Vehicle crash simulation models for reinforcement learning driven crash-detection algorithm calibration

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F25%3A43976029" target="_blank" >RIV/49777513:23520/25:43976029 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1186/s40323-025-00288-4" target="_blank" >https://doi.org/10.1186/s40323-025-00288-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s40323-025-00288-4" target="_blank" >10.1186/s40323-025-00288-4</a>

Result language

angličtina

Original language name

Vehicle crash simulation models for reinforcement learning driven crash-detection algorithm calibration

Original language description

The development of finite element vehicle models for crash simulations is a highly complex task. The main aim of these models is to simulate a variety of crash scenarios and assess all the safety systems for their respective performances. These vehicle models possess a substantial amount of data pertaining to the vehicle’s geometry, structure, materials, etc., and are used to estimate a large set of system and component level characteristics using crash simulations. It is understood that even the most well-developed simulation models are prone to deviations in estimation when compared to real-world physical test results. This is generally due to our inability to model the chaos and uncertainties introduced in the real world. Such unavoidable deviations render the use of virtual simulations ineffective for the calibration process of the algorithms that activate the restraint systems in the event of a crash (crash-detection algorithm). In the scope of this research, authors hypothesize the possibility of accounting for such variations introduced in the real world by creating a feedback loop between real-world crash tests and crash simulations. To accomplish this, a Reinforcement Learning (RL) compatible virtual surrogate model is used, which isadapted from crash simulation models. Hence, a conceptual methodology is illustrated in this paper for developing an RL-compatible model that can be trained using the results of crash simulations and crash tests. As the calibration of the crash-detection algorithm is fundamentally dependent upon the crash pulses, the scope of the expected output is limited to advancing the ability to estimate crash pulses. Furthermore, the real-time

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20302 - Applied mechanics

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

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

Name of the periodical

ADVANCED MODELING AND SIMULATION IN ENGINEERING SCIENCES

ISSN

2213-7467

e-ISSN

2213-7467

Volume of the periodical

12

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

27

Pages from-to

nestránkováno

UT code for WoS article

001529233600001

EID of the result in the Scopus database

2-s2.0-105010615708