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Decision Support for Autonomous Car by Exemplar 3D Model Depth Estimation
V. Sharmila,  S. Karthick,  B.L. Aarthi
As an automatic/autonomous vehicle, it is capable of sensing its environment and navigating without human input. Many sensors are being used in detection of object and its distance. But in this project “image processing technique” is used for detecting and computation of the direction angles. Exemplar 3D model is being used for detection. Exemplar model is a template that is being matched with extracted object in road. Once object is being detected, its distance and speed is being calculated. This concept can be applied to autonomous car application by training the neural network with the help of multilayer feed forward neural network controller namely ‘Hindrance evading Controller’. Hindrance evading Controller ensures collision free motion of car. The controller is trained by the calculated distance and speed. It takes decisions such as to brake or to take left or to take right or to slow its speed. And thus it avoids accidents and controls the speed.
Keywords- Exemplar model, Hindrance evading Controller, Neural network
Unique Identification Number - IJEDR1601118Page Number(s) - 687-689Pubished in - Volume 4 | Issue 1 | March 2016DOI (Digital Object Identifier) -    Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
V. Sharmila,  S. Karthick,  B.L. Aarthi,   "Decision Support for Autonomous Car by Exemplar 3D Model Depth Estimation"
, International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.4, Issue 1, pp.687-689, March 2016, Available at :http://www.ijedr.org/papers/IJEDR1601118.pdf