SDC Fun
This is the SDC Fun
team repo for the capstone project of Udacity's Self-Driving Car Engineer Nanodegree. The team members are:
- James Barfield ([email protected])
- Nick Condo([email protected])
- Jim Duan ([email protected])
- Nimish Sanghi ([email protected])
- Colin Shaw ([email protected])
Building and running the project
The original project repo can be found here, which has a lot of information about environment, simulator, etc.
This project requires the GPU version of Tensorflow along with the CUDA and cuDNN libraries, python 2.7, as well as the following libraries defined more specifically in requirements.txt
:
- Flask
- attrdict
- eventlet
- python-socketio
- numpy
- Pillow
- scipy
- keras
- h5py
- requests
These modules can be installed using: pip install -r requirements.txt
from the root of the project.
You will need to download our model in order to detect traffic lights. You can download the model by navigating to self-driving-car-capstone/data_science/models
and running python get_model.py
. You may have to manually install requests
since it is not included in the upstream requirements.txt
and is needed for downloading the model.
Alternatively, you can download the model manually here.
Next go into the /ros
directory and run catkin_make
to build the ROS project. Be sure to source the project by running . devel/setup.sh
. At this point, if your environment is set up properly, you should be able to launch ROS with roslaunch launch/styx.launch
.
Further information about our implementation can be found here.