Generating Synthetic Vehicle Speed Records Using LSTM

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F23%3A00011353" target="_blank" >RIV/46747885:24220/23:00011353 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-34111-3_12" target="_blank" >https://doi.org/10.1007/978-3-031-34111-3_12</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-34111-3_12" target="_blank" >10.1007/978-3-031-34111-3_12</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Generating Synthetic Vehicle Speed Records Using LSTM

Popis výsledku v původním jazyce

Quality assurance testing of automotive electronic components such as navigation or infotainment displays requires data from genuine car rides. However, traditional static on-site testing methods are time-consuming and costly. To address this issue, we present a novel approach to generating synthetic ride data using Bidirectional LSTM, which offers a faster, more flexible, and environmentally friendly testing process. In this paper, we demonstrate the effectiveness of our approach by generating synthetic vehicle speed along a given route and evaluating the fidelity of the generated output using objective and subjective methods. Our results show that our approach achieves high levels of fidelity and offers a promising solution for quality assurance testing in the automotive industry. This work contributes to the growing research on generative machine learning models and their potential applications in the automotive industry.

Název v anglickém jazyce

Generating Synthetic Vehicle Speed Records Using LSTM

Popis výsledku anglicky

Quality assurance testing of automotive electronic components such as navigation or infotainment displays requires data from genuine car rides. However, traditional static on-site testing methods are time-consuming and costly. To address this issue, we present a novel approach to generating synthetic ride data using Bidirectional LSTM, which offers a faster, more flexible, and environmentally friendly testing process. In this paper, we demonstrate the effectiveness of our approach by generating synthetic vehicle speed along a given route and evaluating the fidelity of the generated output using objective and subjective methods. Our results show that our approach achieves high levels of fidelity and offers a promising solution for quality assurance testing in the automotive industry. This work contributes to the growing research on generative machine learning models and their potential applications in the automotive industry.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/CK01000020" target="_blank" >CK01000020: Vývoj generátoru tras GNSS a signálu CANBUS pomocí strojového učení s využitím Software Defined Radio</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

IFIP Advances in Information and Communication Technology

ISBN

978-3-031-34110-6

ISSN

18684238

e-ISSN

—

Počet stran výsledku

12

Strana od-do

125-136

Název nakladatele

Springer Nature Switzerland

Místo vydání

—

Místo konání akce

León

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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