Capstone Project of the Udacity Self-Driving Car Nanodegree: Programming a Real Self-Driving Car.

This repo is bsed on the Docker setup.

Install Docker

1. Clone this project repository

git clone git@github.com:joustava/CarND-Capstone.git

2. Build the Docker image from the Dockerfile available in the repository

docker build . -t capstone

3. Start a container based on the previously build image

docker run -p 4567:4567 -v $PWD:/capstone -v /tmp/log:/root/.ros/ --rm -it capstone

Within the running container

1. Change to the catkin workspace

cd ros

2. Build the workspace

catkin_make

3. Source the scripts

source devel/setup.sh

4. Run the project

roslaunch launch/styx.launch

To see the result you need to first download the simulator for your system and unzip the package. You probably need to change the permissions e.g: chmod -R u+x ~/Downloads/mac_sys_int (path to wherever you unzipped the files). Then you can click the sys_int application, it should connect to the ROS application and the car will start acting upon the data it receives.

1. Download training bag that was recorded on the Udacity self-driving car.
2. Unzip the file

unzip traffic_light_bag_file.zip

3. Play the bag file

rosbag play -l traffic_light_bag_file/traffic_light_training.bag

4. Launch your project in site mode

cd CarND-Capstone/ros
roslaunch launch/site.launch

5. Confirm that traffic light detection works on real life images

Outside of requirements.txt, here is information on other driver/library versions used in the simulator and Carla:

Specific to these libraries, the simulator grader and Carla use the following:

We are working on a fix to line up the OpenCV versions between the two.