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A framework for Hough-based lane line detection with analytical assessment aided by CORDIC
P D Justin Climend Raj1, V Ravi2
1School of Electronics Engineering, Vellore Institute of Technology, Chennai, 600127, India.
Scientific Reports
|May 18, 2026
Summary
This study introduces a real-time lane detection framework using CORDIC technology, significantly improving processing speed for autonomous vehicles. The new method achieves high accuracy, even in challenging conditions, enhancing road safety.
Area of Science:
- Computer Vision
- Robotics
- Embedded Systems
Background:
- Accurate lane detection is crucial for autonomous vehicles and advanced driver-assistance systems.
- Traditional lane detection methods, like Hough Transform, are computationally intensive due to trigonometric operations.
Purpose of the Study:
- To develop a real-time lane detection framework using CORDIC technology.
- To overcome the computational challenges of traditional lane detection methods.
Main Methods:
- Implemented a pipeline combining region-of-interest masking, Canny edge detection, modified Hough Transform, and CORDIC.
- Utilized CORDIC's iterative rotation method to implement the polar line equation, reducing computational load.
Main Results:
- The CORDIC-enhanced method accurately detects multiple straight lane lines in real-time.
- Achieved a 98.72% correct detection rate in both clear and rain-blurred highway images.
- Demonstrated significant processing speed increases compared to standard algorithms.
Conclusions:
- Hardware-oriented computation and algorithmic optimization enable real-time automotive applications.
- The CORDIC-based lane detection is suitable for intelligent transportation systems.
- The proposed method offers a computationally efficient and accurate solution for lane detection.
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