论文标题
使用分散的游戏理论计划在回旋处的多车辆控制
Multi-Vehicle Control in Roundabouts using Decentralized Game-Theoretic Planning
论文作者
论文摘要
在密集的城市驾驶环境中安全导航仍然是一个空旷的问题和一个积极的研究领域。与典型的预测 - 平面方法不同，游戏理论计划考虑了一辆车辆的计划将如何影响另一个车辆的行为。最近的工作表明，在具有非线性目标和约束的通用和游戏中找到本地NASH均衡所需的时间有了显着改善。当琐碎地应用驾驶时，这些作品假设场景中的所有车辆一起玩游戏，这可能会导致棘手的计算时间，以实现密集的流量。我们通过假设代理只在其观察附近玩游戏来制定一种分散的游戏理论计划方法，我们认为这是人类驾驶的更合理的假设。与互动图的所有牢固连接组件并行播放游戏，大大减少了每个游戏中的玩家和约束数量，从而大大减少了计划的时间。我们证明，我们的方法可以通过比较智能驱动程序模型的改编和集中的游戏理论计划在交互数据集中的回旋处进行比较，可以在城市环境中实现无冲突，有效的驾驶。我们的实施可从http://github.com/sisl/decnashplanning获得。
Safe navigation in dense, urban driving environments remains an open problem and an active area of research. Unlike typical predict-then-plan approaches, game-theoretic planning considers how one vehicle's plan will affect the actions of another. Recent work has demonstrated significant improvements in the time required to find local Nash equilibria in general-sum games with nonlinear objectives and constraints. When applied trivially to driving, these works assume all vehicles in a scene play a game together, which can result in intractable computation times for dense traffic. We formulate a decentralized approach to game-theoretic planning by assuming that agents only play games within their observational vicinity, which we believe to be a more reasonable assumption for human driving. Games are played in parallel for all strongly connected components of an interaction graph, significantly reducing the number of players and constraints in each game, and therefore the time required for planning. We demonstrate that our approach can achieve collision-free, efficient driving in urban environments by comparing performance against an adaptation of the Intelligent Driver Model and centralized game-theoretic planning when navigating roundabouts in the INTERACTION dataset. Our implementation is available at http://github.com/sisl/DecNashPlanning.
