🤖🚙 This is the capstone project for Udacity's Self Driving Car Nanodegree.
The gif below shows our code deployed on Udacity's (actual) self driving car nicknamed Carla. Carla was tested in a small parking lot, and was supervised by a trained engineer from Udacity. I sat shotgun and filmed the sequence. 👍The car drives autonomously and properly stops at a (fake) traffic light when it turns red.
Update (Oct 2017) I graduated the Self-Driving Car Nanodegree 🎉🍾. Here's a brief I wrote on my blog atul.fyi
Here's the certificate 🍾🎉
This repository is maintained by the following:
- George Terzakis
- Martin Herzog
- Yuda Liu
- Atul Acharya
- Yoni Azuelos
The following video shows the code in action:
- Clone the project repository
git clone https://github.com/herzogmartin/CarND-Capstone.git- Clone the team's submodule
cd CarND-Capstone
git submodule init
git submodule update
git pull origin master- Install python dependencies
pip install -r requirements.txt- Make and run styx
cd ros
catkin_make
source devel/setup.sh
roslaunch launch/styx.launch- Run the simulator
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Be sure that your workstation is running Ubuntu 16.04 Xenial Xerus or Ubuntu 14.04 Trusty Tahir. Ubuntu downloads can be found here.
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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.
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Follow these instructions to install ROS
- ROS Kinetic if you have Ubuntu 16.04.
- ROS Indigo if you have Ubuntu 14.04.
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- Use this option to install the SDK on a workstation that already has ROS installed: One Line SDK Install (binary)
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Download the Udacity Simulator.
Build the docker container
docker build . -t capstoneRun the docker file
docker run -p 127.0.0.1:4567:4567 -v $PWD:/capstone -v /tmp/log:/root/.ros/ --rm -it capstone- 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
unzip traffic_light_bag_files.zip- 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