# OWMDrive: Causality-Aware End-to-End Autonomous Driving via 4D Occupancy World Model arXiv: https://arxiv.org/abs/2606.30421 ยท HTML: https://mlsanity.com/paper/2606.30421/ ## Key facts - Cited by 2 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 30 of its 35 references that are in the map. - First posted on arXiv 29 Jun 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, 30 of 30) - [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019) - [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018) - [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022) - [Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction](https://mlsanity.com/paper/2404.02905/) (2024) - [Autoregressive Image Generation using Residual Quantization](https://mlsanity.com/paper/2203.01941/) (2022) - [VAD: Vectorized Scene Representation for Efficient Autonomous Driving](https://mlsanity.com/paper/2303.12077/) (2023) - [TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2205.15997/) (2022) - [DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model](https://mlsanity.com/paper/2310.01412/) (2023) - [NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking](https://mlsanity.com/paper/2406.15349/) (2024) - [Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability](https://mlsanity.com/paper/2405.17398/) (2024) - [DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving](https://mlsanity.com/paper/2411.15139/) (2024) - [VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning](https://mlsanity.com/paper/2402.13243/) (2024) - [Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation](https://mlsanity.com/paper/2406.06978/) (2024) - [Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?](https://mlsanity.com/paper/2312.03031/) (2023) - [SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation](https://mlsanity.com/paper/2405.19620/) (2024) - [OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving](https://mlsanity.com/paper/2311.16038/) (2023) - [GenAD: Generative End-to-End Autonomous Driving](https://mlsanity.com/paper/2402.11502/) (2024) - [DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba](https://mlsanity.com/paper/2408.03601/) (2024) - [OccSora: 4D Occupancy Generation Models as World Simulators for Autonomous Driving](https://mlsanity.com/paper/2405.20337/) (2024) - [Driving in the Occupancy World: Vision-Centric 4D Occupancy Forecasting and Planning via World Models for Autonomous Driving](https://mlsanity.com/paper/2408.14197/) (2024) - [DOME: Taming Diffusion Model into High-Fidelity Controllable Occupancy World Model](https://mlsanity.com/paper/2410.10429/) (2024) - [End-to-End Autonomous Driving through V2X Cooperation](https://mlsanity.com/paper/2404.00717/) (2024) - [Versatile Behavior Diffusion for Generalized Traffic Agent Simulation](https://mlsanity.com/paper/2404.02524/) (2024) - [Occ-LLM: Enhancing Autonomous Driving with Occupancy-Based Large Language Models](https://mlsanity.com/paper/2502.06419/) (2025) - [DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving](https://mlsanity.com/paper/2503.12170/) (2025) - [Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models](https://mlsanity.com/paper/2409.16663/) (2024) - [Generative Planning with 3D-vision Language Pre-training for End-to-End Autonomous Driving](https://mlsanity.com/paper/2501.08861/) (2025) - [InsightDrive: Insight Scene Representation for End-to-End Autonomous Driving](https://mlsanity.com/paper/2503.13047/) (2025) - [OccProphet: Pushing Efficiency Frontier of Camera-Only 4D Occupancy Forecasting with Observer-Forecaster-Refiner Framework](https://mlsanity.com/paper/2502.15180/) (2025) - [A Rule-Based Behaviour Planner for Autonomous Driving](https://mlsanity.com/paper/2407.00460/) (2024) ## Built on by (most cited first, 2 of 2) - [Data-Driven Risk Fields for Safer End-to-End Autonomous Driving](https://mlsanity.com/paper/2609.10377/) (2026) - [AffordDrive3D: Affordance-Aware World-Action Modeling with Spatial Understanding](https://mlsanity.com/paper/2610.11060/) (2026) ## Abstract (arXiv) Autonomous driving systems are steadily moving toward end-to-end paradigms to mitigate the limited adaptability of rule-based pipelines in complex traffic environments. However, most existing learning-based methods still make decisions from static representations of the current scene, without explicit future rollouts or modeling of the temporal causal dynamics in traffic interactions. This limitation often results in unstable or overly conservative planning under high-uncertainty conditions, such as occlusions and unexpected events. To overcome these challenges, we introduce OWMDrive, a generative end-to-end driving framework built upon an Occupancy World Model for multi-step 3D occupancy forecasting, which serves as a conditional prior to guide diffusion-based planning. Conditioned on both current observations and predicted future states, the planner iteratively refines trajectory candidates to generate a reinforced driving trajectory. By explicitly modeling scene evolution over future horizons, OWMDrive captures key spatiotemporal causal dependencies, which leads to more foresighted and robust trajectory generation. Extensive experiments demonstrate that OWMDrive significantly improves planning reliability and safety, especially in challenging and partially observable driving scenarios.