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Smart Automobile for Indian Roads
Objective: To propose a system design for an autonomous vehicle in order to tackle the rising road accidents and vehicle safety issues. Method: A detail study of the various intelligent systems used by the existing manufacturers of autonomous vehicles and drawing out a statistical data of where these systems lack to adapt to certain situations. An attempt to discover new findings which can tackle these issues of dynamic adaptation, unguided self-training and adaptability to roads in India. Findings: Stereoscopic vision is used to get input from environment on which image processing algorithms are applied to obtain the improved response time for autonomous vehicle. Using ANMLP (Artificial Neural Network for Multi Language Processing) algorithms to train and guide the system to learn and understand behavior patterns dynamically. Improvements: The Autonomous Vehicle shows greater adaptability to dynamic environments compared to previous versions. This in turn helps it to drive on roads where there might be unorganized traffic scenarios like in some rural parts of India.
Automobile, Autonomous, Obstacle Detection, Self-Driving, Ultrasonic Sensors
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