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Vehicle location deviation could easily occur in complex road network of open-pit mines, which seriously affect production vehicle route planning and intelligent scheduling.In this light, this study proposed a method for high-precision positioning and rectification of open pit minecars based on an improved HMM(Hiddden Markov Model).Specifically, this study clipped the road section by the complex road network map of the constructed open-pit mine, and cleaned the positioning trajectory data of the minecart, whose density was sparsed and segmented; a buffer zone was established to search for candidate road points of the trajectory, so as to improve the efficiency of the minecart positioning and correction under the complex road network; the HMM optimization model of positioning deviation correction was established by calculating the positioning observation probability and transition probability of the minecart.The optimal deviation correctionwas conducted in combination with the Viterbi algorithm to achieve high-precision positioning and deviation correction of the open-pit minecart.Results indicate that the method produces a better correction effect than the original HMM positioning correction method, the correction accuracy can reach 89.2 %, and the average correction time is only 0.055 s.This could effectively correct the positioning coordinates of open-pit mine vehicles under complex backgrounds.

Negative obstacles such as potholes and road collapses on unstructured roads in open-pit mining areas seriously affect the safe transportation of autonomous trucks. In this paper, we propose a real-time negative obstacle detection method for self-driving trucks in open-pit mines. By analyzing the characteristics of road negative obstacles in open-pit mines, a real-time target detection model based on the Yolov4 network was built. It uses RepVGG as the backbone feature extraction network, applying SimAM space and a channel attention mechanism to negative obstacle multiscale feature fusion. In addition, the classification and prediction modules of the network are optimized to improve the accuracy with which it detects negative obstacle targets. A non-maximum suppression optimization algorithm (CIoU Soft Non-Maximum Suppression, CS-NMS) is proposed in the post-processing stage of negative obstacle detection. The CS-NMS calculates the confidence of each detection frame with weighted optimization to solve the problems of encountering obscure negative obstacles or poor positioning accuracy of the detection boxes. The experimental results show that this research method achieves 96.35% mAP for detecting negative obstacles on mining roads with a real-time detection speed of 69.3 fps, and that it can effectively identify negative obstacles on unstructured roads in open-pit mines with complex backgrounds. [ABSTRACT FROM AUTHOR]