Abstract
Human trajectory forecasting is crucial in applications such as autonomous driving, robotics and surveillance. Accurate forecasting requires models to consider various factors, including social interactions, multi-modal predictions, pedestrian intention and environmental context. While existing methods account for these factors, they often overlook the impact of the environment, which leads to collisions with obstacles. This paper introduces ECAM (Environmental Collision Avoidance Module), a contrastive learning-based module to enhance collision avoidance ability with the environment. The proposed module can be integrated into existing trajectory forecasting models, improving their ability to generate collision-free predictions. We evaluate our method on the ETH/UCY dataset and quantitatively and qualitatively demonstrate its collision avoidance capabilities. Our experiments show that state-of-the-art methods significantly reduce (-40/50%) the collision rate when integrated with the proposed module. The code is available at this https URL.
Abstract (translated)
人类轨迹预测在自动驾驶、机器人技术和监控等领域中至关重要。准确的预测需要模型考虑各种因素，包括社会互动、多模态预测、行人的意图以及环境背景。尽管现有的方法已经考虑到这些因素，但它们常常忽视了环境的影响，从而导致与障碍物相撞的问题。本文介绍了一种名为ECAM（环境碰撞避免模块）的新模块，这是一种基于对比学习的模块，旨在增强模型在复杂环境中避开碰撞的能力。 提出的ECAM模块可以集成到现有轨迹预测模型中，并提高这些模型生成无碰撞预测的能力。我们在ETH/UCY数据集上评估了我们的方法，并通过定量和定性分析展示了其避免碰撞的能力。实验结果表明，当将我们提出的方法与现有的最先进的方法结合使用时，能够显著减少（-40%/50%）碰撞率。 该研究的代码可在提供的网址上获取。
URL
https://arxiv.org/abs/2506.09626
