数据集统计数据、使用文档、可视化方案与数据生成方法说明，请参阅其GitHub仓库。