Modelling and experimental study for automated congestion driving

Joseph Urhahne*, Patrick Piastowski, Mascha C. van der Voort

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

Taking a collaborative approach in automated congestion driving with a Traffic Jam Assist system requires the driver to take over control in certain traffic situations. In order to warn the driver appropriately, warnings are issued (“pay attention” vs. “take action”) due to a control transition strategy that reacts to lane change manoeuvres by surrounding traffic. This paper presents the outcome of a driving simulator study regarding the evaluation of a control transition strategy. The strategy was found to provide adequate support to drivers. However, driver acceptance can be increased. A refined model is proposed.
Original languageEnglish
Title of host publicationAdvances in Visual Computing
Subtitle of host publication11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings
EditorsGeorge Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Ioannis Pavlidis, Rogerio Feris, Tim McGraw, Mark Elendt, Regis Kopper, Eric Ragan, Zhao Ye, Gunther Weber
PublisherSpringer
Pages784-794
Number of pages11
Volume1
ISBN (Electronic)978-3-319-27857-5
ISBN (Print)978-3-319-27856-8
DOIs
Publication statusPublished - 2015
Event11th International Symposium on Visual Computing, ISVC 2015 - Las Vegas, United States
Duration: 14 Dec 201516 Dec 2015
Conference number: 11

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume9474
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Symposium on Visual Computing, ISVC 2015
Abbreviated titleISVC
CountryUnited States
CityLas Vegas
Period14/12/1516/12/15

Keywords

  • Hybrid systems
  • Medical imaging
  • Optimization
  • Pattern recognition
  • Automated image analysis
  • Computer graphics
  • Computer vision
  • Health care
  • Image processing
  • Machine learning
  • Magnetic resonance imaging
  • Medical image software
  • Multi-agent
  • Ontology
  • Simulated annealing
  • SPARQL
  • Video games
  • Virtual reality
  • Warehouse network modeling

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