We derive optimal control policies for a Connected Automated Vehicle (CAV) and cooperating neighboring CAVs to carry out a lane change maneuver consisting of a longitudinal phase where the CAV properly positions itself relative to the cooperating neighbors and a lateral phase where it safely changes lanes. In contrast to prior work on this problem, where the CAV "selfishly" only seeks to minimize its maneuver time, we seek to ensure that the fast-lane traffic flow is minimally disrupted (through a properly defined metric). Additionally, when performing lane-changing maneuvers, we optimally select the cooperating vehicles from a set of feasible neighboring vehicles and experimentally show that the highway throughput is improved compared to the baseline case of human-driven vehicles changing lanes with no cooperation. When feasible solutions do not exist for a given maximal allowable disruption, we include a time relaxation method trading off a longer maneuver time with reduced disruption. Our analysis is also extended to multiple sequential maneuvers. Simulation results show the effectiveness of our controllers in terms of safety guarantees and up to 16% and 90% average throughput and maneuver time improvement respectively when compared to maneuvers with no cooperation.
翻译:我们为连接式自动飞行器(CAV)和相邻合作的CAV制定了最佳控制政策,以实施由长视阶段组成的航道改变操作,即CAV相对于合作邻里适当定位,而横向阶段则安全改变航道。与以前就此问题开展的工作相比,CAV“自私地”只寻求尽量减少机动时间,我们力求确保快速车道交通流量受到最小干扰(通过适当界定的尺度)。此外,在进行更换车道时,我们从一套可行的邻里车辆中以最佳方式选择合作车辆,实验性地显示,与人类驱动的车辆在没有合作的情况下改变航道的基线案例相比,公路吞吐量有所改善。在没有可行办法解决特定最大允许干扰的情况下,我们采用时间松动方法交换时间,减少干扰。我们的分析还扩大到多个顺序操纵。模拟结果显示,与不合作的机动相比,我们的控制员在安全保障和平均达16%和90%的超速和超速改进时间的有效性。