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Human-Aided Trajectory Planning for Automated Vehicles Through Teleoperation and Arbitration Graphs

Le Large, Nick ORCID iD icon 1; Brecht, David 1; Poh, Willi ORCID iD icon 1; Pauls, Jan-Hendrik ORCID iD icon 1; Lauer, Martin ORCID iD icon 1; Diermeyer, Frank
1 Institut für Mess- und Regelungstechnik (MRT), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Teleoperation enables remote human support of automated vehicles in scenarios where the automation is not able to find an appropriate solution. Remote assistance concepts, where operators provide discrete inputs to aid specific automation modules like planning, is gaining interest due to its reduced workload on the human remote operator and improved safety. However, these concepts are challenging to implement and maintain due to their deep integration and interaction with the automated driving system. In this paper, we propose a solution to facilitate the implementation of remote assistance concepts that intervene on planning level and extend the operational design domain of the vehicle at runtime. Using arbitration graphs, a modular decision-making framework, we integrate remote assistance into an existing automated driving system without modifying the original software components.
Our simulative implementation demonstrates this approach in two use cases, allowing operators to adjust planner constraints and enable trajectory generation beyond nominal operational design domains.


Zugehörige Institution(en) am KIT Institut für Mess- und Regelungstechnik (MRT)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 22.06.2025
Sprache Englisch
Identifikator ISBN: 979-8-3315-3804-0
KITopen-ID: 1000187278
Erschienen in 2025 IEEE Intelligent Vehicles Symposium (IV), Cluj-Napoca, Romania, 22-25 June 2025
Veranstaltung 36th IEEE Intelligent Vehicles Symposium (IV 2025), Klausenburg, Rumänien, 22.06.2025 – 25.06.2025
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 598–604
Projektinformation AUTOtech.agil (BMFTR, 01IS22088T)
Nachgewiesen in OpenAlex
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