A framework for Hough-based lane line detection with analytical assessment aided by CORDIC

Accurate and efficient lane detection is required for the effective operation of autonomous vehicles and advanced driver-assistance systems need it for their functioning. Traditional methods frequently use computationally demanding trigonometric operations, which are particularly challenging during line detection using the Hough Transform. One of the focused areas of this study is a real-time lane detection framework based on CORDIC technology. The CORDIC system operates as an iterative process that performs fundamental mathematical functions to enable its implementation on embedded platforms, whereas the matrix Mactor usage of CORDIC iterative methods presents an alternative to regular sine and cosine computations. The proposed pipeline implementation combines region-of-interest masking with Canny edge detection, modified Hough Transform, and CORDIC methods to detect multiple straight lane lines. The CORDIC implements the polar line equation using an iterative rotation method, thereby minimizing the computational requirements. The method can accurately extract multiple lane lines while significantly increasing the processing speed, as shown in the experimental results for both clear and rain-blurred highway images. The CORDIC-enhanced method shows advantages over standard algorithms through timing benchmarks, accumulator space visualizations, and performance metrics, which display the complete results of in-depth comparisons. This study demonstrates how hardware-oriented computation combined with algorithmic optimization enables real-time automotive applications and intelligent transportation systems to scale while achieving a correct rate of 98.72%.