OpenLongTail: Generative Scaling of Long-Tail Driving Data

Scaling robust driving policies is fundamentally bottlenecked by the scarcity of edge cases in curated datasets. While the real world continuously captures these critical events, such long-tail events remain underutilized when collected from heterogeneous sources. Specifically, diverse but valuable in-the-wild long-tail videos lack the full view coverage required for training policy models, often missing multi-view poses or originating solely from monocular dash cameras. This modality gap prevents these ubiquitous observations from being converted into scalable training data for long-tail generalization. We introduce OpenLongTail, an open-source generative data engine for scaling autonomous driving policies under long-tail events. To transform heterogeneous data sources into view-aligned and temporally coherent multi-view assets that are useful for policy learning, we develop a pose-informed extrapolative view synthesis pipeline that generates the missing views. We further incorporate Plücker-ray camera conditioning and lane-weighted supervision to provide geometric guidance for missing-view synthesis while placing greater emphasis on sparse lane structures during training. By synthesizing heterogeneous long-tail data, we observe an effective improvement in closed-loop driving robustness in handling long-tail events. Using extrapolative view-synthesis and pose-recovery metrics, we validate OpenLongTail in terms of visual fidelity and ego-trajectory accuracy.