正解データなしで軌道予測器を訓練する手法
Training Trajectory Predictors Without Ground-Truth Data
正解ラベルなしでも高精度な位置・方位・速度推定を行い、その推定値を用いてTrajectron++ベースの軌道予測モデルを訓練する枠組みを提案した。
著者: Mikolaj Kliniewski, Jesse Morris, Ian R. Manchester, Viorela Ila
分類: cs.RO
原文アブストラクト
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.
