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.

翻译：自主驾驶有望为交通运输带来变革性改善，但构建能够安全应对现实世界场景非结构化复杂性的系统仍极具挑战。关键难题在于有效预测车辆行动后世界演变过程中可能产生的各种潜在结果。为应对这一挑战，我们提出GAIA-1（"面向自主性的生成式AI"），这是一种生成式世界模型，它利用视频、文本和行为输入生成逼真的驾驶场景，同时实现对自我车辆行为及场景特征的精细控制。我们的方法将世界建模视为无监督序列建模问题，通过将输入映射为离散令牌并预测序列中的下一个令牌。模型涌现的特性包括学习高层级结构和场景动态、情境感知、泛化能力以及对几何结构的理解。GAIA-1所学表征捕捉未来事件预期，结合其生成逼真样本的能力，为自主性领域创新提供了新可能，从而能够加速并增强自主驾驶技术的训练。