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Automatic detection of railroad infrastructure using Mobile Laser Scanning systems is a key technology for both advanced rail driver assistance and intelligent track maintenance. The recent research into railway facility extraction or condition monitoring usually relies on high-density point cloud dataset with known sensor parameters but ignores the processing performance in actual deployment and has high requirements for the acquisition device. To address these limitations, a novel rail extraction algorithm is proposed for processing Mobile Laser Scanning data in real time during acquisition, which could extract rail features by a hierarchical coarse-to-fine method with basic structural parameters. Using the geometric and global statistical characteristics of rail in raw data, a new rail descriptor is defined based on the quantitative statistics of points satisfying height difference in generalized local neighborhood. The approach is evaluated experimentally by a simulated real-time acquisition data and compared with a reference method. The experimental results show that the proposed algorithm has a finer extraction effect and good real-time performance. [ABSTRACT FROM AUTHOR]

To solve the problem of trajectory tracking lateral control in autonomous driving technology, a model predictive control (MPC) controller trajectory tracking lateral control method combined with a deep deterministic policy gradient algorithm (DDPG) is proposed in this paper. This method inputs the real-time state of the vehicle into DDPG to achieve real-time automatic optimization of the prediction time domain and control time domain parameters of the MPC controller, and then affects the specific performance of the MPC controller in trajectory tracking lateral control. Specifically, the state space, action space, and reward function of DDPG are defined, and the automatic driving trajectory tracking lateral controller is designed in combination with the vehicle dynamics model. To reduce the exploration space of DDPG and improve the training efficiency of the entire model, the technique of advantage-disadvantage experience separation and extraction is introduced. Finally, the proposed method was trained and verified in various scenarios, and compared with two other lateral control methods for autonomous driving. The results showed that the learning and training time of the trajectory tracking lateral control method based on DDPG-MPC was shorter than that of the DDPG-based method, and the evaluation indicators in the trajectory tracking control process were better than those of the DDPG-based method and original MPC-based method. [ABSTRACT FROM AUTHOR]

Predicting the future trajectories of multiple pedestrians in certain scenes is critical for autonomous moving platforms (like, self‐driving cars and social robots). In this paper, we propose a novel Generative Adversarial Network model with Transformers, which simulates the pedestrian distribution to capture the uncertainty of the predicted paths and generate more reasonable future trajectories. The design of our method includes a generator and a discriminator. The generator mainly contains an encoder, a decoder, and a prediction module. Specifically, the encoder and the decoder comprise multihead convolutional self‐attention to learn the sequence of historical movement, and the prediction module incorporates the Mish Feed‐Forward Network to yield the predicted target. The discriminator takes both the predicted paths and ground truth as input, classifies them as socially acceptable or not. Experimental results show that the proposed method consistently boosts the performance of trajectory forecasting, and our framework surpasses several existing baselines by evaluating the results on various data sets. Code is available at https://github.com/lzz970818/Trajectory-Prediction. [ABSTRACT FROM AUTHOR]

Due to the influence of weather, light, season and angle changes, the appearance of objects changes visually, which makes it difficult for unmanned vehicles that rely on visual positioning to complete their positioning work. This paper proposes a coordinated positioning method that is composed of semantic information and geometric relationships distribution (GRD), which improves the robustness of unmanned vehicle location under the above conditions. First, we improved the FAST-SCNN semantic segmentation network and replaced its fully connected layer with the conv4-3 module to prevent the spatial information of the image from being lost in the fully connected layer. At the same time, the conv4-3 layer contains the richest semantic information, we use image semantic content to create a dense and prominent scene description. These prominent descriptions were learned from a large data set of perceptual changes. The method can accurately segment geometrically stable image regions. We combine the characteristics of these highlighted areas with the existing overall representation to produce a more robust scene descriptor. Second, a method is designed to integrate the matching of semantic labels and geometric distribution relations, which is a new closed loop location recognition label and landmark map. The geometric pair relationship between the ground marks is encoded as a continuous probability density function, the GRD function, which is expressed by a Laguerre polynomial and Fourier series basis expansion. This orthogonal basis representation allows for efficient computation of rotation and translation invariants, which are used to compare signatures and search for potential loop closure candidates. Finally, we evaluate our method with some of the most advanced algorithms, such as OpenSeqSLAM, AlexNet and VSO, to demonstrate its advantages. The experimental results for representative data sets show that the method based on Fast-SCNN is superior to other methods. [ABSTRACT FROM AUTHOR]

Introduction: This review and meta-analysis of randomized controlled trials (RCTs) assessed the side effects and efficacy of esketamine combined with propofol in procedural sedation and analgesia., Evidence Acquisition: The PubMed, MEDLINE, Embase and Cochrane Library databases were searched for relevant studies occurring between their inceptions and September 2022. The primary outcome was the incidence of sedation-related adverse events. Secondary outcomes included recovery time, total consumption of propofol and body movement., Evidence Synthesis: Data from a total of seven RCTs enrolling 808 patients were included in the meta-analysis. Pooling of data showed that compared with other drug regimens, the esketamine and propofol combination was associated with a reduction in the risk of hypotension (relative risk [RR]: 0.37, 95% confidence interval [CI]: 0.25 to 0.56) and bradycardia (RR: 0.34, 95% CI: 0.15 to 0.80) as well as an increase in the risk of agitation (RR: 6.29, 95% CI: 1.15 to 34.32). The results also indicated a decrease in propofol consumption (standardized mean difference: -1.45, 95% CI: -2.39 to -0.50) with the use of the esketamine and propofol combination. No significant difference was observed between the two groups in respiratory depression, nausea/vomiting, recovery time or body movement., Conclusions: Esketamine combined with propofol has an advantage in reducing the incidence of hypotension and bradycardia during procedural sedation and analgesia, but it may increase the risk of agitation in the recovery phase. More studies of high quality are needed before the widespread adoption of the combination of esketamine and propofol.