Search Results (5)

As an essential part of the low-altitude economy, low-altitude carriers are an important cornerstone of its development and a new industry that cannot be ignored strategically. However, it is difficult for the existing two-dimensional vehicle autonomous driving perception scheme to meet the needs of general key technologies for all-scene perception such as the global high-precision map construction of low-altitude vehicles in a three-dimensional space, the perception identification of local environmental traffic participants, and the extraction of key visual information under extreme conditions. Therefore, it is urgent to explore the development and verification of all-scene universal sensing technology for low-altitude intelligent vehicles. In this paper, the literature on vision-based urban rail transit and general perception technology in low-altitude flight environment is studied, and the paper summarizes the research status and innovation points from five aspects, namely the environment perception algorithm based on visual SLAM, the environment perception algorithm based on BEV, the environment perception algorithm based on image enhancement, the performance optimization of the perception algorithm using cloud computing, and the rapid deployment of the perception algorithm using edge nodes, and puts forward the future optimization direction of this topic. Full article

Autonomous rail rapid transit (ART) is a new type of multiunit, articulated, rubber-wheeled urban transport system. The noise sources of ART have significant time-varying characteristics. It is unsuitable to track the error signal by installing too many error sensors, which poses a significant challenge in the active noise control of ART. Thus, this paper proposes a convolutional fuzzy neural network-based active noise cancellation approach without error sensors to solve this problem. The proposed approach utilizes convolutional neural network (CNN) to extract the noise signal characteristics of ART and trains multiple noise source signals using a CNN to estimate the virtual error signal in the target area. In addition, the proposed approach adopts fuzzy neural network (FNN) for adaptive adjustment of filter weight coefficients to achieve real-time noise tracking control with fast convergence and small error in the convergence process. The experimental results demonstrate that the proposed approach can effectively reduce ART low-frequency noise without error sensors, and the average sound pressure level in the target area is reduced more compared with conventional approaches. Full article

The virtual track train (VTT) is a new urban public transportation system that adopts all-axle steering and distributed drive. The Super autonomous Rail rapid Transit (SRT), as one of them, adopts a four-module six-axle structure. In response to its crucial problem, its path tracking, this article proposes a reconfigurable dynamic modeling method, which has two parts: a multi-body dynamics model with generalized forces at each module’s center of gravity (CG) as the input, and the CG generalized force model, which expresses the CG generalized forces generated by the wheel control inputs. Then, a path-tracking strategy is proposed based on the improved MPC and hierarchical framework. Firstly, the CG generalized forces of each module required for path tracking were calculated, and then the CG generalized force redistribution was performed and the “virtual axle” method was proposed. Finally, the wheel state of each module was allocated. This strategy reduces the complexity of each layer of the controller and it solves the problem of insufficient actuators in the middle two modules of the SRT. Finally, through a hardware-in-the-loop (HIL) real-time simulation and comparison with different control strategies, the control strategy’s effectiveness, adaptability, and robustness were verified. Full article

The application of a semi-flexible pavement (SFP) is an effective solution to solve the rutting problems of the autonomous rail rapid transit (ART) system. The service environment of an ART pavement is significantly different from that of the conventional pavement due to the large axle load and high tire pressure of ART vehicles. A test section was constructed in the Zhuzhou ART system and a tire–pavement coupling FE model was built to explore the distribution features of the dynamic responses as well as to optimize the material and structural design. The tire–pavement coupling model was firstly verified by the field test data and then, utilizing the validated model, the parameter study was performed to analyze the influence of the vehicle operating state and pavement conditions. The simulation results show that the transverse tensile strain at the bottom of the SFP layer is dominant for the fatigue cracking of the pavement. Properly reducing the tire pressure can effectively improve the tensile environment at the bottom of the SFP layer. The action of the braking force may cause significant longitudinal tensile strains at the surface of the SFP layer and lead to transverse cracking of the semi-flexible ART pavement. The interlayer bonding between the SFP layer and the asphalt layer has significant influence on the amplitude and distribution of tensile stress at the bottom of the SFP layer. Moreover, to optimize the tensile environment of the semi-flexible ART pavement, the thickness of the SFP layer and the asphalt concrete layer cannot differ too much under the premise of meeting the requirements of rutting resistance performance. Full article

Due to the particularity of the structure, the dynamic properties of multiunit articulated rubber-wheel autonomous rail rapid transit system are very complex, which increases the difficulty of studying its braking stability. In this paper, a dynamic analysis model for the emergency braking of a multiunit articulated rubber-wheel autonomous rail rapid transit system is established by introducing the axle load transfer, suspension deformation compatibility equation, articulation force relationship equations, etc. Based on an in-depth analysis of the risks of the lateral swing instability and their formation mechanisms, an active control strategy for the multiunit articulated rubber-wheel autonomous rail rapid transit system under emergency braking conditions is innovatively proposed to ensure the stability of the vehicle, with the shortest braking distance as the optimization goal. Through simulation and experimentation, the established dynamic model is confirmed to approach the real vehicle well, and the feasibility of the active control strategy is proved. Full article