IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i13p4766-d851325.html
   My bibliography  Save this article

Application of Neural Data Processing in Autonomous Model Platform—A Complex Review of Solutions, Design and Implementation

Author

Listed:
  • Mateusz Malarczyk

    (Department of Electrical Machines, Drives and Measurements, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-372 Wroclaw, Poland)

  • Jules-Raymond Tapamo

    (School of Engineering, University of KwaZulu-Natal, Durban 4041, South Africa)

  • Marcin Kaminski

    (Department of Electrical Machines, Drives and Measurements, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-372 Wroclaw, Poland)

Abstract

One of the bottlenecks of autonomous systems is to identify and/or design models and tools that are not too resource demanding. This paper presents the concept and design process of a moving platform structure–electric vehicle. The objective is to use artificial intelligence methods to control the model’s operation in a resource scarce computation environment. Neural approaches are used for data analysis, path planning, speed control and implementation of the vision system for road sign recognition. For this purpose, multilayer perceptron neural networks and deep learning models are used. In addition to the neural algorithms and several applications, the hardware implementation is described. Simulation results of systems are gathered, data gathered from real platform tests are analyzed. Experimental results show that low-cost hardware may be used to develop an effective working platform capable of autonomous operation in defined conditions.

Suggested Citation

  • Mateusz Malarczyk & Jules-Raymond Tapamo & Marcin Kaminski, 2022. "Application of Neural Data Processing in Autonomous Model Platform—A Complex Review of Solutions, Design and Implementation," Energies, MDPI, vol. 15(13), pages 1-22, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4766-:d:851325
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/13/4766/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/13/4766/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Calin Iclodean & Nicolae Cordos & Bogdan Ovidiu Varga, 2020. "Autonomous Shuttle Bus for Public Transportation: A Review," Energies, MDPI, vol. 13(11), pages 1-45, June.
    2. Xuan Fang & Hexuan Li & Tamás Tettamanti & Arno Eichberger & Martin Fellendorf, 2022. "Effects of Automated Vehicle Models at the Mixed Traffic Situation on a Motorway Scenario," Energies, MDPI, vol. 15(6), pages 1-15, March.
    3. Marcin Kamiński & Krzysztof Szabat, 2021. "Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft," Energies, MDPI, vol. 14(12), pages 1-26, June.
    4. Marcin Kaminski, 2020. "Nature-Inspired Algorithm Implemented for Stable Radial Basis Function Neural Controller of Electric Drive with Induction Motor," Energies, MDPI, vol. 13(24), pages 1-25, December.
    5. Amal A. Al-Shargabi & Abdulbasit Almhafdy & Dina M. Ibrahim & Manal Alghieth & Francisco Chiclana, 2021. "Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    6. Valery Vodovozov & Andrei Aksjonov & Eduard Petlenkov & Zoja Raud, 2021. "Neural Network-Based Model Reference Control of Braking Electric Vehicles," Energies, MDPI, vol. 14(9), pages 1-22, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marcin Kaminski & Tomasz Tarczewski, 2023. "Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction," Energies, MDPI, vol. 16(11), pages 1-25, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marcin Kaminski & Tomasz Tarczewski, 2023. "Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction," Energies, MDPI, vol. 16(11), pages 1-25, May.
    2. Zehua Li & Jiaran Niu & Zhenzhou Li & Yukun Chen & Yang Wang & Bin Jiang, 2022. "The Impact of Individual Differences on the Acceptance of Self-Driving Buses: A Case Study of Nanjing, China," Sustainability, MDPI, vol. 14(18), pages 1-14, September.
    3. Kum Fai Yuen & Ling Qian Choo & Xue Li & Yiik Diew Wong & Fei Ma & Xueqin Wang, 2023. "A theoretical investigation of user acceptance of autonomous public transport," Transportation, Springer, vol. 50(2), pages 545-569, April.
    4. L. Samková, 2023. "Management of integrated passenger transport system and its role in tourism development," Economics Working Papers 2023-03, University of South Bohemia in Ceske Budejovice, Faculty of Economics.
    5. Arno Eichberger & Zsolt Szalay & Martin Fellendorf & Henry Liu, 2022. "Advances in Automated Driving Systems," Energies, MDPI, vol. 15(10), pages 1-5, May.
    6. Yang, Yiran & Li, Gang & Luo, Tao & Al-Bahrani, Mohammed & Al-Ammar, Essam A. & Sillanpaa, Mika & Ali, Shafaqat & Leng, Xiujuan, 2023. "The innovative optimization techniques for forecasting the energy consumption of buildings using the shuffled frog leaping algorithm and different neural networks," Energy, Elsevier, vol. 268(C).
    7. Constantin Volosencu, 2022. "Study of the Angular Positioning of a Rotating Object Based on Some Computational Intelligence Methods," Mathematics, MDPI, vol. 10(7), pages 1-46, April.
    8. Yan, Yingying & Zhong, Shiquan & Tian, Junfang & Li, Tong, 2022. "Continuance intention of autonomous buses: An empirical analysis based on passenger experience," Transport Policy, Elsevier, vol. 126(C), pages 85-95.
    9. Jacek Kabziński & Przemysław Mosiołek, 2021. "Integrated, Multi-Approach, Adaptive Control of Two-Mass Drive with Nonlinear Damping and Stiffness," Energies, MDPI, vol. 14(17), pages 1-23, September.
    10. Jacek Kabziński & Przemysław Mosiołek, 2022. "Observer-Based, Robust Position Tracking in Two-Mass Drive System," Energies, MDPI, vol. 15(23), pages 1-28, November.
    11. Krzysztof Szabat & Tomasz Pajchrowski & Tomasz Tarczewski, 2021. "Modern Electrical Drives: Trends, Problems, and Challenges," Energies, MDPI, vol. 15(1), pages 1-4, December.
    12. Fahimeh Golbabaei & Tan Yigitcanlar & Alexander Paz & Jonathan Bunker, 2023. "Perceived Opportunities and Challenges of Autonomous Demand-Responsive Transit Use: What Are the Socio-Demographic Predictors?," Sustainability, MDPI, vol. 15(15), pages 1-18, August.
    13. Sehyun Tak & Sari Kim & Hwapyeong Yu & Donghoun Lee, 2022. "Analysis of Relationship between Road Geometry and Automated Driving Safety for Automated Vehicle-Based Mobility Service," Sustainability, MDPI, vol. 14(4), pages 1-13, February.
    14. Devi Munandar & Budi Nurani Ruchjana & Atje Setiawan Abdullah & Hilman Ferdinandus Pardede, 2023. "Literature Review on Integrating Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) and Deep Neural Networks in Machine Learning for Climate Forecasting," Mathematics, MDPI, vol. 11(13), pages 1-25, July.
    15. Sönke Beckmann & Sebastian Trojahn & Hartmut Zadek, 2023. "Process Model for the Introduction of Automated Buses," Sustainability, MDPI, vol. 15(19), pages 1-36, September.
    16. Karol Tucki & Olga Orynycz & Agnieszka Dudziak, 2022. "The Impact of the Available Infrastructure on the Electric Vehicle Market in Poland and in EU Countries," IJERPH, MDPI, vol. 19(24), pages 1-23, December.
    17. Sungwon Lee & Devon Farmer & Jooyoung Kim & Hyun Kim, 2022. "Shared Autonomous Vehicles Competing with Shared Electric Bicycles: A Stated-Preference Analysis," Sustainability, MDPI, vol. 14(21), pages 1-19, November.
    18. Andrea Gemma & Tina Onorato & Stefano Carrese, 2023. "Performances and Environmental Impacts of Connected and Autonomous Vehicles for Different Mixed-Traffic Scenarios," Sustainability, MDPI, vol. 15(13), pages 1-19, June.
    19. Hongwen Dou & Radu Zmeureanu, 2023. "Transfer Learning Prediction Performance of Chillers for Neural Network Models," Energies, MDPI, vol. 16(20), pages 1-16, October.
    20. Marian Janusz Łopatka & Karol Cieślik & Piotr Krogul & Tomasz Muszyński & Mirosław Przybysz & Arkadiusz Rubiec & Kacper Spadło, 2023. "Research on Terrain Mobility of UGV with Hydrostatic Wheel Drive and Slip Control Systems," Energies, MDPI, vol. 16(19), pages 1-22, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4766-:d:851325. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.