3 Techniques to Tackle Steep Turns and Varying Light Conditions

3 Techniques to Tackle Steep Turns and Varying Light Conditions

How Self Driving Cars perceive under varying conditions! ASelf driving car needs to perceive lane lines of different colours and under varying lighting conditions in order to detect lanes accurately.

A Self driving car needs to perceive lane lines of different colours and under varying lighting conditions in order to detect lanes accurately. It should also know the lane curvature, apart from speed and car dynamics, to determine the steering angle necessary to stay in the lane.

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Sample Image From Udacity’s Self Driving Car Nano-Degree course

We will look at a few techniques, self-driving cars can use to find lane lines under such varying conditions.


  1. Colour Spaces
  2. Sobel Operator
  3. Radius Of Curvature

Colour Spaces

RGB colour space works well for images with white lanes. It has limitations with other coloured lanes. Lets explore other colour spaces like HSV (Hue, Saturation, Value) and HLS (Hue, Lightness, Saturation ) etc.

Hue represents colour that is independent of any change in brightness. Lightness and Value are different ways to measure lightness or darkness of a colour. Saturation is the measure of colourfulness.

Image with yellow lane lines was split into RGB and HLS as seen below.

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Only R,G and S channels show high pixel intensities corresponding to yellow lane lines. Blue channel has zero yellow pixel intensity.

By choosing the best channel and the right colour thresholds for that channel, we can now identify yellow lane lines more accurately, as seen below.

robotics-automation computer-vision self-driving-cars machine-learning artificial-intelligence deep learning

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