Lane detection under artificial colored light in tunnels and on highways: an IoT-based framework for smart city infrastructure

Lane detection (LD) under different illumination conditions is a vital part of lane departure warning system and vehicle localization which are current trends in the future smart cities. Recently, vision-based methods are proposed to detect lane markers in different road situations including abnormal marker cases. However, an inclusive framework for driverless cars has not been introduced yet. In this work, a novel LD and tracking method is proposed for the autonomous vehicle in the IoT-based framework (IBF). The IBF consists of three modules which are vehicle board (VB), cloud module (CM), and the vehicle remote controller. The LD and tracking are carried out initially by the VB, and then, in case of any failure, the whole set of data is passed to CM to be processed and the results are sent to the VB to perform the appropriate action. If the CM detects a lane departure, then the autonomous vehicle is driven remotely and the VB would be restarted. In addition to the proposed framework, an illumination invariance method is presented to detect lane markers under different light conditions. The simulation results with real-life data demonstrate lane-keeping rates of 95.3% and 95.2% in tunnels and on highways, respectively. The approximate processing time of the proposed method is 31 ms/frame which fulfills the real-time requirements.

CNN based lane detection with instance segmentation in edge-cloud computing

At present, the number of vehicle owners is increasing, and the cars with autonomous driving functions have attracted more and more attention. The lane detection combined with cloud computing can effectively solve the drawbacks of traditional lane detection relying on feature extraction and high definition, but it also faces the problem of excessive calculation. At the same time, cloud data processing combined with edge computing can effectively reduce the computing load of the central nodes. The traditional lane detection method is improved, and the current popular convolutional neural network (CNN) is used to build a dual model based on instance segmentation. In the image acquisition and processing processes, the distributed computing architecture provided by edge-cloud computing is used to improve data processing efficiency. The lane fitting process generates a variable matrix to achieve effective detection in the scenario of slope change, which improves the real-time performance of lane detection. The method proposed in this paper has achieved good recognition results for lanes in different scenarios, and the lane recognition efficiency is much better than other lane recognition models.

Calculation Model and Rapid Estimation Method for Coal Seam Gas Content

Coalbed gas content is the most important parameter for forecasting and preventing the occurrence of coal and gas outburst. However, existing methods have difficulty obtaining the coalbed gas content accurately. In this study, a numerical calculation model for the rapid estimation of coal seam gas content was established based on the characteristic values of gas desorption at specific exposure times. Combined with technical verification, a new method which avoids the calculation of gas loss for the rapid estimation of gas content in the coal seam was investigated. Study results show that the balanced adsorption gas pressure and coal gas desorption characteristic coefficient (Kt) satisfy the exponential equation, and the gas content and Kt are linear equations. The correlation coefficient of the fitting equation gradually decreases as the exposure time of the coal sample increases. Using the new method to measure and calculate the gas content of coal samples at two different working faces of the Lubanshan North mine (LBS), the deviation of the calculated coal sample gas content ranged from 0.32% to 8.84%, with an average of only 4.49%. Therefore, the new method meets the needs of field engineering technology.