Simulation is one of the most essential parts in the development stage of automotive software. However, purely virtual simulations often struggle to accurately capture all real-world factors due to limitations in modeling. To address this challenge, this work presents a test framework for automotive software on the centralized E/E architecture, which is a central car server in our case, based on Vehicle-in-the-Loop (ViL) and digital twin technology. The framework couples a physical test vehicle on a dynamometer test bench with its synchronized virtual counterpart in a simulation environment. Our approach provides a safe, reproducible, realistic, and cost-effective platform for validating autonomous driving algorithms with a centralized architecture. This test method eliminates the need to test individual physical ECUs and their communication protocols separately. In contrast to traditional ViL methods, the proposed framework runs the full autonomous driving software directly on the vehicle hardware after the simulation process, eliminating flashing and intermediate layers while enabling seamless virtual-physical integration and accurately reflecting centralized E/E behavior. In addition, incorporating mixed testing in both simulated and physical environments reduces the need for full hardware integration during the early stages of automotive development. Experimental case studies demonstrate the effectiveness of the framework in different test scenarios. These findings highlight the potential to reduce development and integration efforts for testing autonomous driving pipelines in the future.
Flight training for flight trainees is an important method to ensure civil aviation safety.The core competency assessment of flight trainees is a crucial part of the construction of a pilot lifecycle management system. A multilevel evaluation framework for assessing the core competencies of flight trainees is established by combining Analytic Hierarchy Process（AHP）and Fuzzy Comprehensive Evaluation Method（FCEM）. Firstly，based on relevant research into the competencies of civil aviation practitioners，and taking into account the unique characteristics of flight trainees，a comprehensive core competency index system specifically for flight trainees is formulated. Secondly，AHP is employed to determine the relative weights of each core competency indicator，while fuzzy rules are incorporated to devise evaluation sheets tailored to various behavioral indicators. Subsequently，by integrating these weights with the fuzzy membership degrees assigned to each indicator，a multi-level fuzzy comprehensive evaluation of the flight trainees' core competencies is undertaken. Finally，utilizing flight data sourced from a particular division of a certain college of Civil Aviation Flight University of China，the validity of the proposed evaluation method is verified by comparing the outcomes of the multi-level fuzzy comprehensive evaluation against both existing evaluation methodologies and the assessment results obtained by the flight squadron.
Ehab Mahmoud Mohamed, Hany S. Hussein, Mohammad Ahmed Alnakhli
et al.
In this paper, delay Doppler (DD) domain is utilized for enabling an efficient handover-triggering mechanism in highly dynamic unmanned aerial vehicles (UAVs) or drones to ground networks. In the proposed scheme, the estimated DD channel gains using DD multi-carrier modulation (DDMC), e.g., orthogonal time frequency space (OTFS) modulation, are utilized for triggering the handover decisions. This is motivated by the fact that the estimated DD channel gain is time-invariant throughout the whole OTFS symbol despite the entity speed. This results in more stable handover decisions over that based on the time-varying received-signal strength (RSS) or frequency time (FT) channel gains using orthogonal frequency division multiplexing (OFDM) modulation employed in fifth-generation–new radio (5G-NR) and its predecessors. To mathematically bind the performance of the proposed scheme, we studied its performance under channel estimation errors of the most dominant DD channel estimators, i.e., least square (LS) and minimum mean square error (MMSE), and we prove that they have marginal effects on its performance. Numerical analyses demonstrated the superiority of the proposed DD-based handover-triggering scheme over candidate benchmarks in terms of the handover overhead, the achievable throughput, and ping-pong ratio under different simulation conditions.
In complex real-world traffic environments, autonomous vehicles (AVs) need to interact with other traffic participants while making real-time and safety-critical decisions accordingly. The unpredictability of human behaviors poses significant challenges, particularly in dynamic scenarios, such as multi-lane highways and unsignalized T-intersections. To address this gap, we design a bi-level interaction decision-making algorithm (BIDA) that integrates interactive Monte Carlo tree search (MCTS) with deep reinforcement learning (DRL), aiming to enhance interaction rationality, efficiency and safety of AVs in dynamic key traffic scenarios. Specifically, we adopt three types of DRL algorithms to construct a reliable value network and policy network, which guide the online deduction process of interactive MCTS by assisting in value update and node selection. Then, a dynamic trajectory planner and a trajectory tracking controller are designed and implemented in CARLA to ensure smooth execution of planned maneuvers. Experimental evaluations demonstrate that our BIDA not only enhances interactive deduction and reduces computational costs, but also outperforms other latest benchmarks, which exhibits superior safety, efficiency and interaction rationality under varying traffic conditions.
To enhance the surface quality of metal 3D-printed components, magnetic abrasive finishing (MAF) technology was employed for post-processing polishing. Experimental investigation employing response surface methodology was conducted to explore the impact of processing gap, rotational speed of the magnetic field, auxiliary vibration, and magnetic abrasive particle (MAP) size on the quality enhancement of internal surfaces. A regression model correlating roughness with crucial process parameters was established, followed by parameter optimization. Ultimately, the internal surface finishing of waveguides with blind cavities was achieved, and the finishing quality was comprehensively evaluated. Results indicate that under optimal process conditions, the roughness of the specimens decreased from Ra 2.5 μm to Ra 0.65 μm, reflecting a reduction rate of 74%. Following sequential rough and fine processing, the roughnesses of the cavity bottom, side wall, and convex surface inside the waveguide reduced to 0.59 μm, 0.61 μm, and 1.9 μm, respectively, from the original Ra above 12 μm. The findings of this study provide valuable technical insights into the surface finishing of metal 3D-printed components.
Twist angle errors along the blade radial direction are uncertain and affected by cutting force, tool wear, and other factors. In this paper, the measured twist angle errors of 13 sections of 72 rotor blades were innovatively analyzed to obtain the rational statistical distribution. It is surprisingly found that the under-deflection systematic deviation of twist angle errors shows a gradually increasing W-shaped distribution along the radial direction, while the scatter is nearly linear. Logically, the statistical model is established based on the linear correlation of the scatter by regression analysis to reduce variable dimension from 13 to 1. The influence of the radial non-uniform twist angle errors’ uncertainty on the aerodynamic performance of the three-dimensional compressor rotor is efficiently quantified combining the non-intrusive polynomial chaos method. The results show that the mean values of mass flow rate, total pressure ratio, and isentropic efficiency at the typical operating conditions are lower than the nominal values due to the systematic deviation, indicating that the under-deflection twist angle errors lead to the decrease in compressor thrust. The compressor’s stable operating range is more sensitive to the scatter of twist angle errors, which is up to an order of magnitude greater than that of the total pressure ratio and isentropic efficiency, indicating the compressor’s safe and stable operation risk increases. Additionally, the flow field at the tip region is significantly affected by twist angle errors, especially at the shock wave position of the near-stall condition.
The static aeroelastic characteristics of the distributed propulsion wing (DPW) were studied using the CFD/CSD loose coupling method in this study. The momentum source method of the Reynolds-averaged Navier–Stokes equation based on the k-ω SST turbulence model solution was used as the CFD solution module. The upper and lower surfaces of the DPW were established using the cubic B-spline basis function method, and the surfaces of the inlet and outlet were established using the fourth-order Bezier curve. Finally, a three-dimensional parametric model of the DPW was established. A structural finite-element model of the DPW was established, a multipoint array method program based on the three-dimensional radial basis function (RBF) was written as a data exchange module to realize the aerodynamic and structural data exchange of the DPW’s static aeroelastic analysis process, and, finally, an aeroelastic analysis of the DPW was achieved. The results show that the convergence rate of the CFD/CSD loosely coupled method is fast, and the structural static aeroelastic deformation is mainly manifested as bending deformation and positive torsion deformation, which are typical static aeroelastic phenomena of the straight wing. Under the influence of static aeroelastic deformation, the increase in the lift characteristics of the DPW is mainly caused by the slipstream region of the lower surface and the non-slipstream region of the upper and lower surface. Meanwhile, the increase in its nose-up moment and the increase in the longitudinal static stability margin may have an impact on the longitudinal stability of the UAV. To meet the requirements of engineering applications, a rapid simulation method of equivalent airfoil, which can be applied to commercial software for analysis, was developed, and the effectiveness of the method was verified via comparison with the CFD/CSD loose coupling method. On this basis, the static aeroelastic characteristics of the UAV with DPWs were studied. The research results reveal the static aeroelastic characteristics of the DPW, which hold some significance for engineering guidance for this kind of aircraft.
Airport noise prediction models are divided into physics-guided methods and data-driven methods. The prediction results of physics-guided methods are relatively stable, but their overall prediction accuracy is lower than that of data-driven methods. However, machine learning methods have a relatively high prediction accuracy, but their prediction stability is inferior to physics-guided methods. Therefore, this article integrates the ECAC model, driven by aerodynamics and acoustics principles under the framework of deep neural networks, and establishes a physically guided neural network noise prediction model. This model inherits the stability of physics-guided methods and the high accuracy of data-driven methods. The proposed model outperformed physics-driven and data-driven models regarding prediction accuracy and generalization ability, achieving an average absolute error of 0.98 dBA in predicting the sound exposure level. This success was due to the fusion of physics-based principles with data-driven approaches, providing a more comprehensive understanding of aviation noise prediction.
The rapid development and expansion of the EV market marked by the advent of third decade of the 21st century has improved the possibility of a sustainable automotive future. The present EV drivetrain run by BLDC motor has become increasingly complicated thus requiring efficient and accurate controls. The paper begins with discussing the problems in existing models, the research then focuses on increasing the robustness of the system towards disturbances and uncertainties by using Sliding Mode Control to control the ZSI, which has been chosen as the main power converter topology in place of VSI or CSI. The introduction of SMC has improved the performance of the drivetrain when applied with Vehicle dynamics over a Drive Cycle.
Alessandro Toschi, Nicola Musiu, Francesco Gatti
et al.
The sideslip angle, crucial for vehicle safety and stability, is determined using both longitudinal and lateral velocities. However, measuring the lateral component often necessitates costly sensors, leading to its common estimation, a topic thoroughly explored in existing literature. This paper introduces LOP-UKF, a novel method for estimating vehicle lateral velocity by integrating Lidar Odometry with the Pacejka tire model predictions, resulting in a robust estimation via an Unscendent Kalman Filter (UKF). This combination represents a distinct alternative to more traditional methodologies, resulting in a reliable solution also in edge cases. We present experimental results obtained using the Dallara AV-21 across diverse circuits and track conditions, demonstrating the effectiveness of our method.
Brain-computer interface (BCI) enables direct communication between the brain and external devices by decoding neural signals, offering potential solutions for individuals with motor impairments. This study explores the neural signatures of motor execution (ME) and motor imagery (MI) tasks using EEG signals, focusing on four conditions categorized as sense-related (hot and cold) and motor-related (pull and push) conditions. We conducted scalp topography analysis to examine activation patterns in the sensorimotor cortex, revealing distinct regional differences: sense--related conditions primarily activated the posterior region of the sensorimotor cortex, while motor--related conditions activated the anterior region of the sensorimotor cortex. These spatial distinctions align with neurophysiological principles, suggesting condition-specific functional subdivisions within the sensorimotor cortex. We further evaluated the performances of three neural network models-EEGNet, ShallowConvNet, and DeepConvNet-demonstrating that ME tasks achieved higher classification accuracies compared to MI tasks. Specifically, in sense-related conditions, the highest accuracy was observed in the cold condition. In motor-related conditions, the pull condition showed the highest performance, with DeepConvNet yielding the highest results. These findings provide insights into optimizing BCI applications by leveraging specific condition-induced neural activations.
Marcel Vosshans, Alexander Baumann, Matthias Drueppel
et al.
The increasing complexity of urban environments has underscored the potential of effective collective perception systems. To address these challenges, we present the CoopScenes dataset, a large-scale, multi-scene dataset that provides synchronized sensor data from both the ego-vehicle and the supporting infrastructure.The dataset provides 104 minutes of spatially and temporally synchronized data at 10 Hz, resulting in 62,000 frames. It achieves competitive synchronization with a mean deviation of only 2.3 ms. Additionally the dataset includes a novel procedure for precise registration of point cloud data from the ego-vehicle and infrastructure sensors, automated annotation pipelines, and an open-source anonymization pipeline for faces and license plates. Covering nine diverse scenes with 100 maneuvers, the dataset features scenarios such as public transport hubs, city construction sites, and high-speed rural roads across three cities in the Stuttgart region, Germany. The full dataset amounts to 527 GB of data and is provided in the .4mse format, making it easily accessible through our comprehensive development kit. By providing precise, large-scale data, CoopScenes facilitates research in collective perception, real-time sensor registration, and cooperative intelligent systems for urban mobility, including machine learning-based approaches.
An analysis of the problems that arise when dealing with issues of increasing the efficiency of using an afterburner as part of a current aircraft engine is presented, where the complex problem of reducing its influence in non-afterburning operation and improving the work process when using it is solved. In a number of cases, solving the problem comes down to a compromise which does not allow full realization of all the advantages of the design element in question. The results of a study of the influence of the method of gas-dynamic flame stabilization on the main target indicator – specific fuel consumption in a real engine are presented, which makes it possible to justify conducting this kind of research on a real object. Graphs of changes in specific fuel consumption and thrust of a gas turbine engine when changing the engine rotor speed for non-afterburning operating modes are presented, as well as graphs of changes in specific fuel consumption and thrust of a gas turbine engine when changing the speed of the aircraft for afterburning operating modes. A conclusion is drawn about the expedience of using gas-dynamic flame stabilization in the afterburner from the point of view of its effect on specific fuel consumption.
The subject of the study is the process of forming a mathematical model (MM) of a turboshaft gas turbine engine and a twin-engine helicopter power plant, which provides the determination of parameters of the working process in steady and transient operating modes for use in the estimation of dynamic characteristics, in the analysis and synthesis of engine and helicopter automatic control systems. The goal is to substantiate the structure and methodology of MM formation intended for use in real and accelerated time scale systems. Tasks: implementation of the previously proposed MM structure taking into account the turboshaft engine performances, development of a methodology for determining the MM coefficients based on known information about the static and dynamic properties of the engine, and formation of the MM structure of a two-engine helicopter power plant. For this, the methods of the theory of airjet engines and the theory of linear dynamic systems are used. The following results were obtained: the structure of a multimode high-speed MM of a turboshaft engine and a two-engine power plant was formed and tested. The scientific and practical novelty of the obtained results is as follows: the structure of the multimode linearized MM of the turboshaft engine is formed, which consists of static and dynamic submodels implemented in corrected parameters; the modeling technique was worked out on a simplified model, compiled considering expert information about the static and dynamic properties of the engine in the considered operation area. Formulas were obtained that relate the coefficients of the linear dynamic model to the values of the time constants of the rotors and the sensitivities obtained from the static characteristics; transient characteristics of the engine based on changes in fuel consumption and load power are determined, which correspond to physical knowledge about the engine; the modeling methodology and MM structure of a two-engine power plant were formed, which is distinguished by the combination of individual static and linear dynamic models of two engines with a single nonlinear dynamic model of the helicopter rotor; a simplified MM load necessary for testing the MM of the engine installation is proposed, which provides the calculation of the power consumed by the rotor, depending on the angular position of the blades.
Luo Ruining, Huang Shucai, Zhao Yan, Zhang Zhen, Zhang Fei
In order to solve the possible collision problem in the process of sub-interceptors intercepting UAV cluster, this paper proposes a collision avoidance pure proportional guidance law (CA-PPN) by combining the virtual repulsion force with the pure proportional guidance law. Firstly, it proposes an operational concept of submunition missile intercepting UAV cluster, and analyzes the guidance problem of sub-interceptors. Then, aiming at the possible collision problem in the guidance of sub-interceptors, it designs an artificial potential field for collision avoidance between sub-interceptors, and analyzes the characteristics of PPN. On this basis, combining PPN with the virtual repulsion force in the artificial potential field, and adding the field of view angle and overload constraints, the CA-PPN is proposed. The simulation results show that CA-PPN can effectively intercept the target and avoid the collision between sub-interceptors.
Autonomous unmanned aircraft need a good semantic understanding of their surroundings to plan safe routes or to find safe landing sites, for example, by means of a semantic segmentation of an image stream. Currently, Neural Networks often give state-of-the-art results on semantic segmentation tasks but need a huge amount of diverse training data to achieve these results. In aviation, this amount of data is hard to acquire but the usage of synthetic data from game engines could solve this problem. However, related work, e.g., in the automotive sector, shows a performance drop when applying these models to real images. In this work, the usage of synthetic training data for semantic segmentation of the environment from a UAV perspective is investigated. A real image dataset from a UAV perspective is stylistically replicated in a game engine and images are extracted to train a Neural Network. The evaluation is carried out on real images and shows that training on synthetic images alone is not sufficient but that when fine-tuning the model, they can reduce the amount of real data needed for training significantly. This research shows that synthetic images may be a promising direction to bring Neural Networks for environment perception into aerospace applications.
Olaf Borsboom, Martijn Lokker, Mauro Salazar
et al.
In general, electric motor design procedures for automotive applications go through expensive trial-and-error processes or use simplified models that linearly stretch the efficiency map. In this paper, we explore the possibility of efficiently optimizing the motor design directly, using high-fidelity simulation software and derivative-free optimization solvers. In particular, we proportionally scale an already existing electric motor design in axial and radial direction, as well as the sizes of the magnets and slots separately, in commercial motor design software. We encapsulate this motor model in a vehicle model together with the transmission, simulate a candidate design on a drive cycle, and find an optimum through a Bayesian optimization solver. We showcase our framework on a small city car, and observe an energy consumption reduction of 0.13% with respect to a completely proportional scaling method, with a motor that is equipped with relatively shorter but wider magnets and slots. In the extended version of this paper, we include a comparison with the linear models, and add experiments on different drive cycles and vehicle types.