A Robust Lane Detection Algorithm Adaptable to Challenging Weather Conditions

I. Chen Sang, William R. Norris

Research output: Contribution to journalArticlepeer-review


Driver assistance systems and autonomous vehicle navigation have become important topics in vehicular technology. Among all of the functions, lane detection is one of the most important. A variety of approaches have been proposed in this field. While learning-based methods achieve impressive accuracy in detecting complex lane markings under clear daylight conditions, adapting these models to diverse weather conditions remains a challenge. On the other hand, geometric-based approaches require parameter tuning for different scenarios but require fewer computational resources. A self-tuned algorithm with high generalizability across diverse weather conditions is proposed in this paper. The algorithm integrates fuzzy logic-based adaptive functions with edge identification and line detection modules, enabling image adjustments in response to challenging weather conditions. The proposed tracking function utilizes previous detection results to fine-tune the selected Range of Interest (ROI), optimizing both accuracy and processing time. By incorporating these adaptive features into common geometric-based frameworks, the algorithm achieves higher detection rates compared to previous studies during challenging weather conditions. Furthermore, the proposed work exhibits better generalizability and significantly shorter processing time when compared to state-of-the-art learning-based models, as demonstrated through extensive testing on multiple datasets.

Original languageEnglish (US)
Pages (from-to)11185-11195
Number of pages11
JournalIEEE Access
StatePublished - 2024
Externally publishedYes


  • Adaptive
  • Fuzzy logic
  • Image color analysis
  • Image edge detection
  • Image processing
  • Lane detection
  • Lighting
  • Meteorology
  • Process control
  • Tuning
  • image processing
  • fuzzy logic
  • lane detection

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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