论文标题
行人区域轨迹预测的端到端学习方法
An End-to-End Learning Approach for Trajectory Prediction in Pedestrian Zones
论文作者
论文摘要
本文旨在探讨在异质行人区域中的轨迹预测问题，在该区域中，社会动态表示是一个巨大的挑战。提出的是基于注意力因素输入学习社交互动的注意机制的端到端学习框架，以提高预测准确性。
This paper aims to explore the problem of trajectory prediction in heterogeneous pedestrian zones, where social dynamics representation is a big challenge. Proposed is an end-to-end learning framework for prediction accuracy improvement based on an attention mechanism to learn social interaction from multi-factor inputs.
