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Autonomous vehicle (AV) research is undergoing a rapid shift. The field is being reshaped by the emergence of reasoning-based vision–language–action (VLA) models that bring human-like thinking to AV decision-making.
These models can be viewed as implicit world models operating in a semantic space, allowing AVs to solve complex problems step-by-step and to generate reasoning traces that mirror human thought processes.
This shift extends beyond the models themselves: traditional open-loop evaluation is no longer sufficient to rigorously assess such models, and new evaluation tools are required.
Recently, NVIDIA introduced Alpamayo, a family of models, simulation tools, and datasets to enable development of reasoning-based AV architectures.
Our goal is to provide researchers and developers with a flexible, fast, and scalable platform for evaluating, and ultimately training, modern reasoning-based AV architectures in realistic closed-loop settings.
In this blog, we introduce Alpamayo and how to get up and running with reasoning-based AV development:• Part 1: Introducing NVIDIA Alpamayo 1, an open, 10B reasoning VLA model, as well as how to use the model to both generate trajectory predictions and review the corresponding reasoning traces. • • Part 2: Introducing the Physical AI dataset, one of the largest and most geographically diverse open AV datasets available that enables training and evaluating these models. • • Part 3: Introducing NVIDIA AlpaSim, an open-source end-to-end simulation tool designed for evaluating end-to-end models • • Part 4: Leveraging the ecosystem altogether to drive Alpamayo 1 closed-loop on reconstructed data within AlpaSim. •
These three key components provide the essential pieces needed to start building reasoning-based VLA models: a base model, large-scale data for training, and a simulator for testing and evaluation.
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