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  • 标题:Comparing Collision Threat Measures for Verification of Autonomous Vehicles using Extreme Value Theory
  • 本地全文:下载
  • 作者:Daniel Åsljung ; Jonas Nilsson ; Jonas Fredriksson
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2016
  • 卷号:49
  • 期号:15
  • 页码:57-62
  • DOI:10.1016/j.ifacol.2016.07.709
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractThe verification of safety is expected to be one of the largest challenges in the commercialization of autonomous vehicles. Using traditional methods would require infeasible time and resources. Recent research has shown the possibility of using near-collisions in order to estimate the frequency of actual collisions using Extreme Value Theory. However, little research has been done on how the measure for determining the closeness to a collision affect the result of the estimation. This paper compares a collision-based measure against one that relates to an inevitable collision state. The result shows that using inevitable collision states is more robust and that more research needs to be made into measures of collision proximity.
  • 关键词:KeywordsAutomotiveAutonomous vehiclesSafetyStatistical inferenceVerification & Validation
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