Application of Artificial Neural Networks to Streamline the Process of Adaptive Cruise Control

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F29142890%3A_____%2F21%3A00041385" target="_blank" >RIV/29142890:_____/21:00041385 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2071-1050/13/8/4572/htm" target="_blank" >https://www.mdpi.com/2071-1050/13/8/4572/htm</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/su13084572" target="_blank" >10.3390/su13084572</a>

Result language

angličtina

Original language name

Application of Artificial Neural Networks to Streamline the Process of Adaptive Cruise Control

Original language description

This article deals with the use of neural networks for estimation of deceleration model parameters for the adaptive cruise control unit. The article describes the basic functionality of adaptive cruise control and creates a mathematical model of braking, which is one of the basic functions of adaptive cruise control. Furthermore, an analysis of the influences acting in the braking process is performed, the most significant of which are used in the design of deceleration prediction for the adaptive cruise control unit using neural networks. Such a connection using artificial neural networks using modern sensors can be another step towards full vehicle autonomy. The advantage of this approach is the original use of neural networks, which refines the determination of the deceleration value of the vehicle in front of a static or dynamic obstacle, while including a number of influences that affect the braking process and thus increase driving safety.

Czech name

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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

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OECD FORD branch

20205 - Automation and control systems

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

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

Name of the periodical

Sustainability

ISSN

2071-1050

e-ISSN

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Volume of the periodical

13

Issue of the periodical within the volume

8

Country of publishing house

CH - SWITZERLAND

Number of pages

25

Pages from-to

1-25

UT code for WoS article

000645367400001

EID of the result in the Scopus database

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