Validating multi-sensor object tracking in Heavy-Duty Trucks with extended trailer dynamics for road traffic situations - UTU Research Portal

A4 Refereed article in a conference publication

Validating multi-sensor object tracking in Heavy-Duty Trucks with extended trailer dynamics for road traffic situations

Authors: Adu-Kyere, Akwasi; Nigussie, Ethiopia; Isoaho, Jouni; Ronkainen, Jukka; Kyytinen, Arto

Editors: Shakshuki, Elhadi

Conference name: International Conference on Ambient Systems, Networks and Technologies Networks

Publication year: 2024

Journal: Procedia Computer Science

Book title : The 15th International Conference on Ambient Systems, Networks and Technologies Networks (ANT) / The 7th International Conference on Emerging Data and Industry 4.0 (EDI40), April 23-25, 2024, Hasselt University, Belgium

Journal name in source: Procedia Computer Science

Volume: 238

First page : 167

Last page: 174

eISSN: 1877-0509

DOI: https://doi.org/10.1016/j.procs.2024.06.012

Web address : https://doi.org/10.1016/j.procs.2024.06.012

Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/457187506

Abstract

Multi-sensing technologies in vehicles have the potential to improve road and pedestrian safety. Freighting trucks with extended trailer face challenges in urban maneuvering due to limited visibility. In this paper, we evaluate and validate extended trailer dynamics for Heavy-Duty Trucks in road traffic situations with multi-sensor object tracking emphasizing trailer dynamics, back-end parameters, and characteristics that influence decision-making strategies. A custom in-vehicle compute unit, in combination with the onboard truck’s computer, facilitated this research.

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Funding information in the publication:
This research was performed within the “Programmable Systems for Intelligence in Auto- mobiles” (PRYSTINE) project. PRYSTINE has received funding within the Electronic Components and Systems for European Leadership Joint Undertaking (ECSEL JU) in collaboration with the European Union’s H2020 Framework Programme and National Authorities, under Grant No. 783190.