Considering knowledge gaps for automated driving in conventional traffic

Simeon Calvert, Isabel Wilmink, AMG Soekroella, Bart van Arem

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientific

Abstract

With increasing numbers of vehicles using low level automation and higher level automation expected in the future, significant effects are expected on traffic flow. Despite much simulation and driving simulator research on SAE level 1 vehicles, there remain many questions in regard to the effects of the systems on traffic flow. The effects of higher levels of automation are even more difficult to estimate, as these vehicles are not even present on roads at this time, let alone in sufficient numbers to analyze. In this research, we propose a methodology for a-priori analysis of potential conflictsituations: Method for Explorative TRaffic scenario Observation and Analysis (METRO-A). It is applied to the case of automated driving in conventional traffic to analyzepotential difficulties that SAE level 3and 4and higher vehicles may encounterin mixed traffic conditions, for a weaving section case-study. Furthermore, a set of important research questions are constructed that are relevant forthe automotive industry, and road agencies and authorities.
Original languageEnglish
Title of host publicationProceedings of the Fourth International Conference on Advances in Civil, Structural and Environmental Engineering -ACSEE 2016
PublisherSEEK digital library
Pages102-111
ISBN (Electronic)978-1-63248-114-6
DOIs
Publication statusPublished - 16 Dec 2016
Event4th International Conference on Advances in Civil, Structural and Environmental Engineering - Rome, Italy
Duration: 15 Dec 201616 Dec 2016
Conference number: 4

Conference

Conference4th International Conference on Advances in Civil, Structural and Environmental Engineering
Abbreviated titleACSEE 2016
Country/TerritoryItaly
CityRome
Period15/12/1616/12/16

Keywords

  • Automated driving
  • Traffic flow
  • Vehicle automation

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  • Best paper award ACSEE 2016

    Calvert, S.C. (Recipient), 16 Dec 2016

    Prize: Prize (including medals and awards)

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