Lane Detection in Adverse Visibility Conditions

Team Members

• Rohit Kumar Sharma
• Varun Batra
• Vibhor Goel

Abstract

Given the fact that we are headed towards autonomous driving vehicles, lane detection becomes one of the keyaspects for it. Although, there has been some work in the domain of Lane Detection [Lee and Moon, 2018], [Wang et al., 2018], [Leng and Chen, 2010], real time implementation of robust lane detection algorithms is still missing for adverse visibility conditions. In fact, Escanilla et al [Hancock and Escanilla, 2018] at UW-Madison have done some novel work in the domain of lane detection last year. Over the years, several works on detection of lanes in adverse conditions from image scenes [Shibata et al., 2014], [Fu et al., 2017], [Shen et al., 2018], [Li et al., 2017] have been proposed. We have researched over the task of lane detection in conditions such of glare, provide assistance in autonomous driving vehicles.

Table of Contents:

• Introduction
  • Motivation
  • Spatial Convolutional Neural Network
• Methodology
  • Adverse Condition - Glaze
    1. DeGlazing
    2. SCNN + DeGlaze Training
  • Testing SCNN + DeGlaze
• Results
  • Evaluation Metric
  • Results on Glaze Subset
• Conclusion
  • Challenges
  • Learnings
  • Future Work
• References

Other Material:

• Project Proposal
• Midterm Report
• End term Presentation
• Source Code