New Insights for Scaling Laws in Autonomous Driving
Contents
AI performance have been powered by scale.
500,000 hours of driving
- Similar to LLMs, motion forecasting quality also follows a power-law as a function of training compute.
- Data scaling is critical for improving the model performance.
- Scaling inference compute also improves the model’s ability to handle more challenging driving scenarios.
- Closed-loop performance follows a similar scaling trend. This suggests, for the first time, that real-world AV performance can be improved by increasing training data and compute.