去中心化车辆协同：伯克利深度驾驶无人机数据集与基于共识的模型
Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset and Consensus-Based Models
AI总结：
针对非结构化道路缺乏明确路权规则导致自动驾驶运动规划困难的问题，本文提出新型数据集（含20段航拍视频等）和基于共识的建模框架，证明该框架可有效解释优先级顺序形成，为去中心化避撞规划提供可行方案。
摘要：
在人口稠密的发展中国家，很大一部分道路缺乏明确的路权规则定义。这些非结构化道路给自动驾驶车辆的运动规划带来了巨大挑战，而高效安全的导航依赖于对去中心化人类协同避撞行为的理解。这种常被称为“社交驾驶礼仪”的协同行为，因开源实证数据有限和合适建模框架缺失而未被充分探索。本文提出了一个用于研究非结构化环境中运动规划的新型数据集和建模框架：该数据集包含20段代表性场景的航拍视频、用于训练车辆检测模型的图像数据集，以及车辆轨迹估计开发工具包；我们证明基于共识的建模方法能有效解释数据集中观察到的优先级顺序的形成，因此是去中心化避撞规划的可行框架。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
