为车辆条件化姿态预测建模的行人-车辆交互
Modeling 3D Pedestrian-Vehicle Interactions for Vehicle-Conditioned Pose Forecasting
AI总结：
本文提出了一种基于3D车辆条件化的行人姿态预测方法，通过融合车辆信息提升自动驾驶中行人-车辆交互的预测精度。
摘要：
准确预测行人运动对于在复杂城市环境中实现安全可靠的自动驾驶至关重要。在本工作中，我们提出了一种3D车辆条件化行人姿态预测框架，该框架明确纳入了周围车辆信息。为了支持这一目标，我们增强了Waymo-3DSkelMo数据集，添加了对齐的3D车辆边界框，从而能够真实建模多智能体行人-车辆交互。我们引入了一种采样方案，按行人和车辆数量对场景进行分类，从而在不同交互复杂性下进行训练。我们提出的网络采用TBIFormer架构，结合专用车辆编码器和行人-车辆交互交叉注意力模块，以融合行人和车辆特征，使预测能够基于历史行人运动和周围车辆进行条件化。大量实验表明，预测精度有了显著提高，并验证了不同建模行人-车辆交互的方法，突显了车辆感知的3D姿态预测对自动驾驶的重要性。代码可在：https://github.com/GuangxunZhu/VehCondPose3D 上获得。
英文摘要：
Accurately predicting pedestrian motion is crucial for safe and reliable autonomous driving in complex urban environments. In this work, we present a 3D vehicle-conditioned pedestrian pose forecasting framework that explicitly incorporates surrounding vehicle information. To support this, we enhance the Waymo-3DSkelMo dataset with aligned 3D vehicle bounding boxes, enabling realistic modeling of multi-agent pedestrian-vehicle interactions. We introduce a sampling scheme to categorize scenes by pedestrian and vehicle count, facilitating training across varying interaction complexities. Our proposed network adapts the TBIFormer architecture with a dedicated vehicle encoder and pedestrian-vehicle interaction cross-attention module to fuse pedestrian and vehicle features, allowing predictions to be conditioned on both historical pedestrian motion and surrounding vehicles. Extensive experiments demonstrate substantial improvements in forecasting accuracy and validate different approaches for modeling pedestrian-vehicle interactions, highlighting the importance of vehicle-aware 3D pose prediction for autonomous driving. Code is available at: https://github.com/GuangxunZhu/VehCondPose3D
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
