New Insights for Scaling Laws in Autonomous Driving - CleanTechnica

The article from CleanTechnica discusses Waymo’s recent research into applying scaling laws—well-established in large language models (LLMs)—to autonomous driving, specifically in motion forecasting and planning. Waymo’s study leveraged an extensive internal dataset of 500,000 hours of driving, much larger than prior AV datasets, to investigate how increasing model size, training data, and compute resources impact AV performance. The findings reveal that, similar to LLMs, motion forecasting quality improves predictably following a power-law relationship with training compute. Additionally, scaling data and inference compute enhances the model’s ability to handle complex driving scenarios, and closed-loop planning performance also benefits from increased scale. These results mark a significant advancement by demonstrating for the first time that real-world autonomous vehicle capabilities can be systematically improved through scaling, providing a predictable path to better performance. This predictability applies not only to model training objectives and open-loop forecasting metrics but also to closed-loop planning in simulations, which more closely reflect real driving conditions.