Automated quantification of occupant posture and shoulder belt fit using safety specific key points

  • Virtual evaluation of automotive safety with variation in occupant posture and shoulder belt fit is gaining importance, and there is a need of methods facilitating analysis of occupant postures in driving studies. This study is aimed to develop an AI-based computer vision method to automatically quantify occupant posture and shoulder belt position over time in a car. Traceable defined key points on the occupant were related with the shoulder belt and quantified over time in real 3D coordinates by predefined key measurements, utilising the underlying spatial information of a Intel RealSense 3D Camera. The key points are defined as traceable key points relevant to relate the occupant to the vehicle environment and to estimate shoulder belt position. Key point prediction results suggest an average deviation of around 1cm per coordinate, which enable a reliable spatial categorization of the respective tracked occupant by analyzing the key measurements. This method providing continuousVirtual evaluation of automotive safety with variation in occupant posture and shoulder belt fit is gaining importance, and there is a need of methods facilitating analysis of occupant postures in driving studies. This study is aimed to develop an AI-based computer vision method to automatically quantify occupant posture and shoulder belt position over time in a car. Traceable defined key points on the occupant were related with the shoulder belt and quantified over time in real 3D coordinates by predefined key measurements, utilising the underlying spatial information of a Intel RealSense 3D Camera. The key points are defined as traceable key points relevant to relate the occupant to the vehicle environment and to estimate shoulder belt position. Key point prediction results suggest an average deviation of around 1cm per coordinate, which enable a reliable spatial categorization of the respective tracked occupant by analyzing the key measurements. This method providing continuous information of the occupant position and belt fit will be useful to identify common occupant postures as well as more extreme postures, to be used for expanding variations in postures for vehicle safety assessments.show moreshow less

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Metadaten
Author:Franz HartleitnerORCiD, A. Koppisetty, K. Bohman
URN:urn:nbn:de:bvb:384-opus4-1155566
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/115556
ISSN:2687-7813OPAC
Parent Title (English):IEEE Open Journal of Intelligent Transportation Systems
Publisher:Institute of Electrical and Electronics Engineers (IEEE)
Type:Article
Language:English
Year of first Publication:2022
Publishing Institution:Universität Augsburg
Release Date:2024/09/24
Volume:3
First Page:89
Last Page:103
DOI:https://doi.org/10.1109/ojits.2022.3140612
Institutes:Mathematisch-Naturwissenschaftlich-Technische Fakultät
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Mathematik
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Mathematik / Lehrstuhl für Rechnerorientierte Statistik und Datenanalyse
Nachhaltigkeitsziele
Nachhaltigkeitsziele / Ziel 3 - Gesundheit und Wohlergehen
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung