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Predicting Agent Behaviour and State for Applications in a Roundabout-Scenario Autonomous Driving
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0001-5965-1965
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 19, article id 4279Article in journal (Refereed) Published
Abstract [en]

As human drivers, we instinctively employ our understanding of other road users' behaviour for enhanced efficiency of our drive and safety of the traffic. In recent years, different aspects of assisted and autonomous driving have gotten a lot of attention from the research and industrial community, including the aspects of behaviour modelling and prediction of future state. In this paper, we address the problem of modelling and predicting agent behaviour and state in a roundabout traffic scenario. We present three ways of modelling traffic in a roundabout based on: (i) the roundabout geometry; (ii) mean path taken by vehicles inside the roundabout; and (iii) a set of reference trajectories traversed by vehicles inside the roundabout. The roundabout models are compared in terms of exit-direction classification and state (i.e., position inside the roundabout) prediction of query vehicles inside the roundabout. The exit-direction classification and state prediction are based on a particle-filter classifier algorithm. The results show that the roundabout model based on set of reference trajectories is better suited for both the exit-direction and state prediction.

Place, publisher, year, edition, pages
Basel: MDPI, 2019. Vol. 19, no 19, article id 4279
Keywords [en]
Behaviour modeling, Roundabout
National Category
Signal Processing Robotics
Identifiers
URN: urn:nbn:se:hh:diva-40662DOI: 10.3390/s19194279OAI: oai:DiVA.org:hh-40662DiVA, id: diva2:1357702
Funder
Knowledge FoundationAvailable from: 2019-10-04 Created: 2019-10-04 Last updated: 2019-10-08Bibliographically approved

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