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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jiří David & Pavel Brom & František Starý & Josef Bradáč & Vojtěch Dynybyl, 2021. "Application of Artificial Neural Networks to Streamline the Process of Adaptive Cruise Control," Sustainability, MDPI, vol. 13(8), pages 1-25, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:8:p:4572-:d:539782
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    References listed on IDEAS

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    1. Maode Yan & Jiacheng Song & Lei Zuo & Panpan Yang, 2017. "Neural Adaptive Sliding-Mode Control of a Vehicle Platoon Using Output Feedback," Energies, MDPI, vol. 10(11), pages 1-17, November.
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    Cited by:

    1. Saeed Vasebi & Yeganeh M. Hayeri, 2021. "Collective Driving to Mitigate Climate Change: Collective-Adaptive Cruise Control," Sustainability, MDPI, vol. 13(16), pages 1-30, August.
    2. Yi-Jen Mon, 2022. "Vision Robot Path Control Based on Artificial Intelligence Image Classification and Sustainable Ultrasonic Signal Transformation Technology," Sustainability, MDPI, vol. 14(9), pages 1-14, April.

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