文章基本信息

标题：Nonconvex Consensus ADMM for Cooperative Lane Change Maneuvers of Connected Automated Vehicles
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作者：Alexander Katriniok
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：14336-14343
DOI：10.1016/j.ifacol.2020.12.1379
语种：English
出版社：Elsevier
摘要：AbstractConnected and automated vehicles (CAVs) offer huge potential to improve the performance of automated vehicles (AVs) without communication capabilities, especially in situations when the vehicles (or agents) need to be cooperative to accomplish their maneuver. Lane change maneuvers in dense traffic, e.g., are very challenging for non-connected AVs. To alleviate this problem, we propose a holistic distributed lane change control scheme for CAVs which relies on vehicle-to-vehicle communication. The originally centralized optimal control problem is embedded into a consensus-based Alternating Direction Method of Multipliers framework to solve it in a distributed receding horizon fashion. Although agent dynamics render the underlying optimal control problem nonconvex, we propose a problem reformulation that allows to derive convergence guarantees. In the distributed setting, every agent needs to solve a nonlinear program (NLP) locally. To obtain a real-time solution of the local NLPs, we utilize the optimization engine OpEn which implements the proximal averaged Newton method for optimal control (PANOC). Simulation results prove the efficacy and real-time capability of our approach.
关键词：KeywordsDistributed controlestimationModel predictiveoptimization-based controlReal time optimizationcontrolAutonomous VehiclesMulti-vehicle systems