文章基本信息

标题：Design of the optimal motions of autonomous vehicles in intersections through neural networks
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作者：Balázs Németh ; Péter Gáspár ; Dávid Szőcs 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：9
页码：19-24
DOI：10.1016/j.ifacol.2018.07.004
语种：English
出版社：Elsevier
摘要：AbstractThe handling of vehicle interactions is a challenge in the research into the traveling of autonomous vehicles. This paper focuses on collision-free motion design of autonomous vehicles to guarantee their minimum traveling time in intersections. First, a decision logic of the order of the vehicles in intersections is proposed. Based on the decision logic a constrained nonlinear optimization method is also proposed, with which the minimum traveling time of the vehicles without their collision is guaranteed. Since the on-line solution of the nonlinear optimization task can be numerically complex, a neural network based approximation of the optimal solution is developed. The efficiency of the method with various intersection scenarios is shown in the CarSim simulation environment.
关键词：Keywordsautonomous vehiclesneural networksintersectionsconstrained nonlinear optimization