Research Article
Ameliorated End-to-End Deep Learning System for Self Learning Cars
@INPROCEEDINGS{10.4108/eai.7-12-2021.2314534, author={Thirumahal R and BalajiMuthazhagan T}, title={Ameliorated End-to-End Deep Learning System for Self Learning Cars}, proceedings={Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, India}, publisher={EAI}, proceedings_a={ICCAP}, year={2021}, month={12}, keywords={autonomous vehicles self-driving car end-to-end deep learning convolutional neural networks feature extraction classification tools}, doi={10.4108/eai.7-12-2021.2314534} }- Thirumahal R
BalajiMuthazhagan T
Year: 2021
Ameliorated End-to-End Deep Learning System for Self Learning Cars
ICCAP
EAI
DOI: 10.4108/eai.7-12-2021.2314534
Abstract
The introduction of self-driving automobiles in today's society necessitates the development of high-quality algorithms to guide them. Convolutional neural networks are extensively utilised because of their ability to classify images based on observed attributes. The proposed solution entails steering a car autonomously using just the windshield view as input. This is accomplished by using Convolutional neural networks in an end-to-end deep learning strategy, as proposed by NVIDIA for self-driving automobiles. To improve the accuracy of existing models, the neural network will be supported with inputs from image processing modules that identify lane markers and cars.Reduced costs, increased safety, and increased mobility are all advantages of providing such an algorithm for retrofitting existing cars.
