Understand the existing system framework

• For the setup of Jetracer hardware it can be easily done following the manual [1], [[2]] and assemble it [3] .

• For Jetracer use the jetpack 4.6.1 [4] which is supported by MATLAB latest version.

• For Software setup firstly the Jetracer which has the Jetracer AI Kit on SD card to set it up and all the config. which the full process can be found in [5].

• MATLAB also is been installed on the local device to operate and manage the Jetracer. Then the addon package of nvidia-jetson is also installed for the hardware package. Various other package also been installed for this project such as Deep-learning, computer-vision, also gpu-computing.

Connection to the MATLAB & JETRACER

• USE Jetpack 4.6.1 and MATLAB version 2019b or 2020a.
• Connect JETRACER with a computer with HDMI, mouse, ethernet cable and keyboard.
• It will bring default Linux version and keep procced to setup. Keep username = jetracer & password = jetson during setup.
• After setup open the command window and give command ifconfig to get the Ip address of Jetracer.
• If connection is via WLAN the Ip will change and change accordingly.
• Connect the Ip to MATLAB using obj = jetson(deviceaddress,username,password) in MATLAB.
• Then it will give those command like in section below Herausforderungen.
• To fix those write in Linux terminal sudo apt-get update sudo apt-get install libsdl1.2-dev it will install sdl1.2 version.
• Open the terminal and copy this command :

  cd ~/Downloads
     # Install the CUDA repo metadata that you downloaded manually for L4T
       sudo dpkg -i cuda-repo-l4t-r19.2_6.0-42_armhf.deb
     # Download & install the actual CUDA Toolkit including the OpenGL toolkit from NVIDIA. (It only downloads around 15MB)
       sudo apt-get update
     # Install "cuda-toolkit-6-0" if you downloaded CUDA 6.0, or "cuda-toolkit-6-5" if you downloaded CUDA 6.5, etc.
       sudo apt-get install cuda-toolkit-6-5
     # Install the package full of CUDA samples (optional)
       sudo apt-get install cuda-samples-6-5
     # Add yourself to the "video" group to allow access to the GPU
       sudo usermod -a -G video $USER
  

then add :

 
    echo "# Add CUDA bin & library paths:" >> ~/.bashrc
    echo "export PATH=/usr/local/cuda/bin:$PATH" >> ~/.bashrc
    echo "export LD_LIBRARY_PATH=/usr/local/cuda/lib:$LD_LIBRARY_PATH" >> ~/.bashrc
    source ~/.bashrc

Verify that the CUDA Toolkit is installed on your device: nvcc -V .

Also add the code according this photo to change the PATH in the of .bashrc file. can edit using nano ~\.bashrc command.

• And it will now connect without any error with Cuda10.2 version.