Inferenz von Kreuzungsinformationen aus Flottendaten

By: Material type: ArticleArticleLanguage: German Publication details: KIT Scientific Publishing 2017Description: 1 electronic resource (XIX, 171 p. p.)ISBN:
  • KSP/1000073704
  • 9783731507215
Subject(s): Online resources: Summary: The next generation of driver assistance systems and highly automated driving functions are based on digital maps. In order to meet the high requirements on the correctness and up-to-dateness of this information, this work presents new automated methods to extract up-to-date map information from fleet data. The focus is on the inference of static intersection information from fleet data through machine learning and statistical methods.
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The next generation of driver assistance systems and highly automated driving functions are based on digital maps. In order to meet the high requirements on the correctness and up-to-dateness of this information, this work presents new automated methods to extract up-to-date map information from fleet data. The focus is on the inference of static intersection information from fleet data through machine learning and statistical methods.

Creative Commons https://creativecommons.org/licenses/by-sa/4.0/ cc https://creativecommons.org/licenses/by-sa/4.0/

German

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