Deep Traffic learning: an automatic vehicle speed assisting tool based on the varying traffic conditions to ensure a safe driving
N.V.S.Pavan Kumar1, Feroz khan2, Madhumitha kuppachi3

1N.V.S. Pavan Kumar, Assistant Professor, Computer Science Engineering, KLEF deemed to be University, Vaddeswaram, India.
2Feroz Khan,Under Graduate Student, Computer science engineering, KLEF deemed to be University, Vaddeswaram, India.
3Madhumitha Kuppachi,Under Graduate Student, Computer science engineering, KLEF deemed to be University, Vaddeswaram, India

Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 805-810 | Volume-8 Issue-8, June 2019 | Retrieval Number: F4006048619/19©BEIESP
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Abstract: In a day to day life most of the accidents are increased due to lack of vehicle speed management by the driver in different traffic conditions. In some cases, it is hard to track speed limit board by the driver due to some several reasons like obstruction from large vehicles, trees and due to speed driving, etc. To decreasing accidents on-road we are extending the framework for vehicle speed assisting tool by continuous monitoring of traffic while driving. For this experiment, we are using popular deep learning technique named Convolutional Neural network (CNN) which consists of eight convolutional layers. To input a CNN, we have created our own data namely KL-Traffic Data comprising of nine traffic condition classes which consisting of on-road traffic images and we have set the speed limits for these nine-traffic conditions. The CNN model was trained by the KL-Traffic Data and the output will be Traffic condition class. Based on the traffic condition class our model sets the speed limit which driver must follow for a safe driving.
Keyword: Convolutional neural network, On-road Traffic, Vehicle speed management.
Scope of the Article: Deep Learning.