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Udacity CarND Capstone Project

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Udacity - Self-Driving Car NanoDegree

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.

Individual Submission

Name Udacity Account Email
Marko Dragojevic [email protected]

Overview

Implemented functionality overview is given in this section

Waypoint Updater

Waypoint Updater node implements functionality of using predefined list of waypoints and information about car's current position to determin to which waypoint car should move next. In addition to this, this node is also responsible for 'issuing' stop commands on traffic lights, once it has been notified of red traffic light existance.

Drive By Wire (DBW)

This node implements functionality of 'drive-by-wire'. It is responsible for direct communication with vehicle's platform. For control of throttle PID controller is used. YawController is used for calculating desired steering angle.

Traffic Light Detection/Classification

Final part of this project is traffic light detector/classifier node which is responsbile for publishing target stop waypoint index of next red traffic light. This information is then used by waypoint updater in order to generate stopping trajectory.

In order to detect which state of traffic light is currently present pretrained tensorflow model (SSD_Mobilenet version 11.6.17).

Tensorflow object detection API and Tensorflow Model Zoo was for purpose of retraining/evaluating performances of mentioned model.

Existing dataset was used to retrain the model and it can be found here.

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

  • 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 "uWebSocketIO Starter Guide" found in the classroom (see Extended Kalman Filter Project lesson).

Usage

  1. Clone the project repository
git clone https://github.com/udacity/CarND-Capstone.git
  1. Install python dependencies
cd CarND-Capstone
pip install -r requirements.txt
  1. Make and run styx
cd ros
catkin_make
source devel/setup.sh
roslaunch launch/styx.launch
  1. 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
  1. Play the bag file
rosbag play -l traffic_light_bag_file/traffic_light_training.bag
  1. Launch your project in site mode
cd CarND-Capstone/ros
roslaunch launch/site.launch
  1. Confirm that traffic light detection works on real life images

Other library/driver information

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:

Simulator Carla
Nvidia driver 384.130 384.130
CUDA 8.0.61 8.0.61
cuDNN 6.0.21 6.0.21
TensorRT N/A N/A
OpenCV 3.2.0-dev 2.4.8
OpenMP N/A N/A

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