Scaling Laws of Motion Forecasting and Planning - Technical Report

• URL: http://arxiv.org/abs/2506.08228v2
• Date: Mon, 08 Sep 2025 00:53:59 GMT
• Title: Scaling Laws of Motion Forecasting and Planning - Technical Report
• Authors: Mustafa Baniodeh, Kratarth Goel, Scott Ettinger, Carlos Fuertes, Ari Seff, Tim Shen, Cole Gulino, Chenjie Yang, Ghassen Jerfel, Dokook Choe, Rui Wang, Benjamin Charrow, Vinutha Kallem, Sergio Casas, Rami Al-Rfou, Benjamin Sapp, Dragomir Anguelov,
• Abstract summary: We study the empirical scaling laws of a family of encoder-decoder autoregressive transformer models.<n>We observe a strong correlation between model training loss and model evaluation metrics.<n>We briefly study the utility of training on general logged driving data of other agents to improve the performance of the ego-agent.
• Score: 21.486301157587132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the empirical scaling laws of a family of encoder-decoder autoregressive transformer models on the task of joint motion forecasting and planning in the autonomous driving domain. Using a 500 thousand hours driving dataset, we demonstrate that, similar to language modeling, model performance improves as a power-law function of the total compute budget, and we observe a strong correlation between model training loss and model evaluation metrics. Most interestingly, closed-loop metrics also improve with scaling, which has important implications for the suitability of open-loop metrics for model development and hill climbing. We also study the optimal scaling of the number of transformer parameters and the training data size for a training compute-optimal model. We find that as the training compute budget grows, optimal scaling requires increasing the model size 1.5x as fast as the dataset size. We also study inference-time compute scaling, where we observe that sampling and clustering the output of smaller models makes them competitive with larger models, up to a crossover point beyond which a larger models becomes more inference-compute efficient. Overall, our experimental results demonstrate that optimizing the training and inference-time scaling properties of motion forecasting and planning models is a key lever for improving their performance to address a wide variety of driving scenarios. Finally, we briefly study the utility of training on general logged driving data of other agents to improve the performance of the ego-agent, an important research area to address the scarcity of robotics data for large capacity models training.

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