提出对比学习模块ECAM，让轨迹预测模型更少撞上障碍物。
ECAM: A Contrastive Learning Approach to Avoid Environmental Collision in Trajectory Forecasting
- 用对比学习设计环境避障模块，提升模型对周围障碍的感知能力。
- 在ETH/UCY数据集上使主流模型碰撞率降低40%~50%。
- 可无缝接入现有模型，适合自动驾驶与机器人路径规划场景。
人类轨迹预测在自动驾驶、机器人和监控等应用中至关重要。准确预测需考虑社交互动、多模态输出、行人意图及环境上下文等因素。现有方法虽涵盖这些因素，却常忽略环境影响，导致预测轨迹与障碍物相撞。本文提出ECAM（环境避障模块），一种基于对比学习的模块，用于增强模型对环境的避障能力。该模块可集成至现有轨迹预测模型中，提升其生成无碰撞轨迹的能力。我们在ETH/UCY数据集上评估了该方法，定量与定性地证明其避障性能。实验表明，将该模块融入当前最优方法后，碰撞率显著降低40%至50%。代码已公开于https://github.com/CVML-CFU/ECAM。
原文摘要 · Abstract (English)
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 https://github.com/CVML-CFU/ECAM.
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