Self-Supervised JEPA-based World Models for LiDAR Occupancy Completion and Forecasting

Autonomous driving, as an agent operating in the physical world, requires the fundamental capability to build textitworld models that capture how the environment evolves spatiotemporally in order to support long-term planning. At the same time, scalability demands learning such models in a self-supervised manner; textitjoint-embedding predictive architecture (JEPA) enables learning world models via leveraging large volumes of unlabeled data without relying on expensive human annotations. In this paper, we propose AD-LiST-JEPA, a self-supervised world model for autonomous driving that predicts future spatiotemporal evolution from LiDAR data using a JEPA framework. We evaluate the quality of the learned representations through a downstream LiDAR-based occupancy completion and forecasting (OCF) task, which jointly assesses perception and prediction. Proof of concept experiments show better OCF performance with pretrained encoder after JEPA-based world model learning.