一个负反转变压器,由历史和未来的相互作用为轨迹预测提供能量
IEEE transactions on cybernetics
|November 26, 2025
概括
Miformer通过动态建模历史与未来的相互作用来增强自动驾驶轨迹预测. 这种方法通过考虑未来的代理行为和道路几何学来提高预测准确性,以实现更安全的导航.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 自动驾驶轨迹预测对于安全导航至关重要.
- 现有的方法往往忽略了未来的相互作用,导致近视预测.
- 由于静态查询设计,当前以查询为中心的模型在适应跨时间上下文融合方面存在局限性.
研究的目的:
- 提出一个新的轨迹预测模型Miformer.
- 增强历史-未来的时空背景交互,以提高预测准确度.
- 解决现有方法关于未来相互作用和时间上下文的局限性.
主要方法:
- 引入了一个用于动态时间建模和跨时间阶段信息交换的时间查询机制.
- 设计了一个历史-未来空间-时间交互模块 (HF-STIM),使用 iTransformer 来实现双向上下文依赖.
- 开发了一个负反转变压器 (Mi-Transformer),具有动态残留学习,以改进信息流.
- 采用类似DETR的解码器来生成各种多式联运轨迹预测.
主要成果:
- 在INTERACTION数据集上,Miformer实现了最先进的性能.
- 在Argoverse数据集上展示了竞争性表现.
- 在现实世界的运动预测场景中验证的有效性.
结论:
- Miformer有效地增强了历史-未来的空间-时间背景交互.
- 拟议的模型克服了现有的轨迹预测方法的局限性.
- 结果表明Miformer在推进自动驾驶安全性和效率方面的潜力.
相关概念视频
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