CarND Capstone Project: System Integration

Team

Name	E-Mail	Github	Role
Benjamin Söllner	benjamin.soellner@daimler.com	benjaminsoellner	tl_detector, tf graph integration, Azure integration VM with GPU
Bernhard Rode	mail@bernhardrode.de	bernhardrode	wp_updater, dbw_node
Carsten Mielenz	carsten.mielenz.france@gmail.com	carstenMIELENZ	dbw_node, twist_controller
Johannes Hallier	johannes.hallier@gmail.com	jhallier	tl_classifier, tf graph integration
Tianran Wang	tr.wang236@gmail.com	TrW236	Project Lead, wp_updater, tl_detector, dbw_node

System overview

Overview

There are 3 ROS modules modified:

• waypoint_updater

• tl_detector

• twist_controller

Waypoint Updater

Traffic Light Detector

Twist Controller

Udacity

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.

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

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 (a bag demonstraing the correct predictions in autonomous mode can be found here)
2. Unzip the file

unzip traffic_light_bag_files.zip

3. Play the bag file

rosbag play -l traffic_light_bag_files/loop_with_traffic_light.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