The guidance control system of the high-speed maglev enable that the maglev can travel along the center line of track. When the maglev passes through curve, the guidance control system also provides guidance force. When the maglev operates on the track, the friction between guidance electromagnet and track happens sometimes. This problem will pose a great threat to the safety of maglev running. However, other studies have failed to reflect the actual operation very well. In order to give a reasonable explanation for problems happened in actual test, the dynamics model of the guidance control system in high-speed maglev is established in this paper. The curve operation simulation was carried out, and the reason of the friction problem was analyzed through the results of simulation. Finally, an actual running test was carried out. The result of test shows that the high-speed maglev guidance control model established in this paper is effective. This model can provide an platform for analyzing the motion of guidance control units of the high-speed maglev train, further provide a model reference for the design and optimization of the control algorithm.

The detection of drivable areas in off-road scenes is a challenging problem due to the presence of unstructured class boundaries, irregular features, and dust noise. Three-dimensional LiDAR data can effectively describe the terrain features, and a bird’s eye view (BEV) not only shows these features, but also retains the relative size of the environment compared to the forward viewing. In this paper, a method called LRTI, which is used for detecting drivable areas based on the texture information of LiDAR reflection data, is proposed. By using an instance segmentation network to learn the texture information, the drivable areas are obtained. Furthermore, a multi-frame fusion strategy is applied to improve the reliability of the output, and a shelter’s mask of a dynamic object is added to the neural network to reduce the perceptual delay caused by multi-frame fusion. Through TensorRT quantization, LRTI achieves real-time processing on the unmanned ground vehicle (UGV). The experiments on our dataset show the robustness and adaptability of LRTI to sand dust and occluded scenes.

With the development of deep learning, more and more attention has been paid to end-to-end autonomous driving. However, affected by the nature of deep learning, end-to-end autonomous driving is currently facing some problems. First, due to the imbalance between the “junctions” and “non-junctions” samples of the road scene, the model is overfitted to a large class of samples during training, resulting in insufficient learning of the ability to turn at intersections; second, it is difficult to evaluate the confidence of the deep learning model, so it is impossible to determine whether the model output is reliable, and then make further decisions, which is an important reason why the end-to-end autonomous driving solution is not recognized; and third, the deep learning model is highly sensitive to disturbances, and the predicted results of the previous and subsequent frames are prone to jumping. To this end, a more robust and reliable end-to-end visual navigation scheme (RREV navigation) is proposed in this paper, which was used to predict a vehicle’s future waypoints from front-view RGB images. First, the scheme adopted a dual-model learning strategy, using two models to independently learn “junctions” and “non-junctions” to eliminate the influence of sample imbalance. Secondly, according to the smoothness and continuity of waypoints, a model confidence quantification method of “Independent Prediction-Fitting Error” (IPFE) was proposed. Finally, IPFE was applied to weight the multi-frame output to eliminate the influence of the prediction jump of the deep learning model and ensure the coherence and smoothness of the output. The experimental results show that the RREV navigation scheme in this paper was more reliable and robust, especially, the steering performance of the model intersection could be greatly improved.

The end-to-end autonomous guidance method requires massive high-quality training data to achieve better performance. In reality, new and uncollectable environments– - characterized by spatial variations in geographical scenes (urban/rural/mountainous terrains with diverse road rules like left/right driving), road network topologies (complex intersections, varying lane curvatures), and terrain features (steep slopes, occlusions from tunnels or buildings)—inevitably cause generalization decline. Consequently, this paper proposes an Autonomous Guidance method based on a novel Mixture-of-Experts framework (AG-MoE). AG-MoE maps environment information to guidance by fusing knowledge from training data and pre-trained models, explicitly addressing spatial challenges: its feature extraction structure processes LiDAR point clouds with geometric priors (e.g., lane curvature, slope gradients), while the MoE structure dynamically selects expert branches for diverse scenarios (e.g., urban intersections vs. mountainous curves). This enables effective fusion of multi-scene knowledge—such as adapting to cross-regional infrastructure (roundabouts vs. auxiliary roads) or terrain-induced sensor distortions—thereby improving generalization in unknown environments. Experiments on CARLA (with varied town maps) and real-world datasets validate that AG-MoE outperforms baselines, demonstrating enhanced adaptability to unseen spatial configurations.

This letter presents a technique that allows unmanned vehicles to escort a human to their destinations. Current human-centered following methods depend solely on human movement, which presents significant limitations. The complexity of human movement during tactical maneuvers can lead to erratic vehicle motion. Additionally, the static relative positioning between the human and vehicle creates a rigid following pattern, thereby constraining the vehicle's ability to dynamically adjust its position for optimal coverage. To address these limitations, we propose a data-driven end-to-end escorting system (EES) that takes into account both environmental information and human movement to achieve adaptive escorting.We propose a soft-coding paradigm to replace the traditional hard-coding intent modeling to address the inconsistency of human intention and vehicle motion, and establish human-scene following through a crossmodal attention gating network.We conducted experiments in the CARLA simulation and the real world. The results demonstrate that the proposed EES reduces prediction errors by 41.2% during overall processes and by 54.5% during cornering. Additionally, EES can adapt to various positions and dynamically adjust the relative positions between humans and unmanned systems to adapt to complex scenarios.