2019FallTeam6

From MAE/ECE 148 - Introduction to Autonomous Vehicles
Revision as of 17:47, 29 November 2019 by Fall2019Team6 (talk | contribs) (Methodology)
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Introduction

Our project was to integrate OpenCV into donkeycar in order to try and improve training time and robustness of the model. We used OpenCV to pre-process images in order to extract edges and highlight the white and yellow lanes. We wanted to see if using those images as a combined 3-D image (edges, white lane, yellow lane) would improve training time and robustness of the model compared to the original donkeycar model which trained using RGB images.

Team Members

  • Cyrus Shen - ECE
  • Chenfeng Wu - ECE
  • Maximilian Stadler - CSE (UCSD Extension)
  • Isabella Franco - MAE

Mechanical Design

Camera Mount

Car Circuitry

ECE148 circuit.JPG

Integrating OpenCV

Project

Methodology

In order to compare our OpenCV model to the current donkeycar model, we trained both models on the same images. 6 separate models were trained, 3 for an indoor track and 3 for an outdoor track. The 3 indoor track models were trained with 1000, 5000, and 10000 images of the indoor track with the car going clockwise. The same was done for the outdoor track model except the images were taken from the outdoor track.

We tested each model based on the following criteria:

  1. Training time
  2. Completion of X clockwise laps on the indoor track
  3. Completion of X clockwise laps on the outdoor track
  4. Completion of X counterclockwise lap on the indoor track
  5. Completion of X counterclockwise lap on the outdoor track

Indoor Track Results

Outdoor Track Results