Injury Assessment in Non-Standard Seating Configurations in Highly Automated Vehicles Using Digital Twin and Active Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23640%2F23%3A43969059" target="_blank" >RIV/49777513:23640/23:43969059 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.4271/2023-01-0006" target="_blank" >https://doi.org/10.4271/2023-01-0006</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4271/2023-01-0006" target="_blank" >10.4271/2023-01-0006</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Injury Assessment in Non-Standard Seating Configurations in Highly Automated Vehicles Using Digital Twin and Active Learning

Popis výsledku v původním jazyce

Human-driven vehicles are going to be replaced by highly automated vehicles as one of the future mobility trends. Even though highly automated vehicles’ active safety systems can protect against vehicle-to-vehicle accidents, the traffic mix between human-driven vehicles and highly automated vehicles is still a potential source of vehicle collisions. Additionally, occupants in highly automated vehicles will be passengers not necessarily dealing with driving anymore, so there will be a considerable number of non-standard seating configurations. Those configurations are not able to be assessed for safety by hardware testing due to their number, variability and complexity. The objective of the paper is the development of a fast virtual approach to identify the passengers’ injury risk in non-standard seating configurations under multi-directional impact scenarios and severity. We deploy the concept of surrogate modeling, where we introduce a digital twin for the expected automated vehicle interiors. Non-standard seating configurations are represented by a simplified model of four seats located in the vehicle. These seats are occupied by a previously developed scalable human body model representing passengers of variable anthropometry. Thanks to the vehicle interior simplification and the hybrid human body model, thousands of simulations describing the impacts identified can be run. Based on the numerical simulations describing impact scenarios, a fast and lean artificial intelligence model actively learns a digital twin to approximate injury risk predictions for a huge number of possible crash scenarios fast.

Název v anglickém jazyce

Injury Assessment in Non-Standard Seating Configurations in Highly Automated Vehicles Using Digital Twin and Active Learning

Popis výsledku anglicky

Human-driven vehicles are going to be replaced by highly automated vehicles as one of the future mobility trends. Even though highly automated vehicles’ active safety systems can protect against vehicle-to-vehicle accidents, the traffic mix between human-driven vehicles and highly automated vehicles is still a potential source of vehicle collisions. Additionally, occupants in highly automated vehicles will be passengers not necessarily dealing with driving anymore, so there will be a considerable number of non-standard seating configurations. Those configurations are not able to be assessed for safety by hardware testing due to their number, variability and complexity. The objective of the paper is the development of a fast virtual approach to identify the passengers’ injury risk in non-standard seating configurations under multi-directional impact scenarios and severity. We deploy the concept of surrogate modeling, where we introduce a digital twin for the expected automated vehicle interiors. Non-standard seating configurations are represented by a simplified model of four seats located in the vehicle. These seats are occupied by a previously developed scalable human body model representing passengers of variable anthropometry. Thanks to the vehicle interior simplification and the hybrid human body model, thousands of simulations describing the impacts identified can be run. Based on the numerical simulations describing impact scenarios, a fast and lean artificial intelligence model actively learns a digital twin to approximate injury risk predictions for a huge number of possible crash scenarios fast.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20302 - Applied mechanics

Projekt

<a href="/cs/project/EF17_048%2F0007280" target="_blank" >EF17_048/0007280: Aplikace moderních technologií v medicíně a průmyslu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

World Congress Experience, WCX 2023

ISBN

—

ISSN

0148-7191

e-ISSN

2688-3627

Počet stran výsledku

9

Strana od-do

—

Název nakladatele

SAE Technical Papers

Místo vydání

Detroit

Místo konání akce

Detroit

Datum konání akce

18. 4. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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