J 2021

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

DAVID, Jiří; Pavel BROM; František STARÝ; Josef BRADÁČ; Vojtěch DYNYBYL et. al.

Basic information

Original name

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

Authors

DAVID, Jiří; Pavel BROM; František STARÝ; Josef BRADÁČ and Vojtěch DYNYBYL

Edition

Sustainability, Basel, MDPI, 2021, 2071-1050

Other information

Language

English

Type of outcome

Article in a journal

Field of Study

50200 5.2 Economics and Business

Country of publisher

Switzerland

Confidentiality degree

is not subject to a state or trade secret

Organization

Škoda Auto Vysoká Škola o.p.s. – Repository

UT WoS

645367400001

Keywords in English

car assistance systems; adaptive cruise control; artificial intelligence; real-time systems; neural networks; intelligent systems; control

Tags

International impact, Reviewed
Changed: 22/9/2024 13:36, Barbora Dobrá

Abstract

V originále

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.

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