Design and implementation of self-driving car

Fathy, Mahmoud; Ashraf, Nada; Ismail, Omar; Fouad, S.S.; Shaheen, Lobna; Hamdy, A.

doi:10.1016/j.procs.2020.07.026

Cited by 13 publications

(2 citation statements)

References 10 publications

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“…In order to decrease road accidents and fatalities, the autonomous vehicles (AVs) industry is improving day by day. This innovative technology was initiated and deployed by the utilization of computers in the control function of the vehicles [18] . On the other hand, with the growth and influence of artificial intelligence in all scientific fields, AV technology has also taken a continuous upward development.…”

Section: Introductionmentioning

confidence: 99%

Barriers to the sustainable adoption of autonomous vehicles in developing countries: A multi-criteria decision-making approach

Shahedi

Dadashpour

Rezaei

2023

Heliyon

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Section: Introductionmentioning

confidence: 99%

Barriers to the sustainable adoption of autonomous vehicles in developing countries: A multi-criteria decision-making approach

Shahedi

Dadashpour

Rezaei

2023

Heliyon

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“…A self-driving automobile is simply analytical, with no emotions or distractions because it is a computer. Computers make decisions faster and more intelligently than humans [3]. There are a wide range of definitions across the industry on levels of automation but they are briefly classified as No automation, Semi-autonomous, Fully-autonomous.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Obstacle Detection and Collision Prevention in Self-Driving Cars

Devi

Sathvik

Ananya

et al. 2022

J. Phys.: Conf. Ser.

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With increasing computational power and a vast amount of data to work with, deep learning has risen to prominence since the 2010s. Numerous applications are being researched and developed using deep learning. One of the applications is computer vision in self-driving cars. Convolutional Neural Networks (CNNs) are being widely used because of their high performance compared to other alternative techniques in several perception and control tasks. The Convolutional Neural Networks (CNNs) allows the automobile to learn from different types of roads, scenarios allowing the car to forecast its route on any particular road with minimum inaccuracy. This paper proposes a working model of the autonomous car, which has a Raspberry Pi 4 Model B as the control unit and processing unit. This working model gets real-time images from the Raspberry Pi camera and these images are used by the CNN model, which predicts the direction the car must turn. The raspberry pi sends the control signals to the L298n motor driver. The trained CNN model achieved an accuracy of 96.39% with the test dataset and 84.77% with the validation dataset.

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Aerodynamic Interaction of Minivan Vehicles During an Overtaking Maneuver

Filipkovskij,

Avershyn

2024

Lecture Notes in Networks and Systems

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Design and implementation of self-driving car

Cited by 13 publications

References 10 publications

Barriers to the sustainable adoption of autonomous vehicles in developing countries: A multi-criteria decision-making approach

Barriers to the sustainable adoption of autonomous vehicles in developing countries: A multi-criteria decision-making approach

Vision-Based Obstacle Detection and Collision Prevention in Self-Driving Cars

Aerodynamic Interaction of Minivan Vehicles During an Overtaking Maneuver

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