DriveGPT：自動運転のための自己回帰行動モデルのスケーリング
DriveGPT: Scaling Autoregressive Behavior Models for Driving
自動運転を逐次意思決定タスクとして捉え、将来のエージェント状態をトークンとして自己回帰的に予測するTransformerモデルを大規模化し、スケーリング則や予測性能を検証した論文。
著者: Xin Huang, Eric M. Wolff, Paul Vernaza, Tung Phan-Minh, Hongge Chen, David S. Hayden, Mark Edmonds, Brian Pierce, Xinxin Chen, Pratik Elias Jacob, Xiaobai Chen, Chingiz Tairbekov, Pratik Agarwal, Tianshi Gao, Yuning Chai, Siddhartha Srinivasa
分類: cs.LG, cs.AI, cs.CV, cs.RO
原文アブストラクト
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. In a separate prediction task, DriveGPT outperforms state-of-the-art baselines and exhibits improved performance by pretraining on a large-scale dataset, further validating the benefits of data scaling.
