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Modular Package for Autonomous Driving (mPAD)

Developed a modular package for autonomous driving with advanced pre-processing methods, object detection, and integration with the DonkeyCar platform.

Modular Package for Autonomous Driving (mPAD)

As part of my senior project at Worcester Polytechnic Institute, I led the development of a Modular Package for Autonomous Driving (mPAD), designed to enhance autonomous vehicle capabilities.

Key Achievements

Advanced Pre-processing Methods

Implemented a sophisticated pre-processing method that reduced noise by 90% using a combination of techniques:

  • Grayscale transformation
  • Gaussian blur
  • Canny filter
  • Hough line transform

Additionally, I introduced artificial noise to facilitate easier pattern identification, improving the system's ability to recognize road features.

Object Detection with Parallel Processing

Incorporated object detection with parallel processing by employing a Convolutional Neural Network (CNN) model. This implementation achieved an impressive frame rate of 20 frames per second, enabling real-time detection capabilities essential for autonomous driving.

Integration with DonkeyCar

Successfully integrated the pre-processing and object detection components into the production code for the open-source platform DonkeyCar, enhancing its capabilities and performance.

Reliable Autonomous Operation

Achieved reliable autonomous driving after just 5 laps of manual driving, with robust object detection and comprehensive sensor data collection, demonstrating the effectiveness of the implemented solutions.

Technologies Used

  • Computer Vision
  • Convolutional Neural Networks (CNN)
  • Parallel Processing
  • DonkeyCar Platform
  • Image Processing Techniques
  • Sensor Data Integration