Vision-Based Algorithm for Precise Traffic Sign and Lane Line Matching in Multi-Lane Scenarios

Abstract

With the rapid development of intelligent transportation systems, lane detection and traffic sign recognition have become critical technologies for achieving full autonomous driving. These technologies offer crucial real-time insights into road conditions, with their precision and resilience being paramount to the safety and dependability of autonomous vehicles. This paper introduces an innovative method for detecting and recognizing multi-lane lines and intersection stop lines using computer vision technology, which is integrated with traffic signs. In the image preprocessing phase, the Sobel edge detection algorithm and weighted filtering are employed to eliminate noise and interference information in the image. For multi-lane lines and intersection stop lines, detection and recognition are implemented using a multi-directional and unilateral sliding window search, as well as polynomial fitting methods, from a bird’s-eye view. This approach enables the determination of both the lateral and longitudinal positioning on the current road, as well as the sequencing of the lane number for each lane. This paper utilizes convolutional neural networks to recognize multi-lane traffic signs. The required dataset of multi-lane traffic signs is created following specific experimental parameters, and the YOLO single-stage target detection algorithm is used for training the weights. In consideration of the impact of inadequate lighting conditions, the V channel within the HSV color space is employed to assess the intensity of light, and the SSR algorithm is utilized to process images that fail to meet the threshold criteria. In the detection and recognition stage, each lane sign on the traffic signal is identified and then matched with the corresponding lane on the ground. Finally, a visual module joint experiment is conducted to verify the effectiveness of the algorithm.