无需真实轨迹数据，也能精准预测移动目标的运动轨迹。
Training Trajectory Predictors Without Ground-Truth Data
- 用高精度估计替代真实数据训练轨迹预测模型
- 在数据稀缺时仍能生成鲁棒预测，误差显著降低
- 适合自动驾驶、机器人等实际部署场景
本文提出一种无需真实轨迹数据即可准确、平滑估计位置、航向和速度的框架。基于此高质量输入，我们构建了基于Trajectron++的系统，可稳定生成精确的轨迹预测。与传统依赖真实标注数据的模型不同，本方法消除了对标注数据的依赖。分析表明，低质量输入会导致预测噪声大且不可靠，影响导航模块性能。我们通过评估输入数据质量和模型输出，验证了输入噪声的影响。此外，我们的估计系统可在数据有限情况下有效训练轨迹预测模型，使其在多种环境中均表现稳健。准确估计对真实场景中部署轨迹预测模型至关重要，本系统确保了各类应用下的可靠结果。
原文摘要 · Abstract (English)
This paper presents a framework capable of accurately and smoothly estimating position, heading, and velocity. Using this high-quality input, we propose a system based on Trajectron++, able to consistently generate precise trajectory predictions. Unlike conventional models that require ground-truth data for training, our approach eliminates this dependency. Our analysis demonstrates that poor quality input leads to noisy and unreliable predictions, which can be detrimental to navigation modules. We evaluate both input data quality and model output to illustrate the impact of input noise. Furthermore, we show that our estimation system enables effective training of trajectory prediction models even with limited data, producing robust predictions across different environments. Accurate estimations are crucial for deploying trajectory prediction models in real-world scenarios, and our system ensures meaningful and reliable results across various application contexts.
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