Design and Development of an Autonomous Car using Object Detection with YOLOv4

1
Rishabh Chopda
Rishabh Chopda
2
Saket Pradhan
Saket Pradhan
3
Anuj Goenka
Anuj Goenka
1 Thakur College of Engineering & Technology Mumbai, India

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Future cars are anticipated to be driverless; point-to-point transportation services capable of avoiding fatalities. To achieve this goal, auto-manufacturers have been investing to realize the potential autonomous driving. In this regard, we present a self-driving model car capable of autonomous driving using object-detection as a primary means of steering, on a track made of colored cones. This paper goes through the process of fabricating a model vehicle, from its embedded hardware platform, to the end-to-end ML pipeline necessary for automated data acquisition and model-training, thereby allowing a Deep Learning model to derive input from the hardware platform to control the car’s movements. This guides the car autonomously and adapts well to real-time tracks without manual feature-extraction.

Funding

No external funding was declared for this work.

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The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

Rishabh Chopda. 2026. \u201cDesign and Development of an Autonomous Car using Object Detection with YOLOv4\u201d. Global Journal of Computer Science and Technology - A: Hardware & Computation GJCST-A Volume 23 (GJCST Volume 23 Issue A1): .

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Autonomous car design for adaptive vehicle control, combining sensor tech, AI, and deep learning for self-driving.
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GJCST Volume 23 Issue A1
Pg. 15- 19
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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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January 4, 2024

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English

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Future cars are anticipated to be driverless; point-to-point transportation services capable of avoiding fatalities. To achieve this goal, auto-manufacturers have been investing to realize the potential autonomous driving. In this regard, we present a self-driving model car capable of autonomous driving using object-detection as a primary means of steering, on a track made of colored cones. This paper goes through the process of fabricating a model vehicle, from its embedded hardware platform, to the end-to-end ML pipeline necessary for automated data acquisition and model-training, thereby allowing a Deep Learning model to derive input from the hardware platform to control the car’s movements. This guides the car autonomously and adapts well to real-time tracks without manual feature-extraction.

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Design and Development of an Autonomous Car using Object Detection with YOLOv4

Rishabh Chopda
Rishabh Chopda Thakur College of Engineering & Technology Mumbai, India
Saket Pradhan
Saket Pradhan
Anuj Goenka
Anuj Goenka

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