GAIA-1: A Generative World Model for Autonomous Driving
Anthony Hu Lloyd Russell Hudson Yeo Zak Murez George Fedoseev Alex Kendall Jamie Shotton Gianluca Corrado
Autonomous driving promises transformative improvements to transportation,
but building systems capable of safely navigating the unstructured complexity
of real-world scenarios remains challenging. A critical problem lies in
effectively predicting the various potential outcomes that may emerge in
response to the vehicle's actions as the world evolves.
  To address this challenge, we introduce GAIA-1 ('Generative AI for
Autonomy'), a generative world model that leverages video, text, and action
inputs to generate realistic driving scenarios while offering fine-grained
control over ego-vehicle behavior and scene features. Our approach casts world
modeling as an unsupervised sequence modeling problem by mapping the inputs to
discrete tokens, and predicting the next token in the sequence. Emerging
properties from our model include learning high-level structures and scene
dynamics, contextual awareness, generalization, and understanding of geometry.
The power of GAIA-1's learned representation that captures expectations of
future events, combined with its ability to generate realistic samples,
provides new possibilities for innovation in the field of autonomy, enabling
enhanced and accelerated training of autonomous driving technology.