Behaviorial Cloning Project
Overview
This repository contains files used for the completion of the Behavioral Cloning Project.
In this project, I've used deep neural networks and convolutional neural networks to clone driving behavior. I trained, validated and tested a model using Keras. The model outputs a steering angle to an autonomous vehicle.
Udacity provided a simulator where you can steer a car around a track for data collection. I use image data and steering angles to train a neural network and then use this model to drive the car autonomously around the track.
Check out the writeup for this project.
To meet specifications, the project consists of five files:
- model.py (script used to create and train the model)
- drive.py (script to drive the car - feel free to modify this file)
- model-v13.h5 (a trained Keras model)
- a report writeup file (either markdown or pdf)
- video.mp4 (a video recording of your vehicle driving autonomously around the track for at least one full lap)
The Project
The goals / steps of this project are the following:
- Use the simulator to collect data of good driving behavior
- Design, train and validate a model that predicts a steering angle from image data
- Use the model to drive the vehicle autonomously around the first track in the simulator. The vehicle should remain on the road for an entire loop around the track.
- Summarize the results with a written report
Dependencies
This code requires:
The lab enviroment can be created with CarND Term1 Starter Kit. Click here for the details.
The following resources can be found in this github repository:
- drive.py
- video.py
Running the model
drive.py
To run the trained model use it with drive.py using this command:
python drive.py model-v13.h5The above command will load the trained model and use the model to make predictions on individual images in real-time and send the predicted angle back to the server via a websocket connection.
Results
The model works quite well on track 1 and can be seen in the below video.
