Path finding of auto driving car using deep learning

In this project, CNN has been applied as a training tool to process image classification and object avoidance on remote robotic cars built with the Nvidia Jetson Nano developer kit. The kit was programmed using the wireless programming environment, Jupyter notebook. In addition, two different CNN models have been applied to analyze the output result performance. The main purpose is to train the robot to identify objects and improve its accuracy. The recognition and accuracy rate under different conditions can be observed by comparing the two models with different graphic inputs conditions. This project adopts the pre-train model for real time demonstrations and can be executed in a cloudless environment (without networks involved). As a result, the robot can achieve a high accuracy rate in both CNN models output result performance. Moreover, the pre train model can execute in local service to accomplish cloudless.