论文标题
GameOpt：最佳的实时多代理计划和动态交集的控制
GAMEOPT: Optimal Real-time Multi-Agent Planning and Control for Dynamic Intersections
论文作者
论文摘要
我们提出了GameOpt：一种新型的混合方法，用于用于动态，多车道，未信号交叉点的合作交集控制。安全地导航这些复杂而易于事故的交叉点需要同时进行驾驶员之间的轨迹计划和谈判。 GameOpt是一种混合公式，首先使用拍卖机制为每个代理生成优先入口序列，然后是基于优化的轨迹计划器，该计划器计算满足优先级序列的速度控制。这种耦合的实时速度在高密度流量的高度速度低于10,000辆/小时的高密度流量下运行，比其他基于完全优化的方法快100倍，同时提供了公平，安全性和效率方面的保证。在SUMO模拟器测试后，我们的算法将吞吐量提高了至少25％，与使用交通信号灯和停车标志的基于拍卖的方法和信号的方法相比，花时间达到了75％，燃料消耗增加了33％。
We propose GameOpt: a novel hybrid approach to cooperative intersection control for dynamic, multi-lane, unsignalized intersections. Safely navigating these complex and accident prone intersections requires simultaneous trajectory planning and negotiation among drivers. GameOpt is a hybrid formulation that first uses an auction mechanism to generate a priority entrance sequence for every agent, followed by an optimization-based trajectory planner that computes velocity controls that satisfy the priority sequence. This coupling operates at real-time speeds of less than 10 milliseconds in high density traffic of more than 10,000 vehicles/hr, 100 times faster than other fully optimization-based methods, while providing guarantees in terms of fairness, safety, and efficiency. Tested on the SUMO simulator, our algorithm improves throughput by at least 25%, time taken to reach the goal by 75%, and fuel consumption by 33% compared to auction-based approaches and signaled approaches using traffic-lights and stop signs.
