ssh-shashi / autopilot

A self driving car model for humans.

Autopilot

This code helps in getting the steering angle of self driving car. The inspiraion is taken from Udacity Self driving car module as well End to End Learning for Self-Driving Cars module from NVIDIA

The End to End Learning for Self-Driving Cars research paper can be found at (https://arxiv.org/abs/1604.07316) This repository uses convnets to predict steering angle according to the road.

1. Autopilot Version 1
2. Autopilot Version 2

Code Requirements

You can install Conda for python which resolves all the dependencies for machine learning.

pip install requirements.txt

Description

An autonomous car (also known as a driverless car, self-driving car, and robotic car) is a vehicle that is capable of sensing its environment and navigating without human input. Autonomous cars combine a variety of techniques to perceive their surroundings, including radar, laser light, GPS, odometry, and computer vision. Advanced control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant signage

Autopilot V1 (Udacity Dataset based on Udacity Simulator)

Dataset

You can get the dataset at here

Python Implementation

1. Network Used- Convolutional Network
2. Inspiration - Udacity SDC and End to End Learning for Self-Driving Cars by Nvidia

If you face any problem, kindly raise an issue

Procedure

1. First, run LoadData.py which will get dataset from folder and store it in a pickle file.
2. Now you need to have the data, run TrainModel.py which will load data from pickle and augment it. After this, the training process begins.
3. For testing it on the video, run DriveApp.py

Autopilot V2 (NVIDIA Dataset based on real world)

Dataset

Download the dataset at here and extract into the repository folder

Python Implementation

1. Network Used- Convolutional Network
2. Inspiration - End to End Learning for Self-Driving Cars by Nvidia

If you face any problem, kindly raise an issue

Procedure

1. First, run LoadData_V2.py which will get dataset from folder and store it in a pickle file after preprocessing.
2. Now you need to have the data, run Train_pilot.py which will load data from pickle. After this, the training process begins.
3. For testing it on the video, run AutopilotApp_V2.py

References:

• Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, Karol Zieba. End to End Learning for Self-Driving Cars
• Behavioral Cloning Project
• This implementation also took a lot of inspiration from the Sully Chen github repository: https://github.com/SullyChen/Autopilot-TensorFlow