---
title: "Wayve | GAIA-1 & Autonomous Driving | world-models.io"
canonical: "https://world-models.io/en/labs/wayve/"
description: "Wayve builds generative world models for autonomous driving, including GAIA-1 for diverse driving scenario generation."
author: "Bernard Grenat"
generated: "2026-10-30"
---

New: the Timeline is live. Track world model releases, papers, and benchmark updates in real time.

The Knowledge Hub for AI World Models

# Wayve

Wayve develops generative world models for autonomous driving, with GAIA-1 generating realistic driving scenarios conditioned on text, action, and video inputs.

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/).

## About Wayve

Main editorial body preserved directly in static HTML.

Wayve is a UK-based autonomous driving company that develops end-to-end learned driving systems powered by world models. Their GAIA-1 model is a generative world model that predicts realistic driving scenarios from text, action, and video inputs. Wayve's approach integrates world model learning directly into the autonomous driving stack, enabling scenario generation and safety testing.

Wayve is a industry organization based in United Kingdom with a visible footprint in AI world models.

## Lab Snapshot

Static summary of the lab profile for crawlers and no-JS readers.

| Attribute | Value |
| --- | --- |
| Lab | Wayve |
| Short Name | Wayve |
| Type | industry |
| Region | United Kingdom |
| Related Models | 3 |
| Description | Wayve is a UK-based autonomous driving company that develops end-to-end learned driving systems powered by world models. Their GAIA-1 model is a generative world model that predicts realistic driving scenarios from text, action, and video inputs. Wayve's approach integrates world model learning directly into the autonomous driving stack, enabling scenario generation and safety testing. |

## Focus Areas

Focus areas surfaced directly from the lab record.

autonomous-driving

generative-world-models

end-to-end-driving

scenario-generation

## Related Models

Models currently associated with this lab in the local knowledge base.

| Model | Category | Year | Index v1.1 |
| --- | --- | --- | --- |
| [GAIA-1](https://world-models.io/en/models/gaia-1/) | Foundation World Model | 2023 | 61/100 |
| [GAIA-2](https://world-models.io/en/models/gaia-2/) | Generative World Model | 2025 | N/A |
| [MILE](https://world-models.io/en/models/mile/) | Foundation World Model | 2022 | 63/100 |

## Frequently Asked Questions

FAQ answers rendered directly into static HTML for extractable responses.

### What is GAIA-1?

GAIA-1 is Wayve's generative world model for autonomous driving. It generates realistic driving scenarios conditioned on text, action, and video inputs, enabling diverse scenario generation for self-driving testing.

## Quick Answer

Short extractable summary preserved directly in static HTML.

- Wayve is a industry organization based in United Kingdom with a visible footprint in AI world models.
- On world-models.io, it is associated with 3 world models across editorially linked pages.
- Use this page to review the organization profile, connected models, related comparisons, and official sources.

## Editorial Trust Signals

Editorial provenance and refresh policy preserved directly in static HTML.

**Published by** world-models.io editorial board.

**Lead editor** Bernard Grenat.

This lab page distinguishes official organization sources from world-models.io editorial synthesis and related model coverage.

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: 2026-09-27.

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

## Official Sources

Primary lab and model sources embedded in static HTML.

- [Wayve official website](https://wayve.ai) Primary organizational source
- [GAIA-1 paper](https://arxiv.org/abs/2309.17080) Wayve primary research source
- [GAIA-2 paper](https://wayve.ai/wp-content/uploads/2025/03/GAIA_2_Technical_Report.pdf) Wayve primary research source
- [MILE paper](https://arxiv.org/abs/2210.07729) Wayve primary research source