DonkeyCar installation: RC car

Connecting to your RC via SSH

To connect and work with your RC throughout the rest of the project, you'll need two things:

  • An SSH client
  • The IP address of your RC

SSH Clients:

If you're using Linux or a Mac, you're all set. They come with a SSH client pre-installed, and you just need to open up a terminal and type:

ssh username@ipAddress

If you're using Windows, you need to install one. I'd recommend using MobaXTerm:

  • Download the installer or the portable version and install/unpack it

  • To SSH to a device:

    • Open MobaXTerm

    • Press on the Start local terminal button

    • ssh username@ipAddress

SSH via Ethernet:

If your RC is connected to your network via an ethernet cable, you should be able to find your IP address:

  • Through your Router/Gateway interface, by looking at the DHCP leases;

  • Or by connecting your Nano to a monitor, keyboard and mouse, opening up a terminal and writing:

    ip addr show

SSH via WiFi:

I would highly recommend this approach, so you can connect your RC to a WiFi network and take it and your laptop with you and connect to it on the fly.

To connect it to a WiFi network, you either need to first SSH into it over ethernet, or connect it to a monitor, keyboard and mouse, and do the following:

  • Connect to a WiFi network:

    nmcli device wifi connect YOUR-SSID password YOUR-PASSWORD
  • Or make the Jetson into a hotspot so you can connect your laptop to it:

    nmcli dev wifi hotspot ifname wlan0 ssid HOTSPOT-SSID password HOTSPOT-PASSWORD

What I like to do is connect it to both my home network, and a hotspot on my mobile phone, so I can use it anywhere and still have Internet access on both it and my laptop.

To do so, I use the nmcli autoconnect.priority property, so my home network has a higher priority than my phone hotspot, in case I forget to turn it off while I'm at home, so it doesn't eat up my data plan.

You can find all of your network connections saved in /etc/NetworkManager/system-connections/, which you can open up with a text editor and edit the autoconnect.priority property for each network. The higher the integer you assign to it, the higher the priority.

As an example, the network connection profile for my hotspot looks something like:

autoconnect_priority=2 # The home network has a priority of 3, in my case

If your Nano keeps dropping the connection for some reason, try disabling the power saving mode found in /etc/NetworkManager/conf.d, using a text editor.

Also, I assumed your Nano already has the nmcli or the NetworkManager utility installed, since it, at the time of writing, comes pre-installed with any Ubuntu distro. If, for some reason, you don't have it, you can install it using sudo apt install network-manager.

After connecting your Nano to a WiFi network you want, find out its IP Address by opening up a terminal and typing:

ip addr show

Now you can use your SSH client and SSH into the Nano, type in your username and password and you're ready to follow the rest of the tutorial.


Open up a terminal on your Nano and install the following dependencies:

sudo apt-get update
sudo apt-get upgrade
sudo apt-get install build-essential python3 python3-dev python3-pip libhdf5-serial-dev hdf5-tools nano ntp

Set up a Virtual Env

# Install the venv package
pip3 install virtualenv
python3 -m virtualenv -p python3 env --system-site-packages
# Activate the venv automatically at boot
echo "source env/bin/activate" >> ~/.bashrc
source ~/.bashrc

Compiling and installing OpenCV

First, since OpenCV needs more than 4GB of RAM to be built from source, and our Jetson Nano just doesn't have that much RAM, we have to define some swap space to prevent it from going bonkers while compiling it:

# Allocates 4G of additional swap space at /var/swapfile
sudo fallocate -l 4G /var/swapfile
# Permissions
sudo chmod 600 /var/swapfile
# Make swap space
sudo mkswap /var/swapfile
# Turn on swap
sudo swapon /var/swapfile
# Automount swap space on reboot
sudo bash -c 'echo "/var/swapfile swap swap defaults 0 0" >> /etc/fstab'
# Reboot
sudo reboot

Now, we need to get all the prerequisites needed to build OpenCV from source:

# Update
sudo apt-get update
sudo apt-get upgrade
# Pre-requisites
sudo apt-get install build-essential cmake unzip pkg-config
sudo apt-get install libjpeg-dev libpng-dev libtiff-dev
sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
sudo apt-get install libxvidcore-dev libx264-dev
sudo apt-get install libgtk-3-dev
sudo apt-get install libatlas-base-dev gfortran
sudo apt-get install python3-dev

Okay, let's download the source code for OpenCV which we'll be building it from:

# Create a directory for opencv
mkdir -p projects/cv2
cd projects/cv2

# Download sources
wget -O
wget -O

# Unzip

# Rename
mv opencv-4.1.0 opencv
mv opencv_contrib-4.1.0 opencv_contrib

Also we'll need numpy in our virtual environment for this to work:

# Install Numpy
pip install numpy

We also need to make sure CMake correctly generates the OpenCV bindings for our virtual environment:

# Create a build directory
cd projects/cv2/opencv
mkdir build
cd build

# Setup CMake
    -D CMAKE_INSTALL_PREFIX=/usr/local \
    # Contrib path
    -D OPENCV_EXTRA_MODULES_PATH=~/projects/cv2/opencv_contrib/modules \
    # Your virtual environment's Python executable
    # You need to specify the result of echo $(which python)
    -D PYTHON_EXECUTABLE=~/env/bin/python \
    -D BUILD_EXAMPLES=ON ../opencv

The cmake command shows a summary of its configuration, and you should make sure that the Interpreter is set to the Python executable of your virtual environment, not the base OS one.

Now, to compile the code from the build folder, run the following:

make -j2
# Install OpenCV
sudo make install
sudo ldconfig

This will take a while. And by a while, I mean: Go grab a cup of coffee and watch a TV Show or a movie or something while.

Now we just need to link it to our virtual environment:

  • cd to: /usr/local/lib/python[YOUR.VERSION]/site-packages/cv2/python[YOUR.VERSION] and do ls to find out the exact name of the .so we built.

  • It should look something like: cv2.cpython-[YOURVERSION]m-[***]

  • Rename it to mv

  • And finally:

    • # Go to your virtual environments site-packages folder
      cd ~/env/lib/python[YOUR.VERSION]/site-packages/
      # Symlink the native library
      ln -s /usr/local/lib/python[YOUR.VERSION]/site-packages/cv2/python-[YOUR.VERSION]/

To make sure everything works as it should, run:

import cv2

# Should print 4.1.0

Install DonkeyCar

First, go to a directory where you'd like your stuff to be:

# Probably
cd ~/projects

Install the latest Donkey from GitHub:

# Clone it from GitHub
git clone
cd donkeycar
# Checkout the master branch
git checkout master
pip install -e .[nano]
pip install --extra-index-url tensorflow-gpu==1.13.1+nv19.3