论文标题
这不是旅程，而是目的地：端点条件轨迹预测
It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
论文作者
论文摘要
与多种社会相互作用的代理人进行的人类轨迹预测对于人类环境中的自主航行至关重要，例如自动驾驶汽车和社会机器人。在这项工作中，我们为灵活的人类轨迹预测提供了预测的端点条件网络（PECNET）。 PECNET侵入遥远的轨迹端点，以协助远程多模式轨迹预测。一种新型的非本地社会合并层使PECNET能够推断出多样化但具有社会化的轨迹。此外，我们提出了一个简单的“截断”，用于改善几射击多模式轨迹预测性能。我们表明，PECNET将斯坦福无人机轨迹预测基准的最新性能提高了约20.9％，而在ETH/UCY基准上提高了〜40.8％。项目主页：https：//karttikeya.github.io/pablication/htf/
Human trajectory forecasting with multiple socially interacting agents is of critical importance for autonomous navigation in human environments, e.g., for self-driving cars and social robots. In this work, we present Predicted Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction. PECNet infers distant trajectory endpoints to assist in long-range multi-modal trajectory prediction. A novel non-local social pooling layer enables PECNet to infer diverse yet socially compliant trajectories. Additionally, we present a simple "truncation-trick" for improving few-shot multi-modal trajectory prediction performance. We show that PECNet improves state-of-the-art performance on the Stanford Drone trajectory prediction benchmark by ~20.9% and on the ETH/UCY benchmark by ~40.8%. Project homepage: https://karttikeya.github.io/publication/htf/
