Science

Driving the science of autonomy at Wayve

Science

Advancing Embodied Intelligence

Our goal is to achieve scientific breakthroughs in Embodied AI that address real-world challenges in autonomous driving and beyond.

Since the very beginning, Wayve has been deeply rooted in cutting-edge AI research. Today, our Science division is at the forefront of end-to-end autonomous driving research, driving significant advancements in the performance, measurement, and safety of self-driving technology.

Foundation models for autonomous driving

Our Science innovation is dedicated to developing foundation models for autonomous driving. These ‘general purpose’ models excel at various driving tasks such as perception, reasoning, and motion planning.

We train these models using an extensive corpus of diverse vision, language, and action data from our R&D fleet, partner fleets, and our generative AI models. This multifaceted training approach provides a deep and nuanced understanding of the dynamics of the physical world and fine-tunes the model’s decision-making for safe driving on the road.

Core research areas

Our commitment to developing foundation models and products is supported by extensive research in:

Generative world models

GAIA-2, our latest generative world model for autonomy, pushes the boundaries of synthetic data generation with enhanced controllability, expanded geographic diversity, and broader vehicle representation.

Learn about GAIA

Measurement and simulation

Utilizing neural rendering, we can automatically generate photorealistic 3D worlds from real driving data, creating cost-effective testing scenarios and enriching training data for our foundation models.

Learn about Ghost Gym

Natural language

We’re leveraging natural language to improve the learning process and explainability of our models, making AI decisions more understandable and transparent.

Learn about LINGO

Publications

26 Mar 2025
GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving
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12 Mar 2025
SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment
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14 Jun 2024
CarLLaVA: Vision language models for camera-only closed-loop driving
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21 Dec 2023
LingoQA: Video Question Answering for Autonomous Driving
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13 Oct 2023
Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving
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29 Sep 2023
GAIA-1: A Generative World Model for Autonomous Driving
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03 Nov 2022
Model-Based Imitation Learning for Urban Driving
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18 Oct 2021
FIERY: Future Instance Prediction in Bird’s-Eye View from Surround Monocular Cameras
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12 Aug 2021
Reimagining an autonomous vehicle
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07 May 2021
Video Class Agnostic Segmentation with Contrastive Learning for Autonomous Driving
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20 Apr 2021
Video Class Agnostic Segmentation Benchmark for Autonomous Driving
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17 Jul 2020
Probabilistic Future Prediction for Video Scene Understanding
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19 Dec 2019
Learning a Spatio-Temporal Embedding for Video Instance Segmentation
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05 Dec 2019
Urban Driving with Conditional Imitation Learning
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10 Dec 2018
Learning to Drive from Simulation without Real World Labels
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20 Nov 2018
Orthographic Feature Transform for Monocular 3D Object Detection
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11 Sep 2018
Learning to Drive in a Day
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Join our mission

If you're passionate about pushing the boundaries of AI and autonomous driving, explore our open roles in Science at Wayve.

Applied Scientist, Controllable GAIA
London
Tech Lead Manager - Multi Modal Foundation Models (Language)
Sunnyvale
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