Self-Driving Car Nanodegree Capstone Project
This is the Capstone project repository for the Udacity Self-Driving Car Nanodegree.
For more information about the project, see the project introduction here.
|Name||Udacity account email|
|Dimitris Traskas (Team Lead)||dtraskas at gmail.com|
|Denise Miller||denisej199 at gmail.com|
|Jeremy Shannon||jeremyplaysthedrums at hotmail.com|
|Edwin Wong||sze224 at gmail.com|
|Sergio Gordillo||sgordillogallardo at gmail.com|
Be sure that your workstation is running Ubuntu 16.04 Xenial Xerus or Ubuntu 14.04 Trusty Tahir. Ubuntu downloads can be found here.
If using a Virtual Machine to install Ubuntu, use the following configuration as minimum:
- 2 CPU
- 2 GB system memory
- 25 GB of free hard drive space
The Udacity provided virtual machine has ROS and Dataspeed DBW already installed, so you can skip the next two steps if you are using this.
Follow these instructions to install ROS
- Use this option to install the SDK on a workstation that already has ROS installed: One Line SDK Install (binary)
Download the Udacity Simulator.
Build the docker container
docker build . -t capstone
Run the docker file
docker run -p 127.0.0.1:4567:4567 -v $PWD:/capstone -v /tmp/log:/root/.ros/ --rm -it capstone
- Clone the project repository
git clone https://github.com/udacity/CarND-Capstone.git
- Install python dependencies
cd CarND-Capstone pip install -r requirements.txt
- Make and run styx
cd ros catkin_make source devel/setup.sh roslaunch launch/styx.launch
- Run the simulator
Real world testing
- Download training bag that was recorded on the Udacity self-driving car (a bag demonstraing the correct predictions in autonomous mode can be found here)
- Unzip the file
- Play the bag file
rosbag play -l traffic_light_bag_files/loop_with_traffic_light.bag
- Launch your project in site mode
cd CarND-Capstone/ros roslaunch launch/site.launch
- Confirm that traffic light detection works on real life images