融合人体姿态与边界框，提升自动驾驶中行人轨迹预测精度。
SGNetPose+: Stepwise Goal-Driven Networks with Pose Information for Trajectory Prediction in Autonomous Driving
- 分步目标驱动网络结合骨骼关键点与人体角度信息
- 在JAAD和PIE数据集上超越SGNet模型，达到最新水平
- 适合关注行人预测与自动驾驶安全的研究者
行人轨迹预测对自动驾驶系统至关重要，能显著提升安全性并支持决策。本文提出SGNetPose+，在SGNet基础上融合姿态估计提取的骨骼信息与体段角度，结合边界框从视频中预测行人轨迹，以避免碰撞。通过姿态模型提取关节位置，并计算关节角度；采用水平翻转视频帧进行时序数据增强，扩大数据集规模并提升性能。该方法在JAAD与PIE数据集上使用姿态与边界框数据，取得了当前最优结果，优于原始SGNet模型。代码已开源：SGNetPose+。
原文摘要 · Abstract (English)
Predicting pedestrian trajectories is essential for autonomous driving systems, as it significantly enhances safety and supports informed decision-making. Accurate predictions enable the prevention of collisions, anticipation of crossing intent, and improved overall system efficiency. In this study, we present SGNetPose+, an enhancement of the SGNet architecture designed to integrate skeleton information or body segment angles with bounding boxes to predict pedestrian trajectories from video data to avoid hazards in autonomous driving. Skeleton information was extracted using a pose estimation model, and joint angles were computed based on the extracted joint data. We also apply temporal data augmentation by horizontally flipping video frames to increase the dataset size and improve performance. Our approach achieves state-of-the-art results on the JAAD and PIE datasets using pose data with the bounding boxes, outperforming the SGNet model. Code is available on Github: SGNetPose+.
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