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Path Planning of a Self Driving Vehicle Using Artificial Intelligence Techniques and Machine Vision

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Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) (AICV 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1153))

Abstract

This paper aims to implement an efficient model of the most optimum path to follow an object on a Self Driving Vehicle (SDV). The path of the vehicle is predicted by using Machine Vision (MV) and Neural networks (NN) model. The NN model uses numerous amounts of training data. First the system works by using the MV algorithms to detect objects with predefined colors. Then, the location of the object is fed to the trained NN to get the speeds of the motors needed to reach the object. The training data are obtained from the manual driving of the vehicle in different experiment settings. In this paper, the neural model is compared with two other methods: object detection using MV model and fuzzy logic (FL) model to prove the efficiency of the neural model. All the three models depend on the live record of the camera board and its fast detection of objects using MV algorithms. The three models showed quite similar results; however, the NN model was much more stable and closer to the optimum path.

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Author information

Authors and Affiliations

  1. Robotics and Internet of Things Lab (RIOTU), Prince Sultan University, Riyadh, Saudi Arabia

    Ahmad Taher Azar

  2. Faculty of Computers and Artificial Intelligence, Benha University, Banha, Egypt

    Ahmad Taher Azar

  3. Smart Engineering Systems Research Center (SESC), Nile University, Shaikh Zayed City, 12588, Giza, Egypt

    Hassan Ashraf Elkholy, Abdalla Saber Shahin & Hossam Hassan Ammar

  4. School of Engineering and Applied Sciences, Nile University Campus, Sheikh Zayed District, Juhayna Square, 6th of October City, 12588, Giza, Egypt

    Hassan Ashraf Elkholy, Abdalla Saber Shahin, Omar Ibrahim Elsharkawy & Hossam Hassan Ammar

Authors
  1. Hassan Ashraf Elkholy
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  2. Ahmad Taher Azar
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  3. Abdalla Saber Shahin
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  4. Omar Ibrahim Elsharkawy
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  5. Hossam Hassan Ammar
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Corresponding author

Correspondence to Ahmad Taher Azar .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Faculty of Computers and Information, Giza, Egypt

    Aboul-Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Banha, Egypt

    Ahmad Taher Azar

  3. Faculty of Computers and Information, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadajalara, Jalisco, Mexico

    Diego Oliva

  5. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Fahmy M. Tolba

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Elkholy, H.A., Azar, A.T., Shahin, A.S., Elsharkawy, O.I., Ammar, H.H. (2020). Path Planning of a Self Driving Vehicle Using Artificial Intelligence Techniques and Machine Vision. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_50

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