jiankang1991 / CarND-Capstone-T3-New

Capstone Project - Introduction

The System Integration project is the final project of the Udacity Self-Driving Car Engineer Nanodegree. In this project We have built ROS nodes to implement traffic light detection, PID control, and waypoint following. Initially this software system will be tested on a simulator, later, this will be deployed on Carla to autonomously drive it around a test track.

For more information about the project, see the project introduction [here](https://classroom.udacity.com/nanodegrees/nd013/parts/6047fe34-d93c-4f 50-8336-b70ef10cb4b2/modules/e1a23b06-329a-4684-a717-ad476f0d8dff/lessons/462c933d-9f24-42d3-8bdc-a08a5fc866e4/concepts/5ab4b122-83e6-436d-850f-9f4d26627fd9).

System Architecture Diagram

Team Intro

Team Name: CDriving

Team Members

• Bajrang Chapola (Project Contribution: Team Lead, PID Controller) - [e-mail]
• Nishant Rana (Project Contribution: Traffic Light Detection) - [e-mail]
• Jian Kang (Project Contribution: Train CNN model for traffic light detection) - [e-mail]
• Muhammad Al-Digeil (Project Contribution: Train CNN model for traffic detection) - [e-mail]
• Melanie Schmidt-Wolf (Project Contribution: Waypoint updater) - [e-mail]

System Installation

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

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

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

Traffic light detection based on SSD-MobileNetV2

The traffic light detection subsystem is based on a trained SSD-MobileNetV2 CNN model. The implementation details are under the link.