Michigan Autonomous Vehicles

This software simulates autonomous vehicles within a ROS environment.

Documentation

STABLE — most recently tagged version of the documentation.

LATEST — in-development version of the documentation.

Installation Instructions

These instructions depend on your machine's configuration.

Step 1, Install Docker

Remove any old versions of docker if they are on your machine:

sudo apt-get remove docker docker-engine docker.io

Update the apt package index:

sudo apt-get update

Install the packages to allow apt to use a repository through HTTPS:

sudo apt-get install \
   apt-transport-https \
   ca-certificates \
   curl \
   software-properties-common

Add the official GPG key of Docker:

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -

Verify that the command below print out 9DC8 5822 9FC7 DD38 854A E2D8 8D81 803C 0EBF CD88:

sudo apt-key fingerprint 0EBFCD88

Tell apt to use the stable repository by running the command below:

sudo add-apt-repository \
   "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
   $(lsb_release -cs) \
   stable"

Update the apt package index and install Docker CE:

sudo apt-get update && apt-get install docker-ce

Check installation of docker:

docker run hello-world

Step 2, Update NVIDIA Driver

Use the CUDA 10.1 Toolkit to install CUDA. An example of using this toolkit follows.

After the download is complete, cd into your ~\Downloads folder and follow the installation instructions provided by the toolkit to install CUDA:

sudo dpkg -i $HOME/Downloads/cuda-repo-ubuntu1604-10-1-local-10.1.105-418.39_1.0-1_amd64.deb
sudo apt-key add /var/cuda-repo-<version>/7fa2af80.pub
sudo apt-get update
sudo apt-get install cuda

Note: After you follow the first instruction, the <version> in the second instruction will be provided. For instance, in this example:

$HOME/Downloads/cuda-repo-ubuntu1604-10-1-local-10.1.105-418.39_1.0-1_amd64.deb

Produces:

Selecting previously unselected package cuda-repo-ubuntu1604-10-1-local-10.1.105-418.39.
(Reading database ... 551128 files and directories currently installed.)
Preparing to unpack .../cuda-repo-ubuntu1604-10-1-local-10.1.105-418.39_1.0-1_amd64.deb ...
Unpacking cuda-repo-ubuntu1604-10-1-local-10.1.105-418.39 (1.0-1) ...
Setting up cuda-repo-ubuntu1604-10-1-local-10.1.105-418.39 (1.0-1) ...

The public CUDA GPG key does not appear to be installed.
To install the key, run this command:
sudo apt-key add /var/cuda-repo-10-1-local-10.1.105-418.39/7fa2af80.pub

Thus add the key as instructed, before proceeding with the final instructions.

Reboot your computer and verify that the NVIDIA graphics driver can be loaded

nvidia-smi

which should produce something like this

Mon Jun 10 08:59:09 2019       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.67       Driver Version: 418.67       CUDA Version: 10.1     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX TITAN   On   | 00000000:03:00.0  On |                  N/A |
| 34%   50C    P8    17W / 250W |    433MiB /  6080MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0      1895      G   /usr/lib/xorg/Xorg                            27MiB |
|    0      1965      G   /usr/bin/gnome-shell                          49MiB |
|    0      2943      G   /usr/lib/xorg/Xorg                           177MiB |
|    0      3103      G   /usr/bin/gnome-shell                          97MiB |
|    0      3511      G   ...uest-channel-token=13252725915974596027    76MiB |
+-----------------------------------------------------------------------------+

Step 3, Install NVIDIA-docker

If installed, remove NVIDIA docker 1.0:

docker volume ls -q -f driver=nvidia-docker | xargs -r -I{} -n1 docker ps -q -a -f volume={} | xargs -r docker rm -f
sudo apt-get purge -y nvidia-docker

Add the necessary repositories and update the apt package index and Install NVIDIA docker:

curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - && \
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) && \
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list && \
sudo apt-get update && \
sudo apt-get install -y nvidia-docker2 && \
sudo pkill -SIGHUP dockerd

Test NVIDIA docker installation:

docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi

Step 4, Install MAVs

1. Make a directory to store MAVs in, e.g., $HOME/Documents/workspace/MAVs. cd into that directory.

git clone https://github.com/JuliaMPC/MAVs

3. Build image

   sh build.sh

4. Test MAVs

First start Docker container in the MAVs folder:

./run.sh

Then, the most basic usage of MAVs is simply running the demos. For instance, demoA can be run as:

$roslaunch system demoA.launch

Tests

Unfortunately this software stack exceeds the time limit on Docker as well as Travis services (~45 min). So, while these services are configured, they cannot be utilized.
Docker Hub repo