Přehled o publikaci
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
References:
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.