PPT: 擬似ラベル軌跡による事前学習で動き予測を強化
PPT: Pretraining with Pseudo-Labeled Trajectories for Motion Forecasting
自動生成された多様な擬似ラベル軌跡を事前学習に活用し、少量のラベルデータでの微調整で動き予測性能を向上させるフレームワークPPTを提案。
著者: Yihong Xu, Yuan Yin, Éloi Zablocki, Tuan-Hung Vu, Alexandre Boulch, Matthieu Cord
分類: cs.CV, cs.RO
原文アブストラクト
Accurately predicting how agents move in dynamic scenes is essential for safe autonomous driving. State-of-the-art motion forecasting models rely on datasets with manually annotated or post-processed trajectories. However, building these datasets is costly, generally manual, hard to scale, and lacks reproducibility. They also introduce domain gaps that limit generalization across environments. We introduce PPT (Pretraining with Pseudo-labeled Trajectories), a simple and scalable pretraining framework that uses unprocessed and diverse trajectories automatically generated from off-the-shelf 3D detectors and tracking. Unlike data annotation pipelines aiming for clean, single-label annotations, PPT is a pretraining framework embracing off-the-shelf trajectories as useful signals for learning robust representations. With optional finetuning on a small amount of labeled data, models pretrained with PPT achieve strong performance across standard benchmarks, particularly in low-data regimes, and in cross-domain, end-to-end, and multi-class settings. PPT is easy to implement and improves generalization in motion forecasting.
