More Camera Frames, Better Driving? Within-Scene Information Scaling in Autonomous Driving

Autonomous driving requires models to learn vehicle motion, agent interactions, and scene evolution from driving data. Existing scaling efforts focus on increasing model capacity and expanding driving data coverage, largely treating driving scenes as the unit of training information. However, how the information exposed within each scene should scale with model capacity remains underexplored. We study within-scene information scaling, using temporal sampling frequency as a controlled probe to vary camera-frame density within fixed driving scenes. We formulate a minimal information–capacity model that captures the interaction among bounded driving-relevant information, increasing driving-irrelevant exposure, and finite model capacity. The model characterizes the capacity-dependent scaling behavior of within-scene information and predicts that the preferred temporal sampling frequency increases with model capacity under its assumptions. We conduct controlled frequency sweeps across compact end-to-end models, large vision-language-action (VLA) models, and world-action models on Waymo, nuScenes, and PAVE and in NAVSIM-based settings, with additional iteration-matched comparisons. AutoVLA and DriveVLA-W0 attain their lowest reported nuScenes trajectory errors at the highest tested frequency, yet further reductions above 4 Hz are much smaller for DriveVLA-W0. Drive-JEPA achieves strong driving performance on NAVTEST already at 2 Hz. These findings distinguish how much a model improves with denser temporal exposure from how well it already performs under sparse temporal exposure. They motivate evaluating driving performance alongside frequency-response curves and jointly developing model architectures, learning objectives, and within-scene data construction to utilize driving-relevant information that affects the driving decision while limiting the influence of driving-irrelevant exposure.