AutoRCCar

See self-driving in action (Youtube)

A scaled down version of self-driving system using a RC car, Raspberry Pi, Arduino and open source software. The system uses a Raspberry Pi with a camera and an ultrasonic sensor as inputs, a processing computer that handles steering, object recognition (stop sign and traffic light) and distance measurement, and an Arduino board for RC car control.

Dependencies

• Raspberry Pi:
  • Picamera
• Computer:
  • Numpy
  • OpenCV
  • Pygame
  • PiSerial

About

• raspberrt_pi/
  • stream_client.py: stream video frames in jpeg format to the host computer
  • ultrasonic_client.py: send distance data measured by sensor to the host computer
• arduino/
  • rc_keyboard_control.ino: acts as a interface between rc controller and computer and allows user to send command via USB serial interface
• computer/
  • cascade_xml/
    • trained cascade classifiers xml files
  • chess_board/
    • images for calibration, captured by pi camera
  • training_data/
    • training image data for neural network in npz format
  • testing_data/
    • testing image data for neural network in npz format
  • training_images/
    • saved video frames during image training data collection stage (optional)
  • mlp_xml/
    • trained neural network parameters in a xml file
  • rc_control_test.py: drive RC car with keyboard (testing purpose)
  • picam_calibration.py: pi camera calibration, returns camera matrix
  • collect_training_data.py: receive streamed video frames and label frames for later training
  • mlp_training.py: neural network training
  • mlp_predict_test.py: test trained neural network with testing data
  • rc_driver.py: a multithread server program receives video frames and sensor data, and allows RC car drives by itself with stop sign, traffic light detection and front collision avoidance capabilities

How to drive

1. Flash Arduino: Flash “rc_keyboard_control.ino” to Arduino and run “rc_control_test.py” to drive the rc car with keyboard (testing purpose)

2. Pi Camera calibration: Take multiple chess board images using pi camera at various angles and put them into “chess_board” folder, run “picam_calibration.py” and it returns the camera matrix, those parameters will be used in “rc_driver.py”

3. Collect training data and testing data: First run “collect_training_data.py” and then run “stream_client.py” on raspberry pi. User presses keyboard to drive the RC car, frames are saved only when there is a key press action. When finished driving, press “q” to exit, data is saved as a npz file.

4. Neural network training: Run “mlp_training.py”, depend on the parameters chosen, it will take some time to train. After training, parameters are saved in “mlp_xml” folder

5. Neural network testing: Run “mlp_predict_test.py” to load testing data from “testing_data” folder and trained parameters from the xml file in “mlp_xml” folder

6. Cascade classifiers training (optional): trained stop sign and traffic light classifiers are included in the "cascade_xml" folder, if you are interested in training your own classifiers, please refer to OpenCV documentation and this great tutorial by Thorsten Ball

7. Self-driving in action: First run “rc_driver.py” to start the server on the computer and then run “stream_client.py” and “ultrasonic_client.py” on raspberry pi.