IJEEDC A Comparative Study on Machine Learning Algorithms for Obstruction Detection and Course Correction for Autonomous Driving

Journal Paper

Paper Title :A Comparative Study on Machine Learning Algorithms for Obstruction Detection and Course Correction for Autonomous Driving

Author :Soumyendra Singh, Akhil Sachin

Article Citation :Soumyendra Singh ,Akhil Sachin , (2024 ) " A Comparative Study on Machine Learning Algorithms for Obstruction Detection and Course Correction for Autonomous Driving " , International Journal of Electrical, Electronics and Data Communication (IJEEDC) , pp. 29-33, Volume-12,Issue-9

Abstract : The arrival of autonomous driving systems has introduced another era of transportation, promising upgraded security, accommodation and convenience on the streets. One of the critical challenges in autonomous driving is the real- time detection of obstructions in the vehicle’s path and the capacity to make ecessary course corrections to guarantee a safe route. In this examination paper, we lead a complete relative investigation of two AI calculations for obstruction detection and course correction: the VGG (Visual Geometry Group) model and the PyTorch UNet model. Our review assesses the exhibition of these models as well as examines their suitability with regard to autonomous driving. Through thorough experimentation and investigation, we aim to give significant insights into the improve- ment of autonomous driving systems. Keywords - Image Segmentation, Visual Geometry Groups, UNet, Autonomous Driving

Type : Research paper

Published : Volume-12,Issue-9


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Mar. 2025
Submitted Papers	:	80
Accepted Papers	:	10
Rejected Papers	:	70
Acc. Perc	:	12%
Issue Published	:	143
Paper Published	:	1781
No. of Authors	:	4956

Published : Volume-12,Issue-9

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