Pedestrian trajectory prediction in dynamic and strongly interactive scenes has become one of the most challenging problems in fields such as automated driving. In this paper, we propose Goal-CurveNet, a multimodal trajectory prediction network combining heterogeneous graph attention goal prediction and curve fitting. The model addresses the problems of pedestrian interaction modeling, multimodal trajectory prediction, and performance of predicted trajectories in pedestrian trajectory prediction. Goal-CurveNet can better model the historical trajectories and interaction behaviors in the scene systematically based on heterogeneous graph attention. It predicts the complete trajectories by curve fitting, which effectively improves the quality of predicted trajectories. The model architecture of “goal first and then trajectory” and targeted training paradigm also enhance the final performance. Through detailed training and testing on ETH & UCY datasets, we validate the effectiveness of each contribution of Goal-CurveNet. Compared to many state-of-the-art models, Goal-CurveNet achieves performance improvements in key metrics and effective prediction of pedestrian trajectories.

中文翻译：

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Goal-CurveNet：使用异构图注意力目标预测和曲线拟合的行人轨迹预测网络

动态、强交互场景下的行人轨迹预测已成为自动驾驶等领域最具挑战性的问题之一。在本文中，我们提出了Goal-CurveNet，一种结合异构图注意力目标预测和曲线拟合的多模态轨迹预测网络。该模型解决了行人交互建模、多模态轨迹预测以及行人轨迹预测中预测轨迹性能的问题。 Goal-CurveNet基于异构图注意力可以更好地系统地建模场景中的历史轨迹和交互行为。通过曲线拟合来预测完整的轨迹，有效提高了预测轨迹的质量。 “先目标后轨迹”的模型架构和针对性的训练范式也提升了最终的表现。通过对 ETH 和 UCY 数据集的详细训练和测试，我们验证了 Goal-CurveNet 的每个贡献的有效性。与许多最先进的模型相比，Goal-CurveNet 在关键指标和行人轨迹的有效预测方面实现了性能改进。