SELF DRIVING CAR SIMULATION USING CNN NVIDIA TENSORFLOW

Self-driving cars are moving from fiction to reality, but are we ready for it? This study analyses the current state of self-driving cars. Address gaps that need to be filled and identify problems that need to be solved before self- driving cars become a reality on the road. There are 4,444 technological advancements each year, paving the way for the integration of artificial intelligence into automobiles. In this paper, we provided a comprehensive study-on his implementation of self-driving cars based on deep learning. For your convenience and safety, simulate your car with the simulator provided by Udacity. Data for training the model is collected in the simulator and imported into the project to train the model. Finally, we implemented and compared various existing deep learning models and presented the results. Since the biggest challenge for self-driving cars is the autonomous lateral movement of the, the main goal of this white paper is to clone the drive using multilayer neural networks and deep learning techniques. and improve the performance of self-driving cars. Focuses on the realization of his self-driving cars driving under stimulus conditions. Within the simulator, the mimics the images obtained from the cameras installed in his car, the driver’s vision, and the reaction that is her steering angle of the car. A neural network trains the deep learning technique based on photos taken by camera in manual mode. This provides the conditions for driving the car in automatic mode using a trained multilayer neural network. The driver imitation algorithm created and characterized in this paper is a deep learning technique centered around the NVIDIA CNN model.