Forward-Lane Integrity Watchdog System
Adlene Ebenezer1, S. Karthik Vignesh2, B. Sai Kishore3, A. Goku4
1Adlene Ebenezer, Assistant Professor, Department of CSE, SRM Institute of Technology, Ramapuram Tamilnadu, India.
2S.Karthik Vignesh, B.Tech. under-graduate, Department of CSE, SRM Institute of Technology, Ramapuram, Tamilnadu, India.
3B.Sai Kishore, B.Tech. under-graduate, Department of CSE, SRM Institute of Technology, Ramapuram, Tamilnadu, India.
4A.Gokul, B.Tech. under-graduate, Department of CSE, SRM Institute of Technology, Ramapuram, Tamilnadu, India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 2784-2788 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B2282129219/2019©BEIESP | DOI: 10.35940/ijeat.B2282.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Today there exist a lot of smart vehicles which can change lane on their own, using their sensors to detect the vehicles around them and using various neural or non-neural algorithms to detect the lane on the road. But these are inherently limited to well-structured road environment and struggle with unstructured road or damaged road. This paper aims to propose a new system, based on cloud and deep-learning neutral networks to process images from each region to train a neural network to be highly efficient in that particular region. We use “Collective wisdom” of people along with data analysis to improve the accuracy of the model.
Keywords: Convolutional neural network, cloud-computing, data analysis, un-structured roads, collective wisdom