Team lead: Eric Qian Team members: Ziwei Zeng, Yisi Liu
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.
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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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Speical note about Tensorflow-Gpu
- this program is ok to use Tensorflow-cpu. the inference speed is good enough (<90ms) in a CPU mode. In case you want to use Tensorflow-GPU, please make sure the version is latest Tensorflow-GPU as per the requirement.txt. And the CUDA 9.0 and CUDNN 7.4.24 (supported version for Ubuntu 16.04)
- to simplify your setup and validation process, simply use the Docker Installation below, which is the main way the app is developed and tested.
Clone this project repository assuming you're on Mac or Linux developerment environment
git clone https://github.com/ericq/CarND-Capstone.git
Build the docker container
docker build . -t capstone
Run the docker file. Note that this will start the docker container and mount host $PWD (project folder) directory to the docker container as '/capstone' directory; the same for log file direcotry.
docker run -p 4567:4567 -v $PWD:/capstone -v /tmp/log:/root/.ros/ --name my_capstone --rm -it capstone
Open additional temrinal to access the docker container.
docker exec -it my_capstone bash
- Go to the project folder
cd /capstone
- Install python dependencies
apt-get install vim
pip install -r requirements.txt
# in above operation, please ensure that the tensorflow, numpy, pillow all are updated per the specified version in the requirements.txt
- Make and run styx
cd ros
catkin_make
source devel/setup.sh
roslaunch launch/styx.launch
- Run the simulator
- 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