Decentralized multiagent planning has been an important field of research in robotics. An interesting and impactful application in the field is decentralized vehicle coordination in understructured road environments. For example, in an intersection, it is useful yet difficult to deconflict multiple vehicles of intersecting paths in absence of a central coordinator. We learn from common sense that, for a vehicle to navigate through such understructured environments, the driver must understand and conform to the implicit "social etiquette" observed by nearby drivers. To study this implicit driving protocol, we collect the Berkeley DeepDrive Drone dataset. The dataset contains 1) a set of aerial videos recording understructured driving, 2) a collection of images and annotations to train vehicle detection models, and 3) a kit of development scripts for illustrating typical usages. We believe that the dataset is of primary interest for studying decentralized multiagent planning employed by human drivers and, of secondary interest, for computer vision in remote sensing settings.
翻译:分散式多试剂规划是机器人研究的一个重要领域,该领域一个有趣的、影响深远的应用是分散式车辆在结构不足的道路环境中的协调。例如,在一个交叉点,在没有中央协调员的情况下,要消除交错路径的多重车辆冲突是有用的,但我们从常识中了解到,对于在这种结构不足的环境中航行的车辆来说,驾驶员必须理解和遵守附近驾驶员所观察到的隐含的“社会礼仪”。为了研究这一隐含的驾驶规程,我们收集了Berke DeepDrive Drone数据集。数据集包含:(1) 一套空中录像记录结构化驾驶不足的录像,(2) 一套图像和说明,用于培训车辆探测模型,(3) 一套用于说明典型用途的发展脚本。我们认为,数据集对于研究人类驾驶员使用的分散式多试剂规划,以及具有次要兴趣的计算机在遥感环境中的视觉,具有首要意义。