This is the project repo for the final project of the Udacity Self-Driving Car Nanodegree: Programming a Real Self-Driving Car. For more information about the project, see the project introduction here.
Please use one of the two installation options, either native or docker installation.
This project was completed by Dean Liu, Louis Chow, Luuthien Xuan, Prasad Pillai, and Tom Bertalan.
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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.
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Either clone this repository to /capstone, or clone it elsewhere and symlink it there with
ln -s "$CLONEDIR" /capstone
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Make a python 2.7 virtual environment. virtualenvwrapper is great for this.
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If installing on a machine with a CUDA- and TensorFlow-capable GPU:
- Follow TensorFlow's installation instructions, including the link to instructions for installing the CUDA toolkit and cuDNN. You may want to insert any commands for modifying your environment variables, such as
LD_LIBRARY_PATH
, into your ~/.profile file. - Change
tensorflow
totensorflow-gpu
in requirements.txt. - Note that CUDA 8 may be necessary for running with Python 2.7, as is the norm for ROS. This can be installed alongside CUDA 9 with
sudo apt-get install cuda-8-0
.
- Follow TensorFlow's installation instructions, including the link to instructions for installing the CUDA toolkit and cuDNN. You may want to insert any commands for modifying your environment variables, such as
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Reproduce the installation steps from Dockerfile, including running tod_coco_install.sh. Note that you'll first need to run e.g.
export ROS_DISTRO=kinetic
depending on your ROS distribution. Note that tod_coco_install.sh modifies your .bashrc file. -
Still in the virtualenv, run
pip uninstall em
andpip install
catkin_pkg
em
rospkg
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In /capstone/ros, run
catkin_make
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Either in a terminal where
roslaunch
is to be run, or permanently via ~/.bashrc or ~/.profile, runsource /capstone/ros/devel/setup.sh
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Download luu_real.pb and luu_sim.pb and put them both in /capstone/ros/src/tl_detector/light_classification/saved_nets, making this directory if necessary.
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Optionally, install the the Rviz plugins with
sudo apt-get install ros-kinetic-jsk-rviz-plugins libbullet-dev libsdl-image1.2-dev libsdl-dev
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Build the docker container
docker build . -t capstone
Run the docker file
docker run -p 4567:4567 -v $PWD:/capstone -v /tmp/log:/root/.ros/ --rm -it capstone
To set up port forwarding, please refer to the instructions from term 2
In /capstone/ros, after establishing the right $PYTHONPATH
environment variable to (likely via ~/.profile) and sourcing /capstone/ros/devel/setup.sh, run the controller and light-detector with roslaunch launch/styx.launch
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When the lauch has completed and all nodes are running, use run the simulator, and uncheck "Manual", and check "Camera".
- Download training bag that was recorded on the Udacity self-driving car.
- Unzip the file
unzip traffic_light_bag_file.zip
- Play the bag file
rosbag play -l traffic_light_bag_file/traffic_light_training.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