In the advanced assisted driving device, machine vision technology was used to process the video of vehicles in front in real time to dynamically recognize and predict the posture and behavior of vehicle. Concerning low precision and large delay of this kind of recognition algorithm, a deep learning algorithm for vehicle behavior dynamic recognition based on Long Short-Term Memory (LSTM) was proposed. Firstly, the key frames in vehicle behavior video were extracted. Secondly, a dual convolutional network was introduced to analyze the feature information of key frames in parallel, and then LSTM network was used to sequence the extracted characteristic information. Finally, the output predicted score was used to determine the behavior type of vehicle. The experimental results show that the proposed algorithm has an accuracy of 95.6%, and the recognition time of a single video is only 1.72 s. The improved dual convolutional network algorithm improves the accuracy by 8.02% compared with ordinary convolutional network and increases by 6.36% compared with traditional vehicle behavior recognition algorithm based on a self-built dataset.

The self-driving mine locomotive needs to detect and locate pedestrians in front of it in the underground roadway in real-time. Non-visual methods such as laser radar are costly, while traditional visual methods based on feature extraction cannot solve the problem of poor illumination and uneven light in the laneway. To solve the problem, a pedestrian visual positioning algorithm for underground roadway based on deep learning was proposed. Firstly, the overall structure of the system based on deep learning network was given. Secondly, a multi-layer Convolutional Neural Network (CNN) for object detection was built to calculate the two-dimensional coordinates and the size of bounding box of pedestrians in visual field of the self-driving locomotive. Thirdly, the third-dimensional distance between the pedestrian in the image and the locomotive was calculated by polynomial fitting. Finally, the model was trained, verified and tested through real sample sets. Experimental results show that the accuracy of the proposed algorithm reaches 94%, the speed achieves 25 frames per second, and the distance detection error is less than 4%, thus efficient and real-time laneway pedestrian visual positioning is realized.

To meet the increasing demand of high-precision localization in coal mine, a set of underground positioning anchors and tags based on Ultra WideBand (UWB) communication were designed and implemented by applying high-precision wireless transceiver chip DW1000. Asymmetric Double Sided Two-Way Ranging (ADS-TWR) algorithm was used to improve the accuracy of ranging between the anchor and the tag, which effectively suppressed the error caused by clock drift. Aiming at the problem that a large amount of invalid communication was generated by the tag broadcasting request frame when starting the localization in the underground multi-anchor layout, a region determination strategy for tags based on ADS-TWR was proposed, which made a tag communicate with the anchors in its region only. At the same time, region abnormal self-checking and region correction mechanism for tags were introduced to ensure the efficient and stable operation of the system. In the coordinate analysis phase for tags, triangle centroid algorithm was used to further improve localization accuracy based on high-precision ranging and reduce localization processing time. Finally, the experimental results show that the localization accuracy of tags is within 15 cm, which meets the requirement of high-precision localization in underground coal mine.

To deal with the problem of the locomotive's dynamically wireless access to transmission network in the mine locomotive unmanned system, the Successive Interference Cancellation (SIC) region partition strategy was proposed. Firstly, the nonlinear region partition model was constructed in the Access Point (AP) communication coverage. Secondly, based on the theoretical derivation, the relationship between the number of region partitions and the AP communication coverage, and the relationship between the locomotive position and the transmission power were found. Finally, SIC region partition strategy was designed. The simulation results show that SIC region partition strategy can make one AP access three locomotives at the same time, and the overall optimization effect of locomotive's total passing time and AP coverage utilization increases at least 50%.