GASP: Unifying Geometric and Semantic Self-Supervised Pre-training for
  Autonomous Driving
Authors: name: William Ljungbergh, name: Adam Lilja, name: Adam Tonderski. Arvid Laveno Ling, name: Carl Lindström, name: Willem Verbeke, name: Junsheng Fu, name: Christoffer Petersson, name: Lars Hammarstrand, name: Michael Felsberg

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/.