Key points are not available for this paper at this time.
This project deals with building an autonomous car that can travel safely and intelligently avoiding the risk of human errors. This raspberry Pi based project can detect the obstacles & traffic light. It can compare the data processed with the data provided to it and is able to take an intelligent decision whether to stop or continue its present path. Important components involved in this project are - the hardware platform which includes raspberry pi board, all the hardware like pi camera and the ultrasonic sensor for improved efficiency & the camera used along with an ultrasonic sensor to provide necessary data from the world for real time processing and application. Second being the cloud platform which will be basically used to train our raspberry pi board for real time applications. Cloud helps us to test as well as train better tracking and decision models & helps in providing the offline computing and storage capabilities for vehicle. Basically, it will be used to train the processor to differentiate between positive (green signal) and negative (red signal) images using various thousands of such signal images as an example. The third and most important part includes the algorithms for perception, control, localization, and recognition. Key Words: Raspberry pi, L293D Driver, Machine Learning, Open CV, Ultrasonic Sensor & Pi Camera.
Deepak Kumar Thakur (Mon,) studied this question.