AI Autonomous Driving Vehicle Training Equipment AutoCar III G

1- AI Deep Learning platform of NVIDIA.
2- Motor, encoder, and sensor control with a controller.
3- IoT connectivity applications
4- DToF lidar for SLAM.
5- Supports CUDA-based PyTorch and Tensorflow artificial intelligence framework
6- Supports Blockly.
7- Supports ROS2

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Description

  • On-Device AI Autonomous Driving Vehicle Training Equipment
  • High-performance on-device AI platform of NVIDIA is adopted for the Brain Board
  • Built-in high-resolution wide-angle camera for deep learning-based autonomous driving practice
  • Built-in 9-axis high-precision IMU sensor for path tracking and vehicle posture control
  • Built-in high-precision serial servomotor for more accurate steering control
  • Built-in DC motor with encoder and high-efficiency dual motor driver to increase driving accuracy
  • Motor, encoder, and sensor control with a controller equipped with a high-performance MCU for precise control of the driving unit
  • Connect brain board and controller with highly reliable CAN FD communication
  • Built-in Gigabit Ethernet, dual-band Wi-Fi, and Bluetooth for IoT connectivity applications
  • Built-in digital microphone and speaker for voice recognition and audio playback
  • Built-in power path management circuit enabling practice even while the battery is charging
  • Indoor or indoor/outdoor DToF lidar for SLAM and path planning applications
  • Selectable sensor pack with built-in breadboard to use various IoT sensor modules
  • Selectable touch display to implement GUI-based user interface
  • Provides high-level Pop Library to help focus on implementing autonomous driving
  • Supports autonomous vehicle applications based on robot standard middleware ROS2 and Pop Library
  • Supports CUDA-based PyTorch and Tensorflow artificial intelligence framework
  • Supports web browser-based Google block coding platform (Blockly)
  • Supports pre-set integrated development environment based on Visual Studio Code for professional application development
  • Provides learning contents for self-driving cars based on deep learning

Operation Program

List Specifications
Linux OS Desktop X-Server, Openbox, LightDM, Tint2, blueman, network-manager, conky
CLI Zsh, Tmux, Peco, powerlevel9k theme, Powerline fonts, Powerline fonts
Tool Chain GCC, JDK, Node JS, Python3, Clang
Connectivity Mosquitto(MQTT), Bluez, mtr, nmap, iptraf, Samba, Blynk Server, Remote Desktop Server
Multimedia portaudio, sox, OpenCV 4, Google Assistant
Data Science & AI Python3, Numpy, Matplotlib, sympy, Pandas, Seaborn, Scipy, Gym, Scikit-learn, tensorflow, Keras
Middleware ROS2, Rviz2, RQt, ament, RTPS, Fast DDS, TF2
Pop Library Output Object Led, Laser, Buzzer, Relay, RGBLed, DCMotor, StepMotor, OLed, PiezoBuzzer, PixelDisplay, TextLCD, FND, Led Bar
Input Object Switch, Touch, Reed, LimitSwitch, Mercury, Knock, Tilt, Opto, Pir, Flame, LineTrace, TempHumi, UltraSonic, Shock, Sound, Potentiometer, Cds
SoilMoisture, Thermistor, Temperature, Gas, Dust, Psd, Gesture
Multimedia AudioPlay, AudioPlayList, AudioRecord, Tone, SoundMeter
Voice Assistant GAssistant, create_conversation_stream
AI Linear Regression, Logistic Regression, Perceptron, ANN, DNN, CNN, DQN, Object Follow, Track Driving, YOLO
PC linkage development environment Jupyter Lab Python3 and Cling support, IPython Widgets, Terminal support, Pop Library support
Visual Studio Code Insiders Remote SSH, Python3 and Debugging support, Terminal support, Pop Library support

Block-Based Programming

Training Contents

DDS/RTPS Network-Based Autonomous Driving Vehicle Control in ROS2 Environment

  • WSL2-Based Linux Development Environment
  • Understanding Python Syntax for ROS2
  • Understanding Network Programming for ROS2
  • ROS2 Installation and Environment Configuration
  • Understanding Node, Topic, Service, and Parameter Action
  • ROS2 Build Environment
  • Publisher and Subscriber Nodes
  • Services and Customized Interface
  • Actions and Multi-Node
  • Launch and Multi-Execution
  • Advanced ROS2

Deep Learning-Based Autonomous Driving Technology

  • WSL2-Based Linux Development Environment
  • Supervised Learning and Unsupervised Learning
  • Linear Regression and Logistic Regression
  • ANN, DNN, CNN Basics
  • Understanding Machine Learning Framework
  • High Speed Multidimensional Matrix Library
  • Time Series, Table Data Analysis Library
  • Data Visualization Library
  • Overview of Autonomous Driving Technology
  • Basic Driving and Remote Control
  • Collision Prevention and Follow Object
  • Transfer Learning
  • Advanced Autonomous Driving

Layout

Components

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