SkyDrive: Learning to Drive in a New City from Aerial Traffic Monitoring
Autonomous driving has made remarkable progress through imitation learning with massive human demonstration data. However, a trained planner often degrades severely when applied to a new environment zero-shot, because of domain shifts in traffic regulations, road layout and driving behaviors. Therefore, adapting a trajectory planner to a new city typically requires resource-demanding local data collection with a vehicle sensor suite. In this work, we show that driving behavior can be learned from a scalable and efficient alternative. We introduce \emph{SkyDrive}, a framework that utilizes drone-based traffic monitoring to provide efficient supervision for autonomous driving agents in a ne...
| Authors | Weijiang Xiong, Lan Feng, Alexandre Alahi et al. |
|---|---|
| Primary category | cs.RO |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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