论文标题
基于图的空间变压器具有内存重放，用于多未来的行人轨迹预测
Graph-based Spatial Transformer with Memory Replay for Multi-future Pedestrian Trajectory Prediction
论文作者
论文摘要
对于各种现实生活中的应用，例如自动驾驶和机器人运动计划，行人轨迹预测是必不可少且具有挑战性的任务。除了生成一条未来的路径外，预测多个合理的未来路径在最近的一些轨迹预测方面变得流行。但是，现有方法通常强调行人与周边地区之间的空间相互作用，但忽略了预测的平稳性和时间一致性。我们的模型旨在通过建模基于历史轨迹的多路径，通过对多尺度的基于图的空间变压器进行建模，并结合了使用存储器图的轨迹平滑算法``记忆重播''的轨迹平滑算法。我们的方法可以全面利用空间信息，并纠正时间上不一致的轨迹（例如，尖锐的转弯）。我们还提出了一个名为“轨迹使用百分比”的新评估公制，以评估多种多进取预测的全面性。我们的广泛实验表明，所提出的模型在多未来的预测和单人预测的竞争结果上实现了最先进的表现。在https://github.com/jacobieee/st-mr上发布的代码。
Pedestrian trajectory prediction is an essential and challenging task for a variety of real-life applications such as autonomous driving and robotic motion planning. Besides generating a single future path, predicting multiple plausible future paths is becoming popular in some recent work on trajectory prediction. However, existing methods typically emphasize spatial interactions between pedestrians and surrounding areas but ignore the smoothness and temporal consistency of predictions. Our model aims to forecast multiple paths based on a historical trajectory by modeling multi-scale graph-based spatial transformers combined with a trajectory smoothing algorithm named ``Memory Replay'' utilizing a memory graph. Our method can comprehensively exploit the spatial information as well as correct the temporally inconsistent trajectories (e.g., sharp turns). We also propose a new evaluation metric named ``Percentage of Trajectory Usage'' to evaluate the comprehensiveness of diverse multi-future predictions. Our extensive experiments show that the proposed model achieves state-of-the-art performance on multi-future prediction and competitive results for single-future prediction. Code released at https://github.com/Jacobieee/ST-MR.
