文章基本信息

标题：Information and Collaboration Levels in Vehicular Strings: A Comparative Study ⁎
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作者：R. Austin Dollar ; Antonio Sciarretta ; Ardalan Vahidi 等
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：13822-13829
DOI：10.1016/j.ifacol.2020.12.892
语种：English
出版社：Elsevier
摘要：AbstractThe potential safety, productivity, and energy benefits of automated vehicles have driven a surge of research interest in their algorithms. Even within single-lane driving, control engineers now have a profusion of approaches available to them. Algorithm classes include classical controllers, receding horizon controllers, and constrained eco-driving formulae based on Pontryagin’s Minimum Principle. Differing connectivity architectures and collaboration levels further differentiate algorithms from one another. This study evaluated six controllers in two drive cycle-based scenarios using an electric powertrain model for energy analysis. Individual-vehicle and string performance were examined, including string stability and length. Algorithms with greater access to information generally performed best. Although collaboration affected energy use only slightly, it made a greater impact on string length.
关键词：KeywordsMulti-vehicle systemstrajectorypath planningnonlinear and optimal automotive control