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  • 标题:Distributed Model Predictive Control for Intersection Automation Using a Parallelized Optimization Approach
  • 本地全文:下载
  • 作者:Alexander Katriniok ; Peter Kleibaum ; Martina Joševski
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2017
  • 卷号:50
  • 期号:1
  • 页码:5940-5946
  • DOI:10.1016/j.ifacol.2017.08.1492
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractRoad intersections are usually a bottleneck in big cities and might lead to severe congestion during rush hour traffic. With highly automated vehicles leveraging vehicle-to-vehicle communication, traffic lights and signs might become dispensable one day. For this particular scenario, we propose a distributed model predictive control approach which enables multiple vehicles to pass the intersection simultaneously in a safe and efficient way. The resulting non-convex distributed control problem is solved in parallel without any nested iterations which is achieved by applying constraint prioritization. Constraint prioritization introduces priorities on vehicles such that a vehicle with lower priority has to give right of way in case of a potential conflict without prescribing an intersection passing order. To cope with non-convex safety constraints, we apply a semidefinite programming relaxation with randomization to determine appropriate feasible solutions. Simulation results finally prove the efficacy of our approach.
  • 关键词:KeywordsDistributed controlpredictive controldistributed optimizationautomotive controlintelligent transportation systemsautonomous vehicles
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