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.
🚦 🚗
In this project, we program a real self-driving car to drive on a parking lot and stop on red lights.
To drive on the parking lot, we first test our implementation on two separate simulators.
-
The first simulator is a highway simulator. Here is the video of the results here : ###VIDEO
-
The second simulator is the same as the parking lot but played with rosbag. Here is the video of the results here : ###VIDEO
Finally, we are able to drive the car around the parking lot : Video here : ###VIDEO
The team who built this project is a team of five engineers that met during Term 1, helping eachothers on understanding Convolutional Neural Networks, Computer Vision, and Robotics. We finally decided to team up at the beginning of Term 3 and to call ourselves : The Racers
| Name | Image | Location | Github | |
|---|---|---|---|---|
| Vincent Wiart | FRANCE | linkedin.com/in/vwiart | github.com/vwiart | |
| Anil Dhole | INDIA | linkedin.com/in/anil-dhole | github.com/ajdhole | |
| Jeremy Cohen | FRANCE | linkedin.com/in/jeremycohen2626 | github.com/Jeremy26 | |
| Anurag Kankanala | INDIA | linkedin.com/in/anurag-kankanala | github.com/anuragkankanala | |
| Srikanth Mutyala | USA | linkedin.com/in/srikanth-mutyala-a0a30546 | github.com/srimutyala |
The project runs with ROS and is divided into the following modules :
tl_detectoruses the camera to detect the traffic lights' colortwist_controllerhandles the control of the carwaypoint_followermakes sure the car follow the trajectorywaypoint_loaderloads the route the car is going to followwaypoint_updateradapts the car's route to the situation (eg. traffic light)
Please use one of the two installation options, either native or docker installation.
-
Be sure that your workstation is running Ubuntu 16.04 Xenial Xerus or Ubuntu 14.04 Trusty Tahir. Ubuntu downloads can be found here.
-
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.
-
Follow these instructions to install ROS
- ROS Kinetic if you have Ubuntu 16.04.
- ROS Indigo if you have Ubuntu 14.04.
-
- Use this option to install the SDK on a workstation that already has ROS installed: One Line SDK Install (binary)
-
Download the Udacity Simulator.
The preferred method to run the project is to use docker, as it make sure every dependencies are properly installed. The following commands can be used to run the docker image:
run.shon linux or OSXrun.baton Windows
Alternatively, the following commands can be used to build and run the containers:
-
Create the volume used in the container
docker volume create sdcnd-capstone-volume -
Build the container Be sure to replace or set
$SDC_CAPSTONE_IMAGEto a proper namedocker build -f Dockerfile.builder -t $SDC_CAPSTONE_IMAGE . -
Run the container Be sure to replace or set
$SDC_CAPSTONE_IMAGEto a proper name
docker run --rm -it \
-p 4567:4567 \
-v sdcnd-capstone-volume:/app/ros \
-v $(pwd)/ros/src:/app/ros/src:ro \
-v $(pwd)/ros/launch:/app/ros/launch:ro \
-v $(pwd)/data:/app/data \
-v $(pwd)/resources/run.sh:/app/run.sh:ro \
--name sdcnd-capstone-runner \
$SDC_CAPSTONE_IMAGE /bin/bash
- Run the simulator
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
- 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