My most ambitious project for simulating a self driving car with EEG. This is the code for my project where I used Udacity's self driving car simulator as a testbed for training an autonomous car.
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
__pycache__
images
README.md
drive.py
environment-gpu.yml
environments.yml
model.h5
model.py
self-driving-car.ipynb
utils.py

README.md

Self Driven Future with EEG

EEG Code not open-sourced

My_self_driving_car_project

Author: Rishab Sharma (rishab-sharma)

Overview

This is the code for my project where I used Udacity's self driving car simulator as a testbed for training an autonomous car.

Demo Video

Self Driving car

Dependencies

You can install all dependencies by running one of the following commands

You need a anaconda or miniconda to use the environment setting.

# Use TensorFlow without GPU
conda env create -f environments.yml 

# Use TensorFlow with GPU
conda env create -f environment-gpu.yml

Or you can manually install the required libraries (see the contents of the environemnt*.yml files) using pip.

Usage

Run the pretrained model

Start up the Udacity self-driving simulator, choose a scene and press the Autonomous Mode button. Then, run the model as follows:

python drive.py model.h5

To train the model

You'll need the data folder which contains the training images.

python model.py

This will generate a file model-<epoch>.h5 whenever the performance in the epoch is better than the previous best. For example, the first epoch will generate a file called model-000.h5.