hafizur-rahman / autonomous-car-capstone

Capstone Project for Self Driving Car Engineer Nanodegree Program. Integration of the code in a real self driving car

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.

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Project

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

Team

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	LinkedIn	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

Architecture

The project runs with ROS and is divided into the following modules :

• tl_detector uses the camera to detect the traffic lights' color
• twist_controller handles the control of the car
• waypoint_follower makes sure the car follow the trajectory
• waypoint_loader loads the route the car is going to follow
• waypoint_updater adapts the car's route to the situation (eg. traffic light)

Please use one of the two installation options, either native or docker installation.

Native 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.

• Dataspeed DBW

  • Use this option to install the SDK on a workstation that already has ROS installed: One Line SDK Install (binary)

• Download the Udacity Simulator.

Docker Installation

Install Docker

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.sh on linux or OSX
• run.bat on Windows

Alternatively, the following commands can be used to build and run the containers:

1. Create the volume used in the container docker volume create sdcnd-capstone-volume

2. Build the container Be sure to replace or set $SDC_CAPSTONE_IMAGE to a proper name docker build -f Dockerfile.builder -t $SDC_CAPSTONE_IMAGE .

3. Run the container Be sure to replace or set $SDC_CAPSTONE_IMAGE to 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

4. Run the simulator

Port Forwarding

To set up port forwarding, please refer to the instructions from term 2

Usage

1. Clone the project repository

git clone https://github.com/udacity/CarND-Capstone.git

2. Install python dependencies

cd CarND-Capstone
pip install -r requirements.txt

3. Make and run styx

cd ros
catkin_make
source devel/setup.sh
roslaunch launch/styx.launch

4. Run the simulator

Real world testing

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