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Updated: Jul 13, 2025

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条件目标导向的轨迹预测交互的车辆
IEEE transactions on neural networks and learning systems
|October 17, 2023
概括
预测交互式交通代理的轨迹是很困难的. 新的条件目标导向轨迹预测 (CGTP) 框架模拟了沿代理和时间轴的相互作用,改善了对自动驾驶的预测.
科学领域:
- 自动驾驶自动驾驶的自动驾驶
- 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
背景情况:
- 在交互场景 (例如,切入,合并) 中预测双向交通信号轨迹是自动驾驶系统面临的重大挑战.
- 现有的方法往往无法充分建模未来的相互作用,要么通过边际处理预测,要么使用单轴因子分解.
研究的目的:
- 提出一种新的双轴因子化联合预测管道,即条件目标导向轨迹预测 (CGTP) 框架.
- 为了有效地模拟在代理和时间轴上的未来相互作用,以改进目标和轨迹预测.
主要方法:
- 设计了一个目标利益网络 (GoINet),用于对目标候选人的层次向量化特征提取.
- 开发了一种条件目标预测网络 (CGPNet) 用于多式联网目标对预测,并采用了新的目标交互式损失.
- 提出了一个以目标为导向的轨迹推广网络 (GTRNet),用于通过按时的交互式推广,根据预测的目标对指导场景合规轨迹对预测.
主要成果:
- 拟议的CGTP框架在与最先进的 (SOTA) 预测模型相比显示出更高的性能.
- 对各种数据集进行了验证,包括Waymo开放运动数据集 (WOMD),Argoverse运动预测数据集和内部切割数据集.
结论:
- CGTP框架有效地解决了在自动驾驶中交互式轨迹预测的挑战.
- 考虑代理和时间相互作用的双轴因子化方法,导致预测准确性和场景合规性显著改善.
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