论文标题
分散的车辆协调：伯克利DeepDrive无人机数据集和基于共识的模型
Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset and Consensus-Based Models
论文作者
论文摘要
很大一部分道路，尤其是在人口稠密的发展中国家中，缺乏明确定义的通行权规则。这些未结构化的道路对自动驾驶汽车运动计划构成了重大挑战，在这种情况下，有效且安全的导航依赖于理解分散的人类协调以避免碰撞。由于开源经验数据有限和合适的建模框架，这种经常称为“社交驾驶礼节”的协调仍然没有被忽视。在本文中，我们提出了一个新颖的数据集和建模框架，旨在在这些未建筑环境中研究运动计划。该数据集包括20个代表性方案的航空视频，用于训练车辆检测模型的图像数据集以及用于车辆轨迹估算的开发套件。我们证明，基于共识的建模方法可以有效地解释我们数据集中观察到的优先顺序的出现，因此是分散式避免碰撞计划的可行框架。
A significant portion of roads, particularly in densely populated developing countries, lacks explicitly defined right-of-way rules. These understructured roads pose substantial challenges for autonomous vehicle motion planning, where efficient and safe navigation relies on understanding decentralized human coordination for collision avoidance. This coordination, often termed "social driving etiquette," remains underexplored due to limited open-source empirical data and suitable modeling frameworks. In this paper, we present a novel dataset and modeling framework designed to study motion planning in these understructured environments. The dataset includes 20 aerial videos of representative scenarios, an image dataset for training vehicle detection models, and a development kit for vehicle trajectory estimation. We demonstrate that a consensus-based modeling approach can effectively explain the emergence of priority orders observed in our dataset, and is therefore a viable framework for decentralized collision avoidance planning.
