A monocular ranging algorithm based on track geometric features

Abstract Tram vehicles share roadway resources with other traffic participants, creating a significant risk of collision. Therefore, obstacle-detection technology is crucial for guaranteeing safe driving. Traditional obstacle ranging methods based on picture geometry sometimes rely on the road’s vanishing point or the known size of the obstruction to determine the camera’s pitch angle. Nevertheless, when the measurements of the unstructured road or obstruction are ambiguous, these methods often fail to determine the camera pitch angle. This research introduces a monocular ranging method derived from the geometric characteristics of the track. This study presents a monocular ranging method derived from the geometric characteristics of the track. First, a tram track detection model based on geometric moments is constructed, extracting track edge information from the color space and utilizing geometric moments to extract track features to obtain the starting point information of the track curve. Second, the camera pitch angle is determined by computing the lateral distance variation of the track, utilizing the starting point coordinates of the track curve and fixed track spacing. Finally, the YOLO approach is employed for obstacle identification, and the distance to obstacles is quantified using a developed obstacle range model grounded in image geometry. Static and dynamic ranging experiments were performed to assess the efficacy and resilience of the proposed ranging algorithm. In the static ranging experiment, the approach attained average errors of 1.32% for longitudinal ranging, 5.15% for lateral ranging, and 1.26% for straight-line ranging. Among these, the average errors in the longitudinal and straight-line ranges surpassed those of current techniques. In the dynamic ranging experiment, the average error in the straight-line range was 2.72%, further substantiating the effectiveness of this technique in practical applications.