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

Jiří David, Pavel Brom, František Starý, Josef Bradáč and Vojtěch Dynybyl
Additional contact information
Jiří David: Department of Mechanical and Electrical Engineering, ŠKODA AUTO University, 1457, 29301 Mladá Boleslav, Czech Republic
Pavel Brom: Department of Quantitative Methods, ŠKODA AUTO University, 1457, 29301 Mladá Boleslav, Czech Republic
František Starý: Department of Mechanical and Electrical Engineering, ŠKODA AUTO University, 1457, 29301 Mladá Boleslav, Czech Republic
Josef Bradáč: Department of Mechanical and Electrical Engineering, ŠKODA AUTO University, 1457, 29301 Mladá Boleslav, Czech Republic
Vojtěch Dynybyl: Department of Mechanical and Electrical Engineering, ŠKODA AUTO University, 1457, 29301 Mladá Boleslav, Czech Republic

Sustainability, 2021, vol. 13, issue 8, 1-25

Abstract: 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.

Keywords: artificial intelligence; neural networks; adaptive cruise control; control; car assistance systems; intelligent systems; real-time systems (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/8/4572/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/8/4572/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:8:p:4572-:d:539782

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().