Self Driving Lane Detection Car Using Python and Opencv on Raspberry Pi

EasyChair Preprint 5030

Abstract

1. For vehicles to be able to drive by themselves, they need to understand their surrounding world like human drivers, so they can navigate their way in streets, pause at stop signs and traffic lights, and avoid hitting obstacles such as other cars and pedestrians.

2.  Based on the problems encountered in detecting objects by autonomous vehicles an effort has been made to demonstrate lane detection using OpenCV library.

3. In this project, we present a perception algorithm that is based purely on vision or camera data. We focus on demonstrating a powerful end-to-end lane detection method using contemporary computer vision techniques for self-driving cars.

4. We first present a minimalistic approach based on edge detection. We then propose an improved lane detection technique based on perspective transformations and histogram analysis. In this latter technique, both straight and curved lane lines can be detected.

Keyphrases: Hough transform, OpenCV Library., Python language, Raspberry Pi, histogram analysis, lane detection, threshold

@booklet{EasyChair:5030,
  author    = {Venkata Shiva Prasad Nannuri and Sai Santosh Kumar Mantha and Nikhilesh Pottipally and Sai Krishna Kodati and Suresh T.V Kumar},
  title     = {Self Driving Lane Detection Car Using Python and Opencv on Raspberry Pi},
  howpublished = {EasyChair Preprint 5030},
  year      = {EasyChair, 2021}}