# Auto-JEPA: A Latent World Model of Continuous Intent for End-to-End Autonomous Driving arXiv: https://arxiv.org/abs/2607.29031 ยท HTML: https://mlsanity.com/paper/2607.29031/ ## 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 25 of its 29 references that are in the map. - First posted on arXiv 31 Jul 2026. ## Placed in - World models / [World models for autonomous driving](https://mlsanity.com/wm/world-models-for-autonomous-driving/) - End-to-end autonomous driving / [Generative simulation for driving](https://mlsanity.com/driving/generative-simulation-for-driving/) ## Builds on (most cited first, 25 of 25) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018) - [V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning](https://mlsanity.com/paper/2506.09985/) (2025) - [GAIA-1: A Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2309.17080/) (2023) - [TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2205.15997/) (2022) - [Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability](https://mlsanity.com/paper/2405.17398/) (2024) - [Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving](https://mlsanity.com/paper/2311.17918/) (2023) - [VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning](https://mlsanity.com/paper/2402.13243/) (2024) - [AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning](https://mlsanity.com/paper/2506.13757/) (2025) - [Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation](https://mlsanity.com/paper/2406.06978/) (2024) - [OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving](https://mlsanity.com/paper/2311.16038/) (2023) - [OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model](https://mlsanity.com/paper/2503.23463/) (2025) - [Enhancing End-to-End Autonomous Driving with Latent World Model](https://mlsanity.com/paper/2406.08481/) (2024) - [MotionLM: Multi-Agent Motion Forecasting as Language Modeling](https://mlsanity.com/paper/2309.16534/) (2023) - [End-to-End Driving with Online Trajectory Evaluation via BEV World Model](https://mlsanity.com/paper/2504.01941/) (2025) - [DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving](https://mlsanity.com/paper/2510.12796/) (2025) - [Visual Point Cloud Forecasting enables Scalable Autonomous Driving](https://mlsanity.com/paper/2312.17655/) (2023) - [DrivingGPT: Unifying Driving World Modeling and Planning with Multi-modal Autoregressive Transformers](https://mlsanity.com/paper/2412.18607/) (2024) - [Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation](https://mlsanity.com/paper/2503.12820/) (2025) - [DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning](https://mlsanity.com/paper/2506.06659/) (2025) - [AdaThinkDrive: Adaptive Thinking via Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2509.13769/) (2025) - [DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Driving](https://mlsanity.com/paper/2602.06521/) (2026) - [Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Driving](https://mlsanity.com/paper/2601.22032/) (2026) - [PRANK: motion Prediction based on RANKing](https://mlsanity.com/paper/2010.12007/) (2020) - [Devil is in Narrow Policy: Unleashing Exploration in Driving VLA Models](https://mlsanity.com/paper/2603.06049/) (2026) ## Built on by (most cited first, 5 of 5) - [DA-WAM: Decision-Aligned Future Latents for Driving World Models](https://mlsanity.com/paper/2608.19085/) (2026) - [WALT: Learning World-Model-Aligned Latent Trajectories for Autonomous Driving](https://mlsanity.com/paper/2609.30436/) (2026) - [ReDrive: Shaping Representations with World Modeling for End-to-End Driving](https://mlsanity.com/paper/2609.33854/) (2026) - [AD-E2E-JEPA: A Joint-Embedding Predictive Architecture For End-to-End Autonomous Driving](https://mlsanity.com/paper/2609.34085/) (2026) - [FlowSim: Rendering-Free Closed-Loop Simulation for Evaluating End-to-End Driving Systems](https://mlsanity.com/paper/2610.18030/) (2026) ## Abstract (arXiv) Existing autonomous-driving world models typically perform dense prediction of future videos, occupancy states, BEV representations, or agent motion. We argue that planning need not reconstruct the complete future world, but only focus on scene features that affect future ego action. Based on this perspective, we propose Auto-JEPA, an action-oriented latent world model that learns continuous future driving intent through joint-embedding prediction. Given visual observations, egomotion history, and navigation commands, Auto-JEPA predicts an intent embedding aligned with the latent representation of the future ego trajectory. The predicted intent retrieves executable trajectories from a fixed trajectory memory, which are then ranked by a scene-conditioned candidate selection module. Auto-JEPA keeps the visual encoder frozen, requires no explicit perception annotations, and uses no learned trajectory generator. By optimizing only task-specific modules for trajectory representation, intent prediction, and candidate selection, Auto-JEPA achieves 91.3 PDMS on NAVSIM v1 and 89.1 EPDMS on NAVSIM v2. Semantic occlusion experiments show that masking dynamic-agent regions induces an average intent change 2.97x that of equal-area random masking. Moreover, occluding vehicles that affect future driving substantially changes the predicted intent and selected trajectory, whereas both remain essentially unchanged when non-influential vehicles are occluded. These results show that future-intent prediction encourages the model to focus on planning-relevant visual features and supports high-quality planning without dense future-world modeling.