Trajectory Planning of Automated Vehicles Using Real-Time Map Updates

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dc.contributor.authorSzántó, Mátyás
dc.contributor.authorHidalgo, Carlos
dc.contributor.authorGonzález, Leonardo
dc.contributor.authorRastelli, Joshué Pérez
dc.contributor.authorAsua, Estibaliz
dc.contributor.authorVajta, László
dc.contributor.institutionCCAM
dc.date.accessioned2024-09-10T11:40:08Z
dc.date.available2024-09-10T11:40:08Z
dc.date.issued2023
dc.descriptionPublisher Copyright: © 2013 IEEE.
dc.description.abstractThe development of connected and automated vehicles (CAVs) presents a great opportunity to extend the current range of vehicle vision, by gathering information outside of its sensors. Two main sources could be aggregated for this extended perception; vehicles making use of vehicle-to-vehicle communication (V2V), and infrastructure using vehicle-to-infrastructure communication (V2I). In this paper, we focus on the infrastructure side and make the case for low-latency obstacle mapping using V2I communication. A map management framework is proposed, which allows vehicles to broadcast and subscribe to traffic information-related messages using the Message Queuing Telemetry Transport (MQTT) protocol. This framework makes use of our novel candidate/employed map (C/EM) model for the real-time updating of obstacles broadcast by individual vehicles. This solution has been implemented and tested using a scenario that contains real and simulated CAVs tasked with doing lane change and braking maneuvers. As a result, the simulated vehicle can optimize its trajectory planning based on information which could not be observed by its sensor suite but is instead received from the presented map-management module, while remaining capable of performing the maneuvers in an automated manner.en
dc.description.sponsorshipThis work was supported by the European Commission Horizon 2020 (H2020) Program under the Security By Design IoT Development and Certificate Framework with Front-end Access Control (IoTAC) Research and Innovation Action under Grant 952684.
dc.description.statusPeer reviewed
dc.format.extent14
dc.identifier.citationSzántó , M , Hidalgo , C , González , L , Rastelli , J P , Asua , E & Vajta , L 2023 , ' Trajectory Planning of Automated Vehicles Using Real-Time Map Updates ' , IEEE Access , vol. 11 , pp. 67468-67481 . https://doi.org/10.1109/ACCESS.2023.3291350
dc.identifier.doi10.1109/ACCESS.2023.3291350
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/11556/5041
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85164445429&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofIEEE Access
dc.relation.projectIDEuropean Commission Horizon 2020
dc.relation.projectIDResearch and Innovation Action, 952684
dc.relation.projectIDHorizon 2020 Framework Programme, H2020
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsConnected and automated vehicles (CAVs)
dc.subject.keywordsdynamic obstacle mapping
dc.subject.keywordsexternal perception
dc.subject.keywordsobject avoidance
dc.subject.keywordsreal-time trajectory planning
dc.subject.keywordsvehicle-to-network communication (V2N)
dc.subject.keywordsGeneral Computer Science
dc.subject.keywordsGeneral Materials Science
dc.subject.keywordsGeneral Engineering
dc.titleTrajectory Planning of Automated Vehicles Using Real-Time Map Updatesen
dc.typejournal article
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