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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

20302 - Applied mechanics

Project

<a href="/en/project/EF17_048%2F0007280" target="_blank" >EF17_048/0007280: Application of Modern Technologies in Medicine and Industry</a><br>

Continuities

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

Publication year

2023

Confidentiality

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

Article name in the collection

World Congress Experience, WCX 2023

ISBN

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ISSN

0148-7191

e-ISSN

2688-3627

Number of pages

9

Pages from-to

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Publisher name

SAE Technical Papers

Place of publication

Detroit

Event location

Detroit

Event date

Apr 18, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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