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

• Main Authors: Jiří David, Pavel Brom, František Starý, Josef Bradáč, Vojtěch Dynybyl
• Format: Article
• Language: English
• Published: MDPI AG 2021-04-01
• Series: Sustainability
• Subjects: artificial intelligence; neural networks; adaptive cruise control; control; car assistance systems; intelligent systems
• Online Access: https://www.mdpi.com/2071-1050/13/8/4572

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.