SkyDrive：从空中交通监测学习在新城市中驾驶

中文摘要 AI 辅助

自动驾驶通过模仿学习结合大量人类演示数据已取得显著进展，但训练好的规划器在零样本应用于新环境时通常会严重性能下降，原因在于交通规则、道路布局和驾驶行为的领域偏移。因此，将轨迹规划器适配到新城市通常需要使用车辆传感器套件进行资源密集型的本地数据采集。在本研究中，我们展示了可以从一种可扩展且高效的替代方案中学习驾驶行为。我们引入SkyDrive，这是一个利用基于无人机的交通监测为新环境中的自动驾驶智能体提供高效监督的框架。基于车辆的数据采集记录了自车及其周围环境，而空中平台自然能在更广阔的视野中同时观测到多个道路使用者，因此每辆车都可以成为具有真实驾驶行为的数据源，有效扩大了监督的规模。基于137小时的空中交通监测录像，我们提取了65万个驾驶样本，并构建了用于轨迹规划器和运动预测器的基准。对多个模型的零样本实验显示存在显著的跨城市领域差距，但其中许多差距可以通过来自空中的有限监督缓解，例如每个地点30分钟的监测。我们的发现表明，空中交通监测是适配新城市自动驾驶系统的一种高效且可扩展的数据源，数据和代码将公开提供。

英文摘要

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 new environment. While vehicle-based data collection logs the ego and its surroundings, an aerial platform naturally observes many road users simultaneously over an extended field of view. As a result, every vehicle can be a data source with grounded driving behavior, effectively scaling up the amount of supervision. Based on 137 hours of aerial traffic monitoring footage, we extract 650K driving samples and construct a benchmark for trajectory planners and motion predictors. Zero-shot experiments with multiple models reveal significant cross-city domain gaps, but many of them can be alleviated by limited supervision from the sky, e.g., 30 minutes of monitoring per location. Our findings show that aerial traffic monitoring is an efficient and scalable data source for adapting autonomous driving systems in new cities. Data and code will be made publicly available.

发表机构

• École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）

机构由 AI 辅助整理，请以论文原文为准。