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

翻译：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。本研究提出了一种三维车辆条件行人姿态预测框架，该框架显式地整合了周围车辆信息。为此，我们通过添加对齐的三维车辆边界框对Waymo-3DSkelMo数据集进行了增强，从而能够对多智能体行人-车辆交互进行真实建模。我们引入了一种采样方案，根据行人和车辆数量对场景进行分类，以促进在不同交互复杂性下的训练。我们提出的网络采用TBIFormer架构，并配备了专用的车辆编码器以及行人-车辆交互交叉注意力模块，以融合行人和车辆特征，使得预测能够同时基于历史行人运动和周围车辆信息进行条件化。大量实验表明，该框架在预测准确性方面取得了显著提升，并验证了建模行人-车辆交互的不同方法，突显了车辆感知的三维姿态预测对于自动驾驶的重要性。代码发布于：https://github.com/GuangxunZhu/VehCondPose3D