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.
You can install Conda for python which resolves all the dependencies for machine learning.
pip install requirements.txt
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
You can get the dataset at here
- Network Used- Convolutional Network
- Inspiration - Udacity SDC and End to End Learning for Self-Driving Cars by Nvidia
If you face any problem, kindly raise an issue
- First, run
LoadData.pywhich will get dataset from folder and store it in a pickle file.
- Now you need to have the data, run
TrainModel.pywhich will load data from pickle and augment it. After this, the training process begins.
- For testing it on the video, run
- 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