Self driving piCar

Lanes detection

we use the following preprocessing pipeline:

• Load in RGB color space
• Perform HSV color space transform
• Create a binary mask by thresholding
• Apply Gaussian blur on mask
• Apply Canny filter
• Crop top half image
• Apply Probabilistic Hough Transform
• Compute centroids and mean direction on patches
• Compute weighted polynomial interpolation over centroids
• Compute steering angle

Results

Few comments:

• We removed the blur since with the speed there is already blur and actually the magnitude parameter when using canny filter needs to be tuned accordingly.

• Since the steering angle computed is noisy we smooth it by applying the following update:

theta_new <- alpha * theta_new + (1-alpha) * theta_old

• The camera provided with the car has only 120 degree angle view, which is a bit short. In the reference https://github.com/dctian/DeepPiCar we followed they use a 170 degree angle camera and noticed significant improvements.

• Black lanes are very sensitive to illumination and choosing colored lanes would probably improve results.

• Our front wheels are not yet properly calibrated and even though steering angle is smoothly updated the wheels tend to have too much angle when turning left side which slows down the car

Car view

Note: The steering angle indicated by text is smoothed while the steering angle represented by the red line is the raw current steering angle.

Outside view