Airport aprons are complex, multi-node operational hubs frequently affected by queue congestion resulting from control handovers, taxi conflicts, and external factors. To enable proactive congestion management, we propose a new and accurate method for apron queue length prediction. The core of our approach is a multi-queue network model in which queues are systematically divided by control position and taxi direction. This framework, which applies the Fluid Flow Approximation and is calibrated with historical data, effectively captures the dynamics of multi-node traffic flow. In a validation case study at Beijing Daxing International Airport (ZBAD), the model achieved high accuracy, with the mean absolute error of queue length prediction averaging 0.5 aircraft. The results demonstrate the model’s ability to characterize queue dynamics on a minute-level scale across a full day.
Traditional water quality monitoring methods are limited in providing timely chlorophyll-<i>a</i> (Chl-<i>a</i>) assessments in small inland reservoirs. This study presents a rapid Chl-<i>a</i> retrieval approach based on a cooperative unmanned aerial vehicle–uncrewed surface vessel (UAV–USV) framework that integrates UAV hyperspectral imaging, machine learning algorithms, and synchronized USV in situ sampling. We carried out a three-day cooperative monitoring campaign in the Longhu Reservoir of Fujian Province, during which high-frequency hyperspectral imagery and water samples were collected. An innovative median-based correction method was developed to suppress striping noise in UAV hyperspectral data, and a two-step band selection strategy combining correlation analysis and variance inflation factor screening was used to determine the input features for the subsequent inversion models. Four commonly used machine-learning-based inversion models were constructed and evaluated, with the random forest model achieving the highest accuracy and stability across both training and testing datasets. The generated Chl-<i>a</i> maps revealed overall good water quality, with localized higher concentrations in weakly hydrodynamic zones. Overall, the cooperative UAV–USV framework enables synchronized data acquisition, rapid processing, and fine-scale mapping, demonstrating strong potential for fast-response and emergency water-quality monitoring in small inland drinking-water reservoirs.
This paper presents a semi-circular, non-contact current sensor designed to simplify the layout of automotive wiring harnesses and enhance measurement convenience and reliability. The sensor integrates a hybrid sensing array consisting of Hall-effect and tunnel magnetoresistance (TMR) elements. To address common challenges in automotive power systems and vehicle wiring—such as conductor eccentricity and magnetic interference from adjacent cables—two key techniques are proposed. First, an eccentricity error compensation algorithm is developed, achieving a measurement accuracy of 97.07% under specific misalignment conditions. Second, an equivalent modeling method based on eccentricity principles is introduced to characterize interference fields in complex wiring environments, maintaining 94.31% accuracy in the presence of external disturbances. When the conductor is centered within the array, the average measurement accuracy reaches 99.05%. Experimental results demonstrate that the proposed sensor can reliably measure large currents from 0 to 210 A, making it highly suitable for applications in electric vehicles, high-voltage harness monitoring, power electronics, and intelligent transportation systems.
Mechanical engineering and machinery, Machine design and drawing
This study presents a hierarchical, network-level traffic flow control framework for mixed traffic consisting of Human-driven Vehicles (HVs), Connected and Automated Vehicles (CAVs). The framework jointly optimizes vehicle-level eco-driving behaviors and intersection-level traffic signal control to enhance overall network efficiency and decrease energy consumption. A decentralized Multi-Agent Reinforcement Learning (MARL) approach by Value Decomposition Network (VDN) manages cycle-based traffic signal control (TSC) at intersections, while an innovative Signal Phase and Timing (SPaT) prediction method integrates a Machine Learning-based Trajectory Planning Algorithm (MLTPA) to guide CAVs in executing Eco-Approach and Departure (EAD) maneuvers. The framework is evaluated across varying CAV proportions and powertrain types to assess its effects on mobility and energy performance. Experimental results conducted in a 4*4 real-world network demonstrate that the MARL-based TSC method outperforms the baseline model (i.e., Webster method) in speed, fuel consumption, and idling time. In addition, with MLTPA, HONEST-CAV benefits the traffic system further in energy consumption and idling time. With a 60% CAV proportion, vehicle average speed, fuel consumption, and idling time can be improved/saved by 7.67%, 10.23%, and 45.83% compared with the baseline. Furthermore, discussions on CAV proportions and powertrain types are conducted to quantify the performance of the proposed method with the impact of automation and electrification.
A Stackelberg equilibrium–based Model Reference Adaptive Control (MSE) method is proposed for spacecraft Pursuit–Evasion (PE) games with incomplete information and sequential decision making under a non–zero–sum framework. First, the spacecraft PE dynamics under <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>J</mi><mn>2</mn></msub></semantics></math></inline-formula> perturbation are mapped to a dynamic Stackelberg game model. Next, the Riccati equation solves the equilibrium problem, deriving the evader’s optimal control strategy. Finally, a model reference adaptive algorithm enables the pursuer to dynamically adjust its control gains. Simulations show that the MSE strategy outperforms Nash Equilibrium (NE) and Single–step Prediction Stackelberg Equilibrium (SSE) methods, achieving 25.46% faster convergence than SSE and 39.11% lower computational cost than NE.
For intercepting modern highly maneuvering targets, considering the velocity variation of adversaries, a varying speed cooperative differential games guidance method is proposed, which can enable multiple air-to-air missiles to intercept targets at a predefined relative intercept angle. This guidance method can help to improve the hit probability of high-value maneuvering targets. The planar cooperative engagement model and linearized kinematics are established. The varying speed cooperative differential game guidance laws (VSCDGGLs) are derived based on the linearized model and the two-sided optimization problem. To verify the accuracy of the cooperative guidance laws, conjugate point analysis and numerical simulations are conducted to examine the performance of the guidance laws. The results indicate that compared with the cooperative guidance laws based on the constant speed model, the VSCDGGL exhibits a lower demand for the maneuver advantage of the missiles over the target. It also demonstrates stronger robustness against different evasive target maneuvers in the case of varying speeds of missiles and targets. Missiles using the proposed guidance laws adjust their trajectories earlier to cope with the uncertainty of target maneuvers, thus obtaining lower miss distances and relative intercept angle errors with time-varying speeds.
Teleoperation is a key enabler for future mobility, supporting Automated Vehicles in rare and complex scenarios beyond the capabilities of their automation. Despite ongoing research, no open source software currently combines Remote Driving, e.g., via steering wheel and pedals, Remote Assistance through high-level interaction with automated driving software modules, and integration with a real-world vehicle for practical testing. To address this gap, we present a modular, open source teleoperation software stack that can interact with an automated driving software, e.g., Autoware, enabling Remote Assistance and Remote Driving. The software featuresstandardized interfaces for seamless integration with various real-world and simulation platforms, while allowing for flexible design of the human-machine interface. The system is designed for modularity and ease of extension, serving as a foundation for collaborative development on individual software components as well as realistic testing and user studies. To demonstrate the applicability of our software, we evaluated the latency and performance of different vehicle platforms in simulation and real-world. The source code is available on GitHub
This study investigates the efficiency and safety outcomes of implementing different adaptive coordination models for automated vehicle (AV) fleets, managed by a centralized coordinator that dynamically responds to human-controlled vehicle behavior. The simulated scenarios replicate an underground mining environment characterized by narrow tunnels with limited connectivity. To address the unique challenges of such settings, we propose a novel metric - Path Overlap Density (POD) - to predict efficiency and potentially the safety performance of AV fleets. The study also explores the impact of map features on AV fleets performance. The results demonstrate that both AV fleet coordination strategies and underground tunnel network characteristics significantly influence overall system performance. While map features are critical for optimizing efficiency, adaptive coordination strategies are essential for ensuring safe operations.
Mohammad Abtahi, Farhang Motallebi Araghi, Navid Mojahed
et al.
Accurate modeling and control of autonomous vehicles remain a fundamental challenge due to the nonlinear and coupled nature of vehicle dynamics. While Koopman operator theory offers a framework for deploying powerful linear control techniques, learning a finite-dimensional invariant subspace for high-fidelity modeling continues to be an open problem. This paper presents a deep Koopman approach for modeling and control of vehicle dynamics within the curvilinear Frenet frame. The proposed framework uses a deep neural network architecture to simultaneously learn the Koopman operator and its associated invariant subspace from the data. Input-state bilinear interactions are captured by the algorithm while preserving convexity, which makes it suitable for real-time model predictive control (MPC) application. A multi-step prediction loss is utilized during training to ensure long-horizon prediction capability. To further enhance real-time trajectory tracking performance, the model is integrated with a cumulative error regulator (CER) module, which compensates for model mismatch by mitigating accumulated prediction errors. Closed-loop performance is evaluated through hardware-in-the-loop (HIL) experiments using a CarSim RT model as the target plant, with real-time validation conducted on a dSPACE SCALEXIO system. The proposed controller achieved significant reductions in tracking error relative to baseline controllers, confirming its suitability for real-time implementation in embedded autonomous vehicle systems.
Lars Ullrich, Michael Buchholz, Klaus Dietmayer
et al.
Assuring safety of artificial intelligence (AI) applied to safety-critical systems is of paramount importance. Especially since research in the field of automated driving shows that AI is able to outperform classical approaches, to handle higher complexities, and to reach new levels of autonomy. At the same time, the safety assurance required for the use of AI in such safety-critical systems is still not in place. Due to the dynamic and far-reaching nature of the technology, research on safeguarding AI is being conducted in parallel to AI standardization and regulation. The parallel progress necessitates simultaneous consideration in order to carry out targeted research and development of AI systems in the context of automated driving. Therefore, in contrast to existing surveys that focus primarily on research aspects, this paper considers research, standardization and regulation in a concise way. Accordingly, the survey takes into account the interdependencies arising from the triplet of research, standardization and regulation in a forward-looking perspective and anticipates and discusses open questions and possible future directions. In this way, the survey ultimately serves to provide researchers and safety experts with a compact, holistic perspective that discusses the current status, emerging trends, and possible future developments.
With the rapid development of high-end manufacturing industries such as aerospace and national defense, the demand for metal additive manufactured parts with complex internal cavities has been steadily increasing. However, the finishing of complex internal surfaces, especially for irregularly shaped parts, remains a significant challenge due to their intricate geometries. Through a comparative analysis of common finishing methods, the distinctive characteristics and applicability of magnetic abrasive finishing (MAF) are highlighted. To meet the finishing needs of complex metal additive manufactured parts, this paper reviews the current research on magnetic abrasive finishing devices, processing mechanisms, the development of magnetic abrasives, and the MAF processes for intricate internal cavities. Future development trends in MAF for complex internal cavities in additive manufactured parts are also explored; these are (1) investigating multi-technology composite magnetic abrasive finishing equipment designed for complex internal surfaces; (2) studying the dynamic behavior of multiple magnetic abrasive particles in complex cavities and their material removal mechanisms; (3) developing high-performance magnetic abrasives suitable for demanding conditions; and (4) exploring the MAF process for intricate internal surfaces.
The validity of the subjective evaluation of steering feedback in driving simulators is crucial for modern vehicle development. Although there are established objective steering characteristics for the assessment of both stationary and dynamic feedback behaviour, factors such as steering wheel vibrations and vehicle body motion, particularly in high-frequency ranges, present challenges in simulator fidelity. This work investigates the influence of steering wheel vibration and vehicle body motion frequency content on the subjective evaluation of steering feedback during closed-loop driving in a dynamic driving simulator. A controlled subject study with 30 participants consisting of a back-to-back comparison of a reference vehicle with an electrical power steering system and three variants of its virtual representation on a dynamic driving simulator was performed. Subjective evaluation focused on the representation of road feedback in comparison to the reference vehicle. The statistical analysis of subjective results show that there is a significant influence of the frequency content of both steering wheel torque and vehicle motion on the subjective evaluation of steering feedback in a dynamic driving simulator. The results suggest an influence of frequency content on the subjective evaluation quality of steering feedback characteristics that are not associated with the dynamic feedback behaviour in the context of established performance indicators.
The autonomous driving industry is expected to grow by over 20 times in the coming decade and, thus, motivate researchers to delve into it. The primary focus of their research is to ensure safety, comfort, and efficiency. An autonomous vehicle has several modules responsible for one or more of the aforementioned items. Among these modules, the trajectory planner plays a pivotal role in the safety of the vehicle and the comfort of its passengers. The module is also responsible for respecting kinematic constraints and any applicable road constraints. In this paper, a novel online spatial-temporal graph trajectory planner is introduced to generate safe and comfortable trajectories. First, a spatial-temporal graph is constructed using the autonomous vehicle, its surrounding vehicles, and virtual nodes along the road with respect to the vehicle itself. Next, the graph is forwarded into a sequential network to obtain the desired states. To support the planner, a simple behavioral layer is also presented that determines kinematic constraints for the planner. Furthermore, a novel potential function is also proposed to train the network. Finally, the proposed planner is tested on three different complex driving tasks, and the performance is compared with two frequently used methods. The results show that the proposed planner generates safe and feasible trajectories while achieving similar or longer distances in the forward direction and comparable comfort ride.
Hydrogen is the most plentiful chemical element in the visible universe. The mass composition of the visible universe is approximately 74% hydrogen, 24% helium, 1% oxygen, and the rest of all other chemical elements is about 1%. Hydrogen has the symbol H and the atomic number 1. It is placed in the first position in Mendeleev's periodic table of elements, in the upper left corner. It is an easily flammable, colorless, tasteless, odorless gas, and in nature, it is found mainly in the form of the diatomic molecule, H 2. With an atomic mass unit of 1.00794, hydrogen is the lightest chemical element. Etymologically, the word hydrogen is a combination of two Greek words hydor and gennan meaning:
water producer. Hydrogen (H 2) has a very good calorific value per mass unit 143 MJ/kg which is 3.33 times more than the calorific value of kerosene or diesel fuel. Green hydrogen (clean hydrogen or renewable hydrogen) is produced by electrolysis of water (splitting of water into hydrogen and oxygen) using electricity from renewable sources such as solar energy, wind energy, seawater waves energy, or tidal power. Green hydrogen is an environmentally-friendly power source (no harmful gases). This paper presents recent documentary research by the authors on green hydrogen as an environmentally-friendly power source: for space rocket launches and for hydrogen fuel cells used in the space shuttle as electrical power generators and drinking water generators from launch to return from the space mission; as fuel for a modified turboprop engine (Rolls-Royce and easyJet); as fuel for the European Destinus aircraft using the Jungfrau technology system for a planned hypersonic aircraft using a modified commercial afterburning engine; as fuel for modified gas turbine engines and hydrogen fuel cells to supply electrical power to supplement the gas turbine for the Airbus ZEROe aircraft, etc.
As the interest in probing deep space increases, it is necessary to enhance the autonomous navigation capabilities of the spacecraft. Since traditional navigation methods rely on ground-based radiometric tracking, the vehicle has a significant communication delay resulting in no ability to handle unexpected situations on time. Image-based optical navigation allows interplanetary spacecraft to determine their orbits autonomously. This paper explores how to accurately extract optical observations from the original images to perform autonomous navigation. First, we introduce a simple and efficient idea to locate valuable contours of the celestial body based on gradient variations. Then, we establish a rough estimation with RANSAC to remove the outliers around the edges. Next, we propose a refined estimation based on the hybrid genetic algorithm to precisely estimate the navigation observations. Lastly, numerous experiments have confirmed that our method achieves outstanding accuracy and robustness.
Accurate predictions of the blade response in a multi-row compressor is one of the most important tasks within the design process of compressor blades. Some recent studies have shown that the decoupled method considering only the stator disturbances cannot obtain accurate results for cases with strong rotor–stator interactions, especially for the interaction between the rotor and downstream stator, and the coupled method with multi-row configurations is necessary. Factors determine what computational domains to model need to be clarified to find a balance between accuracy requirements and computational costs. To this end, this study conducted full-annulus unsteady calculations with decoupled and coupled configurations to investigate the forced response of an embedded compressor rotor induced by upstream and downstream stator disturbances and rotor–stator interactions, respectively. The results show that the upstream IGV disturbances were dominated by the wake, and the IGV and S1 potential fields had little effect on the R1 response. Meanwhile, the IGV–R1 interactions and S1–R1 interactions were dominated by one cut-on mode, respectively. The comparisons of the blade vibration amplitude and the unsteady pressure field calculated by decoupled and coupled methods revealed the mechanism of the forced response, namely, for the R1 response induced by upstream aerodynamic disturbances, the dominant excitation source was the IGV wake, and the blade vibration amplitude can be predicted by the decoupled method. In terms of the response induced by downstream disturbances, the cut-on S1-R1-interaction mode was dominant and the use of the decoupled method without considering its influence will lead to an inaccurate prediction. This study concluded that the formation process of rotor–stator interactions was the key factor that determines whether the decoupled method or coupled method should be used, and analogized a process independent of the downstream stator disturbance. The results can provide a preliminary configuration for accurate and efficient blade response predictions and explain the reason why including downstream stator vanes is very important.
Richard Schubert, Marcus Nolte, Arnaud de La Fortelle
et al.
Advanced driving functions, for assistance or full automation, require strong guarantees to be deployed. This means that such functions may not be available all the time, like now commercially available SAE Level 3 modes that are made available only on some roads and at law speeds. The specification of such restriction is described technically in the Operational Design Domain (ODD) which is a fundamental concept for the design of automated driving systems (ADS). In this work, we focus on the example of trajectory planning and control which are crucial functions for SAE level 4+ vehicles and often rely on model-based methods. Hence, the quality of the underlying models has to be evaluated with respect to the ODD. Mathematical analyses such as uncertainty and sensitivity analysis support the quantitative assessment of model quality in general. In this paper, we present a new approach to assess the quality of vehicle dynamics models using an ODD-centric sensitivity analysis. The sensitivity analysis framework is implemented for a 10-DoF nonlinear double-track vehicle dynamics model used inside a model-predictive trajectory controller. The model sensitivity is evaluated with respect to given ODD and maneuver parameters. Based on the results, ODD-compliant behavior generation strategies with the goal of minimizing model sensitivity are outlined.
We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to navigate through a conflict area. Adversarial attacks such as Sybil attacks can cause safety violations resulting in collisions and traffic jams. In addition, uncooperative (but not necessarily adversarial) CAVs can also induce similar adversarial effects on the traffic network. We propose a decentralized resilient control and coordination scheme that mitigates the effects of adversarial attacks and uncooperative CAVs by utilizing a trust framework. Our trust-aware scheme can guarantee safe collision free coordination and mitigate traffic jams. Simulation results validate the theoretical guarantee of our proposed scheme, and demonstrate that it can effectively mitigate adversarial effects across different traffic scenarios.
This paper presents a novel energy-efficient motion planning algorithm for Connected Autonomous Vehicles (CAVs) on urban roads. The approach consists of two components: a decision-making algorithm and an optimization-based trajectory planner. The decision-making algorithm leverages Signal Phase and Timing (SPaT) information from connected traffic lights to select a lane with the aim of reducing energy consumption. The algorithm is based on a heuristic rule which is learned from human driving data. The optimization-based trajectory planner generates a safe, smooth, and energy-efficient trajectory toward the selected lane. The proposed strategy is experimentally evaluated in a Vehicle-in-the-Loop (VIL) setting, where a real test vehicle receives SPaT information from both actual and virtual traffic lights and autonomously drives on a testing site, while the surrounding vehicles are simulated. The results demonstrate that the use of SPaT information in autonomous driving leads to improved energy efficiency, with the proposed strategy saving 37.1% energy consumption compared to a lane-keeping algorithm.
Driven by deep learning techniques, perception technology in autonomous driving has developed rapidly in recent years, enabling vehicles to accurately detect and interpret surrounding environment for safe and efficient navigation. To achieve accurate and robust perception capabilities, autonomous vehicles are often equipped with multiple sensors, making sensor fusion a crucial part of the perception system. Among these fused sensors, radars and cameras enable a complementary and cost-effective perception of the surrounding environment regardless of lighting and weather conditions. This review aims to provide a comprehensive guideline for radar-camera fusion, particularly concentrating on perception tasks related to object detection and semantic segmentation.Based on the principles of the radar and camera sensors, we delve into the data processing process and representations, followed by an in-depth analysis and summary of radar-camera fusion datasets. In the review of methodologies in radar-camera fusion, we address interrogative questions, including "why to fuse", "what to fuse", "where to fuse", "when to fuse", and "how to fuse", subsequently discussing various challenges and potential research directions within this domain. To ease the retrieval and comparison of datasets and fusion methods, we also provide an interactive website: https://radar-camera-fusion.github.io.