Search Results (196)

Sub-Saharan Africa’s cereal yields remain ~60% below global benchmarks, while unmanned aerial vehicle (UAV) adoption in smallholder systems averages below 2–3% across major economies, revealing a performance–adoption disconnect that requires systematic investigation. This Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 synthesis of 127 sources quantifies the performance of UAV sensors and identifies mechanisms that constrain their adoption across regional agricultural systems. Random-effects meta-analysis synthesized evidence from 81 quantitative studies, yielding 101 total observations. Pooled detection accuracy was estimated from 49 studies contributing 52 observations (mean 90.2%, 95% confidence interval (CI): 89.8–92.6%). Yield prediction performance was assessed from 32 studies contributing 49 observations (pooled coefficient of determination (R²) = 0.841, 95% CI: 0.827–0.855), validating technical feasibility. Cost-effectiveness analysis reveals significant performance–price differentiation: red-green-blue (RGB) sensors achieve 89.4% accuracy at United States Dollar (USD) 16.50 per percentage point versus hyperspectral systems at 93.7% accuracy but at USD 132.17 per point, resulting in a 25.6 times cost differential. Yield prediction models demonstrate robust performance (R² = 0.81; cereal crops R² = 0.82). Barrier analysis identifies economic constraints as the primary limiter, with capital requirements reaching 0.8–3.1 times the annual smallholder income. Infrastructure deficits impose secondary constraints, particularly in rural electrification, below 50%. Case study synthesis reveals that coordinated interventions addressing multiple barriers simultaneously—cooperative ownership, off-grid infrastructure, and streamlined regulation—achieve substantially higher adoption than isolated approaches. Engineering economics positions RGB platforms for individual deployment and multispectral systems for cooperative scales (20–50 farmers), establishing feasible deployment pathways for tens of million regional smallholder operations. Full article

The rapid advancement of connected and autonomous vehicles (CAVs) has the potential to revolutionize road transportation, promising significant improvements in safety, efficiency, and sustainability. However, traditional CAV architectures are predominantly modular and rule-based. They struggle with interaction, cooperation, and adaptability in complex mixed-traffic environments. Moreover, the substantial infrastructure investment required and the absence of compelling killer applications have limited large-scale deployment of CAVs and roadside units (RSUs), resulting in insufficient penetration to realize the full safety benefits of CAV applications and creating a deployment stalemate. To address the above challenges, this paper proposes an innovative connected autonomous vehicle system, termed AQ-CAV, which leverages recent advances in AI agents and QR codes. AI agents are employed to enable cooperative, self-adaptive, and intelligent vehicular behavior, while QR codes provide a cost-effective, accessible, robust, and scalable mechanism for supporting CAV deployment. We first analyze existing CAV systems and identify their fundamental limitations. We then present the architectural design of the AQ-CAV system, detailing the components and functionalities of vehicle-side and infrastructure-side agents, inter-agent communication and coordination mechanisms, and QR code-based authentication for AQ-CAV operations. Representative applications of the AQ-CAV system are investigated, including a case study on emergency response. Preliminary results demonstrate the feasibility and effectiveness of the proposed system, which achieves significant safety improvements at low system cost. Finally, we discuss the key challenges faced by AQ-CAV and outline future research directions that require exploration to fully realize its potential. Full article

Copper is a strategic raw material and an important component in electric motors, widely used across industries because of its excellent conductivity and recyclability. It plays an important role in the transformation from fossil fuel-based systems to green, electrified systems. However, substantial material losses continue throughout the lifecycle of electric motors, even with copper’s intrinsic capacity for circularity. Also, copper’s increasing demand, which is driven by the emergence of electric vehicles, industrial electrification, and renewable energy infrastructure, poses questions regarding its sustainable supply. The recovery of secondary copper sources from end-of-life (EoL) products is becoming more and more important in this context. However, it is still difficult to achieve circularity of copper, especially from industrial electric motors. This study investigates the challenges of closing the loop for copper during the lifecycle of motors in industrial applications. Based on an examination of EoL strategies, material flow insights, and practical investigation, the research pinpoints significant inefficiencies in the current processes. The widespread use of scraping as an approach of end-of-life management is one significant issue. Most of the electric motors are not built to separate their components, which makes both mechanical and manual disassembly difficult. The quality of recovered copper is thus compromised by the dominance of mixed metal shredding methods in the recycling step. This study highlights the need for systemic changes in addition to technical solutions to address copper circularity issues. It requires a focus on circularity in designing, giving disassembly and metal recovery a priority. This study focuses on circularity and its technological challenges in a value chain of copper. It not only identifies different processes such as supply chain disconnections and design constraints, but it also suggests workable solutions to close the copper flow loop in the electric motor sector. Copper quality and recovery is ultimately a problem involving design, technology, and cooperation, in addition to resources. This study supports the transition to a more sustainable and circular electric motor industry by offering a basis for directing such changes in industry practices and prospective EU regulations. Full article

Accurate Unmanned Aerial Vehicle (UAV) positioning is vital for swarm cooperation. However, this remains challenging in situations where Global Navigation Satellite System (GNSS) and other external infrastructures are unavailable. To address this challenge, we propose to use only the onboard Microelectromechanical System Inertial Measurement Unit (MIMU), Magnetic sensor, Monocular camera and Ultra-Wideband (UWB) device to construct a distributed and anchor-free cooperative localization system by tightly fusing the measurements. As the onboard UWB measurements under dynamic motion conditions are noisy and discontinuous, we propose an adaptive adjustment method based on chi-squared detection to effectively filter out inconsistent and false ranging information. Moreover, we introduce the pose-only theory to model the visual measurement, which improves the efficiency and accuracy for visual-inertial processing. A sliding window Extended Kalman Filter (EKF) is constructed to tightly fuse all the measurements, which is capable of working under UWB or visual deprived conditions. Additionally, a novel Multidimensional Scaling-MAP (MDS-MAP) initialization method fuses ranging, MIMU, and geomagnetic data to solve the non-convex optimization problem in ranging-aided Simultaneous Localization and Mapping (SLAM), ensuring fast and accurate swarm absolute pose initialization. To overcome the state consistency challenge inherent in the distributed cooperative structure, we model not only the UWB noisy uncertainty but also the neighbor agent’s position uncertainty in the measurement model. Furthermore, we incorporate the Covariance Intersection (CI) method into our UWB measurement fusion process to address the challenge of unknown correlations between state estimates from different UAVs, ensuring consistent and robust state estimation. To validate the effectiveness of the proposed methods, we have established both simulation and hardware test platforms. The proposed method is compared with state-of-the-art (SOTA) UAV localization approaches designed for GNSS-challenged environments. Extensive experiments demonstrate that our algorithm achieves superior positioning accuracy, higher computing efficiency and better robustness. Moreover, even when vision loss causes other methods to fail, our proposed method continues to operate effectively. Full article

Applications for intelligent cooperative Unmanned Aerial Vehicle (UAV) swarms are rapidly expanding. Efficient and reliable communication is critical for realizing this swarm intelligence, especially in remote areas lacking infrastructure where ad hoc networking is a prevalent approach. However, in such long-distance scenarios, significant propagation delays pose a fundamental challenge to Medium Access Control (MAC) protocols like carrier sense multiple access with collision avoidance (CSMA/CA). This paper theoretically compares random backoff and p-persistent to determine the optimal strategy for these conditions. We present analytical models for both strategies. The model for random backoff reveals its optimal performance is dependent on network topology, making it ill-suited for dynamic swarms. In contrast, our model for p-persistent yields an optimal transmission probability that is independent of the network topology. Simulation results validate our models, showing p-persistent achieves significantly higher throughput (over 40% improvement in an 80-node swarm). We conclude that the topology-independent characteristic of p-persistent makes it a more feasible, more robust, and superior solution for long-distance, dynamic UAV swarm networks. Full article

The use of full-battery electric vehicles is an essential strategy for reducing greenhouse gas emissions and mitigating climate change. This study examined the transition to full-battery electric vehicles by conducting a cross-sectional household survey in 2023 that collected information on vehicle preferences, evaluations, purchase intentions, environmental attitudes, and socioeconomic and demographic characteristics. The results show that among households using a vehicle as their primary mode of transportation, approximately 89% relied on fossil fuel vehicles, whereas only 6% used electric vehicles. The study further finds that acceptance of vehicles during inter-vehicle transitions is closely linked to energy type: households currently owning fossil fuel vehicles exhibited a high likelihood of repurchasing a fossil fuel vehicle, while electric vehicle owners were more inclined to choose another electric vehicle across cities and areas of different sizes. Households that own electric vehicles tend to report higher levels of well-being compared with those that own fossil fuel vehicles. In addition, sufficient charging infrastructure, stronger knowledge of environmental issues, participation in altruistic donation activities, and cooperative behavior positively influenced electric vehicle adoption. These findings suggest several policy implications, including the expansion of charging stations for business and public use, setting reasonable vehicle prices, improving charging speed, developing electric vehicles suitable for large families, and encouraging individuals to gain initial driving experience with electric vehicles to promote adoption. Full article

Based on an enhanced artificial potential field approach, this paper presents a control method for obstacle avoidance in vehicle platoons within Intelligent Vehicle-Infrastructure Cooperative Systems (I-VICS). To enhance safety during maneuvers, an inter-vehicle obstacle avoidance potential field model is established. By integrating virtual forces and a consistency control strategy into the control law, the proposed method effectively handles obstacle avoidance for vehicles operating at large inter-vehicle distances (80–110 m). Experimental validation using real-world trajectory data shows a 34% improvement in trajectory smoothness, as quantified by a proposed Vehicle Trajectory Stability (VTS) metric, leading to significantly safer avoidance maneuvers. A coordinated multi-vehicle obstacle avoidance strategy is further devised using a rotating potential field method, enabling collaborative and safe overall motion planning. Moreover, a path tracking strategy based on virtual force design is introduced to enhance platoon stability and reliability. Future work will focus on collision avoidance for vehicle platoons with varying inter-vehicle distances and will extend the consistency control and cooperative avoidance strategies to longer vehicle platoon to further improve overall traffic safety. Full article

This study presents the design and implementation of a real-time flight-path tracking control system for a quadrotor unmanned aerial vehicle (UAV) capable of accurately following a mobile ground target under dynamic and uncertain environmental conditions. The proposed framework integrates classical fixed-gain PID regulation executed on Pixhawk with its built-in adaptive mechanisms, namely autotuning, hover-throttle learning, and dynamic harmonic notch filtering, to enhance robustness under communication latency and disturbances. No machine learning PID tuning is used on Pixhawk; adaptive features are estimator based rather than ML based. The proposed system addresses critical challenges in trajectory tracking, including real-time delay compensation between the UAV and rover, external perturbations, and the requirement to maintain stable six-degree-of-freedom (DOF) control of altitude, yaw, pitch, and roll. A dynamic mathematical model, formulated using ordinary differential equations with embedded delay elements, is developed to emulate real-world flight behavior and validate control performance. Experimental evaluation demonstrates robust path-tracking accuracy, attitude stability, and responsiveness across diverse terrains and weather conditions, achieving a mean positional error below one meter and effective resilience against an 8.2 ms communication delay. Overall, this work establishes a scalable, computationally efficient, and high-precision control framework for UAV guidance and cooperative ground-target tracking, with potential applications in autonomous navigation, search-and-rescue operations, infrastructure inspection, and intelligent surveillance. The term “delay-aware” in this work refers to the explicit modeling of the measured 8.2 ms end-to-end delay and the use of Pixhawk’s estimator-based adaptive mechanisms, without any machine learning-based PID tuning. Full article

The penetration of distributed energy resources (DERs), such as photovoltaic (PV) generation and electric vehicles (EVs), in distribution systems has been increasing rapidly. At the same time, load demand is rising due to the proliferation of data centers and the growing use of artificial intelligence. These trends have introduced new operational challenges: reverse power flow from PV generation during the day and low-voltage conditions during periods of peak load or when PV output is unavailable. To address these issues, this paper proposes a two-stage adaptive rolling horizon (ARH)-based model predictive control (MPC) framework for coordinated voltage and power factor (PF) control in distribution systems. The proposed framework, designed from the perspective of a distributed energy resource management system (DERMS), integrates EV charging and discharging scheduling with PV- and EV-connected inverter control. In the first stage, the ARH method optimizes EV charging and discharging schedules to regulate voltage levels. In the second stage, optimal power flow analysis is employed to adjust the voltage of distribution lines and the power factor at the substation through reactive power compensation, using PV- and EV-connected inverters. The proposed algorithm aims to maintain stable operation of the distribution system while minimizing PV curtailment by computing optimal control commands based on predicted PV generation, load forecasts, and EV data provided by vehicle owners. Simulation results on the IEEE 37-bus test feeder demonstrate that, under predicted PV and load profiles, the system voltage can be maintained within the normal range of 0.95–1.05 per unit (p.u.), the power factor is improved, and the state-of-charge (SOC) requirements of EV owners are satisfied. These results confirm that the proposed framework enables stable and cooperative operation of the distribution system without the need for additional infrastructure expansion. Full article

In urban arterials, buses face dual constraints from signal-controlled intersections and bus stop dwell demands, and frequent start–stop cycles result in reduced operational efficiency and elevated energy consumption. To address this critical challenge, a sustainable eco-driving strategy integrating offline and online Reinforcement Learning (RL) is proposed in this study. Leveraging real-world trajectory data from a 15.47 km route with 31 stops, the energy consumption characteristics of electric buses under the combined effects of stops and intersections are systematically analyzed, and high energy consumption scenarios are precisely identified. An initial energy saving strategy is first trained using offline RL, and subsequently subjected to online optimization in a vehicle–infrastructure cooperative simulation environment that incorporates three typical stop configurations. The soft actor-critic algorithm is employed to reconcile the dual goals of energy efficiency and ride comfort. Simulation results reveal a significant improvement with the proposed strategy, achieving an 11.2% reduction in energy consumption and a 37.7% decrease in travel time compared to the Krauss benchmark model. This study confirms the effectiveness of RL in boosting the operational sustainability of public transport systems, offering a scalable technical framework to promote the development of green urban mobility. The research findings provide theoretical support and practical references for the large-scale promotion and engineering application of energy saving autonomous driving technology for electric buses. Full article

Cooperative path planning is recognized as a critical technology for Autonomous Underwater Vehicle (AUV) clusters to execute complex marine operations. Through multi-AUV cooperative decision-making, perception limitations of individual robots can be mitigated, thereby significantly enhancing the efficiency of tasks such as deep-sea resource exploration and submarine infrastructure maintenance. However, the underwater environment is characterized by severe disturbances and limited communication, making cooperative path planning for AUV clusters particularly challenging. Currently, this field is still in its early research stage, and there exists an urgent need for the integration of scattered technical achievements to provide theoretical references and directional guidance for relevant researchers. Based on representative studies published in recent years, this paper provides a review of the research progress in three major technical domains: heuristic optimization, reinforcement and deep learning, and graph neural networks integrated with distributed control. The advantages and limitations of different technical approaches are elucidated. In addition to cooperative path planning algorithms, the evolutionary logic and applicable scenarios of each technical school are analyzed. Furthermore, the lack of realism in algorithm training environments has been recognized as a major bottleneck in cooperative path planning for AUV clusters, which significantly limits the transferability of algorithms from simulation-based validation to real-sea applications. This paper aims to comprehensively outline the current research status and development context of the field of AUV cluster cooperative path planning and propose potential future research directions. Full article

Germany’s energy transition to a higher share of renewable energy sources (RESs) is characterized by decentralization, with citizens, cooperatives, SMEs, and municipalities playing a central role. As of early 2025, private individuals own a significant share of renewable energy installations, particularly PV panels, which corresponds to approximately half of the total installed PV power. This trend is driven by physical, technological, and societal factors. Technological advances in battery storage and sector coupling are expected to further decentralize the energy system. Thereby, the electrification of mobility, particularly through electric vehicles (EVs), offers significant storage potential and grid-balancing capabilities via bidirectional charging, although it also introduces challenges, especially for distribution grids during peak loads. Within this work we present a detailed digital twin of the entire distribution grid of the rural German municipality of Wüstenrot. Using grid operator data and transformer measurements, we evaluate strategic expansion scenarios for electromobility, PV and heat pumps based on existing infrastructure and predicted growth in both public and private sectors. A core focus is the intelligent integration of EV charging infrastructure to avoid local overloads and to optimise grid utilisation. Thereby municipally planned and privately driven expansion scenarios are compared, and grid bottlenecks are identified, proposing solutions through charge load management and targeted infrastructure upgrades. This study of Wüstenrot’s low-voltage grid reveals substantial capacity reserves for future integration of heat pumps, electric vehicles (EVs), and photovoltaic systems, supporting the shift to a sustainable energy system. While full-scale expansion would require significant infrastructure investment, mainly due to widespread EV adoption, simple measures like temporary charge load reduction could cut grid stress by up to 51%. Additionally, it is shown that bidirectional charging offers further relief and potential income for EV owners. Full article

As vehicle-centric perception struggles with occlusion and dense traffic, vehicle-infrastructure cooperative perception (VICP) offers a viable route to extend sensing coverage and robustness. This study proposes a feature-level VICP framework that fuses vehicle- and roadside-derived visual features via V2X communication. The model integrates four components: regional feature reconstruction (RFR) for transferring region-specific roadside cues, context-driven channel attention (CDCA) for channel recalibration, uncertainty-weighted fusion (UWF) for confidence-guided weighting, and point sampling voxel fusion (PSVF) for efficient alignment. Evaluated on the DAIR-V2X-C benchmark, our method consistently outperforms state-of-the-art feature-level fusion baselines, achieving improved ${AP}_{3D}$ and ${AP}_{BEV}$ (reported settings: 16.31% and 21.49%, respectively). Ablations show RFR provides the largest single-module gain +3.27% ${AP}_{3D}$ and +3.85% ${AP}_{BEV}$, UWF yields substantial robustness gains, and CDCA offers modest calibration benefits. The framework enhances occlusion handling and cross-view detection while reducing dependence on explicit camera calibration, supporting more generalizable cooperative perception. Full article

The Internet of Vehicles (IoV) relies on Vehicle-to-Everything (V2X) communications to enable cooperative perception among vehicles, infrastructures, and devices, where Vehicle-to-Infrastructure (V2I) links are crucial for reliable transmission. However, the openness of wireless channels exposes IoV to eavesdropping, threatening privacy and security. This paper investigates an Intelligent Reflecting Surface (IRS)-assisted interweave Cognitive IoV (CIoV) network to enhance physical layer security in V2I communications. A non-convex joint optimization problem involving spectrum allocation, transmit power for Vehicle Users (VUs), and IRS phase shifts is formulated. To address this challenge, a heterogeneous multi-agent (HMA) Mamba RainbowDQN algorithm is proposed, where homogeneous VUs and a heterogeneous secondary base station (SBS) act as distinct agents to simplify decision-making. Simulation results show that the proposed method significantly outperform benchmark schemes, achieving a 13.29% improvement in secrecy rate and a 54.2% reduction in secrecy outage probability (SOP). These results confirm the effectiveness of integrating IRS and deep reinforcement learning (DRL) for secure and efficient V2I communications in CIoV networks. Full article

Cooperative Intelligent Transport Systems (C-ITS) have emerged as a key enabler of more efficient, safer, and environmentally sustainable road traffic by allowing vehicles and infrastructure to exchange information and coordinate behavior. To evaluate their benefits, impact assessment studies are essential. However, most existing studies focus on individual C-ITS services in isolation, overlooking how combined deployments influence outcomes. This study addresses this gap by presenting the first systematic evaluation of individual and joint deployments of Cooperative Adaptive Cruise Control (CACC) and Green Light Optimal Speed Advisory (GLOSA) under diverse conditions. A dual-model simulation framework is applied, combining controlled artificial networks with calibrated real-world corridors in Upper Austria. This allows both statistical testing and validation of plausibility in real-world contexts. Key performance indicators include travel time and CO₂ emissions, evaluated across varying lane configurations, numbers of traffic lights, demand levels, and equipment rates. The results demonstrate that C-ITS effectiveness is strongly context-dependent: while CACC generally provides larger efficiency gains, GLOSA yields consistent emission reductions, and the combined deployment offers conditional synergies but may also diminish benefits at high demand. The study contributes a guideline for selecting service configurations based on site conditions, thereby providing practical recommendations for future C-ITS rollouts. Full article