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MSTT:用于行人轨迹预测的多空间时间图注意力模型
Qingrui Zhang1, Xuxiu Zhang1, Zilang Ye2
1School of Automation & Electrical Engineering, Dalian Jiaotong University, Dalian 116028, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
预测行人运动是自动驾驶的关键. 一个新的相对时空编码 (RSTE) 和多时空图 (MSTG) 模型通过分析相互作用来提高准确性,优于旧方法.
科学领域:
- 计算机科学 计算机科学
- 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:
- 准确的行人运动预测对于自动驾驶和智能系统至关重要.
- 现有的模型往往无法捕捉个人之间的复杂的时空相互作用,因为它们依赖于绝对定位.
研究的目的:
- 通过有效地建模时空相互作用,开发一种用于预测行人运动的新方法.
- 引入相对时空编码 (RSTE) 策略和多时空图 (MSTG) 建模技术.
主要方法:
- 实施了相对时空编码 (RSTE) 策略,以捕捉行人之间的关系.
- 设计了一个多空间-时间图 (MSTG),以模拟多个个体在时间和空间上的相互作用.
- 开发了一个基于注意力的MSTT模型,用于MSTG结构的端到端学习.
主要成果:
- 这项研究发现,一个人以前的发展轨迹显著影响了其他人未来的发展轨迹.
- 与传统的基于轨迹的方法相比,拟议的MSTT模型显示出优异的预测性能.
- 对两个具有挑战性的数据集的评估证实了MSTT模型的有效性.
结论:
- RSTE策略和MSTT模型在准确预测行人行为方面取得了重大进展.
- 建模相对的时空相互作用对于可靠的行人运动预测至关重要.
- 这项研究有助于更安全,更有效的自主系统和人机交互.
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