This document presents algorithms for lane line and obstacle detection in outdoor environments to assist autonomous vehicles. It uses image processing techniques like grayscale conversion, thresholding, binary morphology and Hough transforms. The lane line algorithm works well in illuminated environments but requires more adaptive parameters. The obstacle detection accurately finds barrels but is computationally expensive. Overall, the algorithms aim to improve vision systems for self-driving cars.

1. Image Analysis for Lane and Obstacle Identification and Outdoor Environments
Herta Llusho, Eva berndt
ELEE 5920: Digital Image Processing
Obstacle Detection
This lane line algorithm is not ready
to be used for autonomous driving.
There are a few parameters that
have to be re-evaluated and made
more adaptive to all sorts of
environments. This algorithm is
effective in well illuminated
environments.
The obstacle algorithm is extremely
accurate in finding the barrels but
requires more computational
power.
Lane Line Detection
ABSTRACT: This is an algorithm to automatically help identify lane lines and obstacles, specifically orange barrels. It helps to improve illumination and contrast of images as well. It deals with grayscales,
thresholding, binary morphology and hough transform to determine classification. This task is critical for the development of safe and effective self-driving or autonomous vehicles.
Grayscale Images
Binary Images
ANDed Image
Left and Right Division Hough Transform
Conclusion