OpenLongTail: Generative Scaling of Long-Tail Driving Data
Authors: name: Lulin Liu, name: Nuo Chen, name: Yan Wang, name: Bangya Liu, name: Wenyan Cong, name: Hezhen Hu, name: Boris Ivanovic, name: Hao Wang, name: Ziyao Zeng, name: Xinyu Gong, name: Yang Zhou, name: Zixiang Xiong, name: Dilin Wang, name: Zhangyang Wang, name: Weisong Shi, name: Ruohan Zhang, name: Marco Pavone, name: Zhiwen Fan
Organization: Texas A&M University

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 enhance cross-view consistency and the temporal alignment for the newly generated views by injecting Plücker ray geometry into the scalable generation engine. By synthesizing heterogeneous long-tail data, we observe a significant improvement in closed-loop driving robustness in handling long-tail events. By measuring the extrapolative view synthesis and pose metrics, we validate the effectiveness of OpenLongTail in visual fidelity, cross-view consistency, and ego-trajectory recovery.