Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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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.
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): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
The methods for personal identification and authentication are no exception.
Total Score: 103
Country: India
Subject: Global Journal of Computer Science and Technology - A: Hardware & Computation
Authors: Rishabh Chopda, Saket Pradhan, Anuj Goenka (PhD/Dr. count: 0)
View Count (all-time): 327
Total Views (Real + Logic): 2244
Total Downloads (simulated): 30
Publish Date: 2026 01, Fri
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Neural Networks and Rules-based Systems used to Find Rational and
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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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