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

arXiv: https://arxiv.org/abs/2602.12540 · HTML: https://mlsanity.com/paper/2602.12540/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in World models for autonomous driving, one of 11 directions in World models.
- Builds on 19 of its 21 references that are in the map.
- First posted on arXiv 13 Feb 2026.

## Placed in

- World models / [World models for autonomous driving](https://mlsanity.com/wm/world-models-for-autonomous-driving/)

## Builds on (most cited first, 19 of 19)

- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning](https://mlsanity.com/paper/2506.09985/) (2025)
- [Genie: Generative Interactive Environments](https://mlsanity.com/paper/2402.15391/) (2024)
- [GAIA-1: A Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2309.17080/) (2023)
- [Revisiting Feature Prediction for Learning Visual Representations from Video](https://mlsanity.com/paper/2404.08471/) (2024)
- [DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning](https://mlsanity.com/paper/2411.04983/) (2024)
- [LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics](https://mlsanity.com/paper/2511.08544/) (2025)
- [Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation](https://mlsanity.com/paper/2406.06978/) (2024)
- [GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2503.20523/) (2025)
- [Enhancing End-to-End Autonomous Driving with Latent World Model](https://mlsanity.com/paper/2406.08481/) (2024)
- [World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model](https://mlsanity.com/paper/2507.00603/) (2025)
- [Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models](https://mlsanity.com/paper/2502.14819/) (2025)
- [Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting](https://mlsanity.com/paper/2302.13130/) (2023)
- [UnO: Unsupervised Occupancy Fields for Perception and Forecasting](https://mlsanity.com/paper/2406.08691/) (2024)
- [LiDAR-based 4D Occupancy Completion and Forecasting](https://mlsanity.com/paper/2310.11239/) (2023)
- [BEV-MAE: Bird's Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving Scenarios](https://mlsanity.com/paper/2212.05758/) (2022)
- [Separating the World and Ego Models for Self-Driving](https://mlsanity.com/paper/2204.07184/) (2022)

## Built on by (most cited first, 5 of 5)

- [Cross4D-JEPA: Dense Cross-modal Correspondence Distillation for 4D Point Cloud Representation Learning](https://mlsanity.com/paper/2607.00514/) (2026)
- [TERRA: Task-Embedded Reasoning and Representation Architecture for Cross-Domain Applications](https://mlsanity.com/paper/2606.01520/) (2026)
- [SR-JEPA: Learning Predictive Latent State in 3D Scenes](https://mlsanity.com/paper/2608.05774/) (2026)
- [AD-E2E-JEPA: A Joint-Embedding Predictive Architecture For End-to-End Autonomous Driving](https://mlsanity.com/paper/2609.34085/) (2026)
- [LeCuration: A Tiny World Model as a Data Curation Multi-Tool](https://mlsanity.com/paper/2610.09285/) (2026)

## Abstract (arXiv)

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.