Learning to Drive is a Free Gift: Large-Scale Label-Free Autonomy Pretraining from Unposed In-The-Wild Videos Humans build road intuition before they ever drive. LFG applies the same idea to autonomous vehicles, pretraining on millions of unlabeled dashcam clips instead of costly labeled sensor data. Quentin Herau • August 10, 2026 • 5 min read Training self-driving cars today requires enormous amounts of specialized data: calibrated multi-sensor rigs, millions of miles of driving, and painstaking labeling. This is expensive and hard to scale to new cities and conditions. Yet millions of hours of dashcam footage are freely available online, people driving across the world in all kinds of scenarios. Just as humans build road intuition long before getting behind the wheel, such video contains rich information about how roads look, how objects move, and how scenes unfold. The problem? It comes with no labels at all, which is why it has remained largely untapped. The research described here, nicknamed LFG or "Learning to Drive is a Free Gift," bridges this gap. It uses a set of existing AI models as teachers, each one labeling a different aspect of unlabeled dashcam footage, and trains a new model to reconstruct the current scene and predict what comes next. When fine-tuned for driving, this single front-camera model outperforms systems built with far richer sensor setups. It resulted in a paper accepted to CVPR 2026. A Data Bottleneck, And Millions of Hours of Untapped Footage Building self-driving systems today requires vast amounts of expensive labeled data: 3D lidar scans, high-definition maps, hand-annotated bounding boxes, and expert driver trajectories. Collecting this data is slow, costly, and hard to scale across geographies and driving conditions. Meanwhile, the internet is overflowing with dashcam footage. Millions of hours of dashcam footage are uploaded every year, covering an enormous diversity