---
title: "MILE | Model-Based Imitation Learning for Driving | world-models.io"
canonical: "https://world-models.io/en/models/mile/"
description: "MILE is a model-based imitation learning approach for autonomous driving using learned world models. Review MILE: evidence, scope, limitations, and primary."
author: "Tyler D."
generated: "2026-10-30"
---

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The Knowledge Hub for AI World Models

# MILE

MILE (Model-Based Imitation Learning) learns a world model of driving dynamics from expert demonstrations, enabling future-aware planning for autonomous vehicles.

robotics

model-based-rl

simulation

embodied-ai

## What is a world model?

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](https://world-models.io/en/world-models-database/), compare systems on the [leaderboard](https://world-models.io/en/leaderboard/), or read the [Performance Index methodology](https://world-models.io/en/methodology/performance-index/).

## Key Attributes

Static HTML snapshot of the model record for crawlers and no-JS readers.

| 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) |

## About MILE

Main editorial body preserved directly in static HTML.

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.

## Editorial Snapshot

Short extractable facts for answer engines and no-JS readers.

| 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 |

## Notable Features

Key capabilities associated with this model.

- • Joint perception-prediction-planning
- • Imagination-based planning
- • Camera-only input
- • End-to-end differentiable

## Use Cases

Representative applications attached to this model record.

Autonomous driving

Driving scene prediction

End-to-end planning

Simulation-based training

## Strengths and Limitations

Balanced assessment surfaced in static HTML.

### Strengths

- • Unified world model for driving
- • State-of-the-art on CARLA
- • Camera-only (no LiDAR needed)
- • Imagination enables counterfactual reasoning

### Limitations

- • Evaluated primarily in simulation
- • Single-city training data
- • Limited to urban driving scenarios

## Benchmarks

Published benchmark evidence attached to this model record.

| Benchmark | Metric | Result | Source |
| --- | --- | --- | --- |
| CARLA Benchmark | Driving Score 81 DS | State-of-the-art | [Source](https://arxiv.org/abs/2210.07729) |

## References and Citations

Primary references preserved in static HTML for citation extraction.

| Reference | Link |
| --- | --- |
| Hu et al., 2022. Model-Based Imitation Learning for Urban Driving. NeurIPS 2022. arXiv:2210.07729 | [Open source](https://arxiv.org/abs/2210.07729) |

## Related Models

Nearby models linked from the current editorial record.

| Model | Category | World Model Type | Index v1.1 |
| --- | --- | --- | --- |
| [GAIA-1](https://world-models.io/en/models/gaia-1/) | Foundation World Model | Generative driving world model | 61/100 |
| [Copilot4D](https://world-models.io/en/models/copilot4d/) | Foundation World Model | Spatiotemporal LiDAR world model | 57/100 |
| [NVIDIA Cosmos](https://world-models.io/en/models/nvidia-cosmos/) | Foundation World Model | Video world foundation model | 87/100 |

## Direct Comparisons

Side-by-side comparisons already connected to this model.

| Comparison | Matchup | Summary |
| --- | --- | --- |
| [MILE vs GAIA-1](https://world-models.io/en/compare/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. |

## Frequently Asked Questions

FAQ answers rendered directly into static HTML for extractable responses.

### How does MILE differ from GAIA-1?

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.

### Can MILE drive a real car?

MILE was primarily evaluated in the CARLA simulator. Wayve has since developed real-world driving systems inspired by MILE's world-model approach.

## Quick Answer

Short extractable summary preserved directly in static HTML.

- MILE is a model-based imitation learning for driving developed by Wayve in 2022 for autonomous driving.
- Use this page when you need a fast read on how MILE fits into the foundation world model landscape, then validate the details in the benchmarks, citations, and related pages.
- A key strength surfaced in the editorial record is unified world model for driving.

## Editorial Trust Signals

Editorial provenance and refresh policy preserved directly in static HTML.

**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.

Each editorial page is assembled from primary sources, normalized into extractable summaries, checked for factual drift, and reviewed before publication or major refreshes. Last reviewed: 2026-04-07.

Pages are refreshed when a new paper, benchmark, release, architecture update, or stronger primary source materially changes the answer a reader or AI system should retrieve.

Each page links back to relevant primary sources and keeps a stable canonical URL so readers can verify claims, trace context, and reference the most up-to-date version. [See the editorial policy.](https://world-models.io/en/editorial-policy/)

Primary sources only

Last reviewed date visible

Methodology documented

Source links included

Correction channel published

Review date visible

## External Sources

Primary model and lab sources embedded in static HTML.

- [MILE paper](https://arxiv.org/abs/2210.07729) Wayve primary research source
- [Wayve official website](https://wayve.ai) Related lab

## References

1. [[1] Hu et al., 2022. Model-Based Imitation Learning for Urban Driving. NeurIPS 2022. arXiv:2210.07729](https://arxiv.org/abs/2210.07729)