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  • 标题:On-line Maneuver Identification in Highway Traffic Using Elastic Template Matching ⁎
  • 本地全文:下载
  • 作者:Pavlo Tkachenko ; Jinwei Zhou ; Davide Gagliardi
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2018
  • 卷号:51
  • 期号:15
  • 页码:557-562
  • DOI:10.1016/j.ifacol.2018.09.214
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractEarly identification and interpretation of traffic maneuvers will become key elements of modern driver assistance systems. Currently, only simple traffic situations can be detected by using adecision tree(DT) procedure in most cases. Even though this approach is straightforward, it still requires an expert to build a different DT for every maneuver. Moreover, for complex driving behaviours such simple logic identification does not work. In the present work we develop a template (or pattern) matching (TM) algorithm for maneuver recognition in highway traffic with a particular focus on acut-inmaneuver. By using maneuver patterns we are able to consider scenarios of arbitrary complexity and make the identification procedure more systematic and with minimal man expert involvement. The key element of the proposed TM algorithm is the settlement of a distance measure. We usedynamic time warping(DTW) method for an optimal non-linear alignment between two sequences under certain restrictions. It enables a correct assignment of the same maneuver but having different dynamics (speed, aggressiveness, etc.), and is robust to data with missing values. We illustrate the proposed method by cut-in detection from real traffic data and compare it to the standard DT identification.
  • 关键词:KeywordsIdentificationTemplate MatchingRoad TrafficManeuversADASDTWCut-in
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