论文标题
与深度学习模型的交叉口协调骑士群
Coordinating CAV Swarms at Intersections with a Deep Learning Model
论文作者
论文摘要
连接和自动化的车辆（CAVS）被视为一种特殊的机器人，有可能显着提高交通的安全性和效率。与许多通过使用少量机器人在实验室中证明的许多群体机器人研究相反，CAV研究旨在实现不断的机器人群流的合作驾驶。但是，即使对于无信号的交叉路口，如何获得此类机器人群流的最佳传递顺序也是一个NP - 硬性问题（具体来说，基于枚举的算法需要几天的时间才能找到20-CAV场景的最佳解决方案）。在这里，我们介绍了一种新颖的合作驾驶算法（Alphaorder），该算法结合了离线深度学习和在线树搜索，以实时找到近乎最佳的通过订单。 Alphaorder从解决方案中构建了指针网络模型，并为新方案即时生成了近乎最佳的传递订单。此外，我们的方法提供了一种一般的方法来管理群体机器人技术之间的先发资源共享（例如，在相互冲突的地区安排多个自动化的引导车辆（AGV）和无人驾驶汽车（UAV）
Connected and automated vehicles (CAVs) are viewed as a special kind of robots that have the potential to significantly improve the safety and efficiency of traffic. In contrast to many swarm robotics studies that are demonstrated in labs by employing a small number of robots, CAV studies aims to achieve cooperative driving of unceasing robot swarm flows. However, how to get the optimal passing order of such robot swarm flows even for a signal-free intersection is an NP-hard problem (specifically, enumerating based algorithm takes days to find the optimal solution to a 20-CAV scenario). Here, we introduce a novel cooperative driving algorithm (AlphaOrder) that combines offline deep learning and online tree searching to find a near-optimal passing order in real-time. AlphaOrder builds a pointer network model from solved scenarios and generates near-optimal passing orders instantaneously for new scenarios. Furthermore, our approach provides a general approach to managing preemptive resource sharing between swarm robotics (e.g., scheduling multiple automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) at conflicting areas
