A world model is an AI system that learns a predictive representation of how an environment changes over time. It can use observations, actions, or language to estimate future states and possible outcomes before an agent acts. World models support planning in model-based reinforcement learning, robotics, embodied AI, autonomous driving, and interactive simulation. This site catalogs these systems with model records, research papers, benchmark context, and source links. Start with the world models database, compare systems on the leaderboard, or read the Performance Index methodology.
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| Attribute | Value |
|---|---|
| Model | MILE |
| Lab / Organization | Wayve |
| Category | Foundation World Model |
| Subtype | Driving World Model |
| World Model Type | Model-based imitation learning for driving |
| Primary Domain | Autonomous Driving |
| Architecture | Variational autoencoder with spatial-temporal transformer for dynamics prediction |
| Modality | Visual (Multi-camera) + Ego-state |
| Training Method | Model-based imitation learning with future imagination rollouts |
| Status | active |
| Year | 2022 |
| Performance Index | 63/100 (medium confidence, v1.1) |
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MILE (Model-based Imitation LEarning) is a world model for autonomous driving that learns a joint representation of scene dynamics, semantic segmentation, and ego-vehicle planning from camera-only input. Unlike modular driving stacks, MILE uses a learned world model to imagine future driving scenarios and optimize planning within that imagination, achieving state-of-the-art performance on the CARLA benchmark. It pioneered the concept of world-model-based end-to-end driving.
MILE is a model-based imitation learning for driving developed by Wayve in 2022 for autonomous driving.
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| Signal | Value |
|---|---|
| Definition | MILE is a model-based imitation learning for driving developed by Wayve in 2022 for autonomous driving. |
| Short Description | A world model for autonomous driving that jointly learns dynamics, perception, and planning through model-based imitation learning. |
| Benchmark Rows | 1 |
| FAQ Entries | 2 |
| Related Models | 3 |
| Related Guides | 0 |
| Related Research Topics | 0 |
| Last Updated | 2026-04-07 |
Key capabilities associated with this model.
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Published benchmark evidence attached to this model record.
| Benchmark | Metric | Result | Source |
|---|---|---|---|
| CARLA Benchmark | Driving Score 81 DS | State-of-the-art | Source |
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| Reference | Link |
|---|---|
| Hu et al., 2022. Model-Based Imitation Learning for Urban Driving. NeurIPS 2022. arXiv:2210.07729 | Open source |
Nearby models linked from the current editorial record.
| Model | Category | World Model Type | Index v1.1 |
|---|---|---|---|
| GAIA-1 | Foundation World Model | Generative driving world model | 61/100 |
| Copilot4D | Foundation World Model | Spatiotemporal LiDAR world model | 57/100 |
| NVIDIA Cosmos | Foundation World Model | Video world foundation model | 87/100 |
Side-by-side comparisons already connected to this model.
| Comparison | Matchup | Summary |
|---|---|---|
| MILE vs GAIA-1 | MILE vs GAIA-1 | Both from Wayve, these models represent two generations of driving world models. MILE focuses on actionable imagination for planning, while GAIA-1 scales to photorealistic scenario generation. |
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MILE uses a VAE-based world model for imagination-based planning and focuses on decision-making. GAIA-1 is a larger generative model that produces high-fidelity video predictions but doesn't directly plan actions.
MILE was primarily evaluated in the CARLA simulator. Wayve has since developed real-world driving systems inspired by MILE's world-model approach.
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Published by world-models.io editorial board.
Lead editor Tyler D. - Technical editor, methodology and benchmark analysis.
This model page synthesizes primary papers, official model pages, benchmark evidence, and related world-models.io context into a reference resource.
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