GASP: 自動運転のための幾何・意味論的自己教師あり事前学習の統合
GASP: Unifying Geometric and Semantic Self-Supervised Pre-training for Autonomous Driving
時空間の任意の未来点で3D占有・自車占有・視覚基盤モデルの特徴を予測し、自動運転向けの統合表現を自己教師あり学習する手法を提案。
著者: William Ljungbergh, Adam Lilja, Adam Tonderski. Arvid Laveno Ling, Carl Lindström, Willem Verbeke, Junsheng Fu, Christoffer Petersson, Lars Hammarstrand, Michael Felsberg
分類: cs.CV, cs.RO
原文アブストラクト
Self-supervised pre-training based on next-token prediction has enabled large language models to capture the underlying structure of text, and has led to unprecedented performance on a large array of tasks when applied at scale. Similarly, autonomous driving generates vast amounts of spatiotemporal data, alluding to the possibility of harnessing scale to learn the underlying geometric and semantic structure of the environment and its evolution over time. In this direction, we propose a geometric and semantic self-supervised pre-training method, GASP, that learns a unified representation by predicting, at any queried future point in spacetime, (1) general occupancy, capturing the evolving structure of the 3D scene; (2) ego occupancy, modeling the ego vehicle path through the environment; and (3) distilled high-level features from a vision foundation model. By modeling geometric and semantic 4D occupancy fields instead of raw sensor measurements, the model learns a structured, generalizable representation of the environment and its evolution through time. We validate GASP on multiple autonomous driving benchmarks, demonstrating significant improvements in semantic occupancy forecasting, online mapping, and ego trajectory prediction. Our results demonstrate that continuous 4D geometric and semantic occupancy prediction provides a scalable and effective pre-training paradigm for autonomous driving. For code and additional visualizations, see \href{https://research.zenseact.com/publications/gasp/.
