DriveGPT: Scaling Autoregressive Behavior Models for Driving

Understand

We present DriveGPT, a scalable behavior model for autonomous driving.

• We model driving as a sequential decision-making task, and learn a transformer model to predict future agent states as tokens in an autoregressive fashion.
• We scale up our model parameters and training data by multiple orders of magnitude, enabling us to explore the scaling properties in terms of dataset size, model parameters, and compute.
• We evaluate DriveGPT across different scales in a planning task, through both quantitative metrics and qualitative examples, including closed-loop driving in complex real-world scenarios.