Self_Driving_Car

The goal of the project is to train a Deep Neural Network to replicate the human steering behavior while driving, thus being able to drive autonomously on a simulator. The network takes as input the frame of the frontal camera ( a roof-mounted camera) and will predict the steering direction at each instant.

Dependencies

• socketio
• eventlet
• numpy
• pandas
• flask
• keras
• Tensorflow
• base64
• io
• openCV
• PIL
• Python 3
• imgaug

For the purpose of simulation and training the neural network, i have used Udacity simulator. To run this, start simulator in autonomous mode, and run on terminal/cmd python drive_car.py

Dataset

The dataset consists of images (captured from the simulator) and steering angle. [

Data preprocessing

• crop the image
• Convert RGB to YUV
• Gaussian Blur
• Resize the image
• Normalize the image

Data Augmentation

• After selecting the final set of frames, we augment the data by adding artificial shifts and rotations to teach the network how to recover from a poor position or orientation.
• Data Augmentation techiques : Zoom, Width shift, Heightshift, Image brightness and Flipped image.
• All of these are implemented in order to diversify our dataset so that car moves more acuurately and smoothly.