This is the final project of Udacity's Self Driving Car Nanodegree. My name is:
Manuel Alejandro Rios Rivas ([email protected])
In order to run the project you will need to download the trained model from here and copy its contents inside
CarND-Capstone/ros/src/tl_detector/light_classification/tensorflow
Please note you will have to install Tensorflow 1.7.0 or higher. (See requirements.txt)
All work was based upon two main sources:
- Udacity's Capstone Project walktrough
- Traffic Light Detection and Classification with TensorFlow Object Detection API
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.
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
- 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