MTP-STG:空间时间图形转换器网络,用于在人群中预测多个未来轨迹
Zichen Zhang1, Xingwen Cao1, Yi Song2
1School of Urban Planning and Design, Peking University, Shenzhen 518055, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
预测行人轨迹对于自动驾驶系统至关重要. 本研究引入了一种使用时空图形注意网络的新型模型,以提高拥挤环境中的多轨迹预测准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 预测未来的多个行人轨迹对于自动驾驶和机器人运动规划至关重要.
- 当前的方法往往忽视了遥远的环境影响,并与轨迹的平滑性和时间一致性作斗争.
- 了解复杂的人群动态需要先进的建模技术.
研究的目的:
- 为人群场景提出一个多式联运轨迹预测模型.
- 通过结合遥远的空间信息和确保时间一致性来增强轨迹预测.
- 在预测多个未来的行人路径方面实现最先进的性能.
主要方法:
- 利用了时空图形注意力网络和多式联络数据增强.
- 使用CARLA进行多视图分析生成模拟行人轨迹数据.
- 采用多目标检测和跟踪算法以及用于轨迹平滑的内存存储模块.
主要成果:
- 拟议的MTP-STG模型在预测多个未来轨迹方面表现出卓越的表现.
- 在拥挤的环境中有效模拟行人之间的缩放空间相互作用.
- 与现有的轨迹预测方法相比,取得了最先进的结果.
结论:
- 开发的多式联运轨迹预测模型显著推进了该领域的发展.
- 整合时空图的注意力网络可以提高预测的准确性和一致性.
- 该方法为复杂的人群导航场景提供了强大的解决方案.
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