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Action intention recognition of cars and bicycles in intersections
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-1043-8773
2020 (English)In: International Journal of Vehicle Design, ISSN 0143-3369, E-ISSN 1741-5314, Vol. 83, no 2-4, p. 103-121Article in journal (Refereed) Published
Abstract [en]

Copyright © 2020 Inderscience Enterprises Ltd.Action intention recognition is becoming increasingly important in the road vehicle automation domain. Autonomous vehicles must be aware of their surroundings if we are to build safe and efficient transport systems. This paper presents a method for predicting the action intentions of road users based on sensors in the road infrastructure. The scenarios used for demonstration are recorded on two different public road sections. The first scenario includes bicyclists and the second includes cars that are driving in a road approaching an intersection where they are either leaving or continuing straight. A 3D camera-based data acquisition system is used to collect trajectories of the road users that are used as input for models trained to predict the action intention of the road users. The proposed system enables future connected and automated vehicles to receive collision warnings from an infrastructure-based sensor system well in advance to enable better planning.

Place, publisher, year, edition, pages
Inderscience Publishers , 2020. Vol. 83, no 2-4, p. 103-121
Keywords [en]
Data mining, Intention recognition, Random forest, Traffic behaviour modeling, Variable selection
National Category
Vehicle Engineering
Identifiers
URN: urn:nbn:se:hh:diva-44659DOI: 10.1504/IJVD.2020.115056ISI: 000652649400002Scopus ID: 2-s2.0-85106175015OAI: oai:DiVA.org:hh-44659DiVA, id: diva2:1564087
Available from: 2021-06-11 Created: 2021-06-11 Last updated: 2021-10-20Bibliographically approved

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Englund, Cristofer

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CiteExportLink to record
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  • apa
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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