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Multi-intelligent vehicles forming and maintaining a specific formation during traveling can save energy consumption and road resources, and improve traffic efficiency and driving safety. Centered around the Cooperative Vehicle Infrastructure(CVI) framework of the Internet of Vehicles(IOV), this paper delves into the control objectives of forming multi-intelligent vehicle formation, explores the theory of formation shaping process, and investigates the optimization of energy consumption in the formation shaping process. First, the clustering design is used to take the kinematic process of vehicles as an optimization process from the bionics, taking the instantaneous consumption indexes as the optimization objectives. Then, optimize the acceleration inputs of the vehicle formation process using the improved Artificial Fish Swarm Algorithm(AFSA), and iterate with the clustering algorithm part, so as to realize the formation shaping process with energy consumption as the optimization objective. Finally, ajoint simulation platform built by Simulation of Urban Mobility (SUMO) and Python is used for simulation verification, and the results show that the formation shaping process can be realized and achieve the optimal energy consumption under the scenarios proposed in this paper, thus verifying the validity of the clustering model of vehicle formation shaping and the energy consumption optimization algorithm designed in this paper. [ABSTRACT FROM AUTHOR]

The traditional LiDAR simultaneous localization and mapping (SLAM) algorithm is subject to large outlier interference and inaccurate keyframe matching. To address this a LiDAR SLAM algorithm using normal distributions transform (NDT) matching combined with two-step screening of key frames is proposed. The algorithm is built on light weight and groundoptimized LiDAR odometry and mapping (LeGO-LOAM). First, the point cloud is pre-processed using statistical outlier removal (SOR). The current frame is matched with its corresponding key frame for NDT and the matching result is added to the loop closure detection. Feature extraction is performed on the keyframe and the feature points are added to the loop closure detection and pose map. A two-step keyframe screening method is used in the loop closure detection module to effectively reduce map building errors. The proposed method was evaluated on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset. Additionally, the experimental results show that the proposed algorithm can improve the mapping accuracy as measured by the value of the root mean square error (RMSE). [ABSTRACT FROM AUTHOR]

An E-band (60–90 GHz) multi-channel Doppler backscattering (DBS) system with X-mode polarization has been installed on the Experimental Advanced Superconducting Tokamak (EAST), which can measure the turbulence at five different radial locations simultaneously. This system can launch 31 fixed microwave frequencies in the range of 60–90 GHz with a 1 GHz interval into the plasma, and five probing signals are selected by employing a reference signal and multiple filters. During experiments, the frequency of the reference signal is tunable in the E-band, and the selected probing signals can be changed as needed without any other adjustments, which can be performed in one shot or between shots. Furthermore, the incident angle can be adjusted from −10° to 20°, and the wavenumber range is 4–25 cm−1 with a wavenumber resolution of Δk/k ≤ 0.35. Ray tracing simulations are employed to calculate the scattering locations and the perpendicular wavenumber. In this article, the hardware design, ray tracing, and initial results obtained from the EAST plasma will be presented. [ABSTRACT FROM AUTHOR]

Current LiDAR systems are limited to vertical field of view (FoV) have a blind scanning region which leads to uncompleted three-dimensional (3-D) spatial reconstruction. To solve this problem this paper proposes a device mounted with a multi-layer LiDAR to collect data, which spins clockwise around the LiDAR's y-axis. Statistical outlier removal (SOR) and an optimized voxel-grid filter are employed to do the point cloud registration. The coarse registration and the fine registration are performed by 3-D normal distributions transform (NDT) and point-to-plane iterative closest point (ICP), respectively. The completed 3-D reconstruction is unrestricted by the LiDAR's vertical FoV. Additionally, the experimental results show that the proposed algorithm can improve the registration accuracy as measured by the value of the root mean square error (RMSE). [ABSTRACT FROM AUTHOR]

Vehicle-vehicle cooperation technology can improve the traffic capacity and the safety of vehicles. This article focuses on the common lane changing scenarios, based on vehicle-vehicle coordination, an autonomous lanechanging cooperation method is put forward. Firstly, the framework of cooperative lane changing is established, considering the driving safety, comfort and road traffic efficiency, an ideal trajectory model of lane changing based on fifth-order polynomials is established. Secondly, considering the demands of vehicles in the traffic environment, four cooperative schemes are proposed, according to the ideal trajectory, an evaluation function is established to obtain the optimal lane changing scheme. Finally, in order to verify the feasibility of the cooperative schemes, using two common scenarios of low speed and high speed, the cooperative schemes are verification, and the results show that the proposed optimal cooperative schemes are reasonable. [ABSTRACT FROM AUTHOR]

The model of (2 + 1)-dimensional breaking soliton equation is proposed, and the novel soliton solutions are derived by using the variable separation and extended mapping approach of Riccati equation and then, the propagation dynamics of the solutions which include multi-dromions, ring breather, fusion solitons and other multi-solitons are also constructed. Furthermore, the rich local excitations and fractal structures are investigated. The simulation results verify the proposed scheme. It can get more abundant local excitation structures than the (1 + 1)-dimensional nonlinear systems and it has potential values for certain applications in optical field. [ABSTRACT FROM AUTHOR]

For autonomous vehicles, when encountering traffic accidents or had weather and other complex situations that made the traffic signal stop working, recognizing the command of traffic police gesture was very important. A new method of traffic police gesture recognition was proposed. First, the traffic police gestures were decomposed into "key actions" and "transition actions" based on the motion characteristics, Secondly, using Faster R-CNN and fuzzy matching the gesture recognition model was build. Faster R-CNN was used to extract the features of decomposed actions for image recognition; a fuzzy matching method was built to match gestures based on the recognition of decomposed actions. Finally, the recognition experiment was executed, as for common traffic police gesture, the result showed that the new proposed method was effective. [ABSTRACT FROM AUTHOR]

For the improvement of active safety and the reduction of traffic collisions, significant efforts have been made on collision avoidance technology. However, the majority of the current research focus on the relative motion of the vehicle, with less consideration of the road adhesion condition. This paper designs Multiple fading factors Strong Tracking Unscented Kalman Filter (MSTUKF) algorithm to identify road adhesion coefficient and proposes the collision avoidance strategy based on road adhesion. First, according to sensor information, the most dangerous target is selected. Then, multiple fading factors are introduced to construct MSTUKF algorithm, and combined with the vehicle dynamics model to identify the current road adhesion coefficient. For better collision avoidance effect, the dual risk assessment of vehicle and road are proposed, in which TimeTo-Collision (TTC) is established according to the relative motion state of ego vehicle and the target, and Time-To-Avoidance (TTA) is calculated based on the road adhesion coefficient. Finally, using the feedforward and feedback control, the desired braking deceleration can be obtained by adjusting the master cylinder brake pressure. The simulation results show that the proposed algorithm can identify road adhesion coefficients with high accuracy and stability. Furthermore, the proposed strategy can provide good collision avoidance control effect under different scenarios. [ABSTRACT FROM AUTHOR]

In previous studies, researchers established mathematical models for predicting the pressure coefficient in simple cavities and tubed vortex reducers based on the assumptions of incompressibility and adiabatic reversibility. However, these mathematical models are not suitable for engineering design and cannot predict the internal pressure and temperature. In this study, we first derived mathematical equations for predicting the pressure drop and temperature change in a tubed vortex reducer, by considering the irreversible loss at the tube inlet. To compensate for the shortcomings of the incompressibility assumption, we developed an iterative alternating calculation method that revises the density. Subsequently, we established a coupled prediction model based on the aforementioned equations and methods. The verified Reynolds stress model results proved that the coupled prediction model and the single prediction model, which represents the incompressible case, yield similar results in predicting pressure under low-rotating-speed conditions. However, as the rotating speed was increased, the error of the single prediction model gradually increased, whereas the coupled prediction model still had good prediction accuracy. With an increase in the length and number of tubes, the pressure drop showed a decreasing trend, whereas the temperature change did not fluctuate significantly. [ABSTRACT FROM AUTHOR]

The coarse registration is required before fine registration to obtain a better initial pose; however, it is difficult for coarse registration to obtain a better initial pose when the overlapping coefficient of source and target point cloud is low. To overcome this we propose a registration method using difference of normals (DoN) based segmentation algorithm and sample consensus initial alignment algorithm. First, the DoN based segmentation algorithm is employed to segment areas where obvious normal differences from source and target point cloud. Afterwards, Gaussian models for these subsets are established to find a pair of point cloud subsets with the most similar distribution. Then the sample consensus initial alignment algorithm (SAC-IA) is employed to register the matched subsets to obtain transformations between them, applied to source point cloud to find a better initial pose. Eventually, the iterative closest point (ICP) algorithm is involved to complete the registration. The experiments show the root mean square error (RMSE) of the registered point cloud using proposed method has been improved by 0.435 m compared with that of previous algorithm when the overlapping coefficient is 5%. The analysis shows the segmented region has clearer normal features; consequently, the SAC-IA algorithm to extract a more effective feature histogram to obtain a more accurate transformation. [ABSTRACT FROM AUTHOR]