Braking distance measurement between unmanned rollers and pavers based on object detection and inverse perspective mapping

During the process of enabling rollers and pavers with intelligence, the accurate measurement of the braking distance between these two machines impacts on the timely compaction of asphalt mixtures and the construction safety. Although unmanned rollers are equipped with obstacle detectors to detect the braking distance, these detectors can only mechanically detect obstacles without the ability to identify them. To solve this problem, a novel method based on object detection and inverse perspective mapping (IPM) is proposed to measure the braking distance between unmanned rollers and pavers. First, YOLOv12 is employed to identify the paver. Second, IPM is applied to the object detection results to generate the inverse perspective image. After that, the pixel coordinates of the object detection box are extracted from the inverse perspective image. Finally, combined with the proposed distance measurement model and the distance scale factor, the braking distance can be accurately measured. The experimental results show that the proposed distance measurement method can accurately and rapidly measure the braking distance. The relative error is less than 1.54%, and the processing time is 76.9ms. The proposed method can fully meet the requirements of asphalt road construction.