Search Results (2,763)

Reliable 3D scene understanding is a fundamental requirement for intelligent machines in autonomous transportation systems, as on-board perception must remain accurate and stable across diverse environments and sensing conditions. However, LiDAR point clouds acquired in real traffic scenes are often sparse and irregular, and they exhibit heterogeneous sampling patterns that hinder consistent and fine-grained semantic interpretation. To address these challenges, this paper proposes LVCA-Net, a lightweight voxel–coordinate attention framework designed for efficient LiDAR-based 3D semantic segmentation in autonomous driving scenarios. The architecture integrates (i) an anisotropic depthwise residual module for direction-aware geometric feature extraction, (ii) a hierarchical LiteDown–LiteUp pathway for multi-scale feature fusion, and (iii) a Coordinate-Guided Sparse Semantic Module that enhances spatial consistency in a cylindrical voxel space while maintaining computational sparsity. Experiments on the SemanticKITTI and nuScenes benchmarks demonstrate that LVCA-Net achieves 67.17% mean Intersection over Union (mIoU) and 91.79% overall accuracy on SemanticKITTI, as well as 77.1% mIoU on nuScenes, while maintaining real-time inference efficiency. These results indicate that LVCA-Net delivers scalable and robust 3D scene understanding with high semantic precision for LiDAR-only perception, making it well suited for deployment in autonomous vehicles and other safety-critical intelligent systems. Full article

The membrane oxygenator serves as the core component of extracorporeal life support systems, and its gas exchange efficiency critically influences clinical outcomes. However, gas transfer is predominantly limited by the diffusion barrier within the blood-side boundary layer, where saturated red blood cells accumulate. Current research focuses mainly on static approaches such as optimizing fiber bundle configuration to promote passive blood mixing or modifying material properties, which are fixed after fabrication. In contrast, dynamic blood flow control remains an underexplored avenue for enhancing oxygenator performance. This study proposes an active pulsatile flow control method that disrupts the boundary layer barrier by optimizing periodic flow profiles, thereby directly improving gas exchange. A deep reinforcement learning framework integrating proximal policy optimization and long short-term memory networks was developed to autonomously search for optimal flow waveforms under constant flow conditions. A simplified stacked-plate membrane oxygenator was specially designed as the experimental platform to minimize flow path interference. Experimental results demonstrate that the optimized pulsatile profile increases the oxygen transfer rate by $20.64\%$ without compromising hemocompatibility. Full article

This Editorial presents an integrative overview of the Special Issue “Advanced Autonomous Systems and Artificial Intelligence Stage”, which assembles fifteen peer-reviewed articles dedicated to the recent evolution of AI-enabled and autonomous systems. The contributions span a broad spectrum of domains, including renewable energy and power systems, intelligent transportation, agricultural robotics, clinical and assistive technologies, mobile robotic platforms, and space robotics. Across these diverse applications, the collection highlights core research themes such as robust perception and navigation, semantic and multi modal sensing, resource-efficient embedded inference, human–machine interaction, sustainable infrastructures, and validation frameworks for safety-critical systems. Several articles demonstrate how physical modeling, hybrid control architectures, deep learning, and data-driven methods can be combined to enhance operational robustness, reliability, and autonomy in real-world environments. Other works address challenges related to fall detection, predictive maintenance, teleoperation safety, and the deployment of intelligent systems in large-scale or mission-critical contexts. Overall, this Special Issue offers a consolidated and rigorous academic synthesis of current advances in Autonomous Systems and Artificial Intelligence, providing researchers and practitioners with a valuable reference for understanding emerging trends, practical implementations, and future research directions. Full article

The comprehensive evaluation of factors that increase the difficulty of autonomous driving in various complex traffic situations and diverse roadway geometries within living lab environments is of great interest, particularly in developing sustainable urban mobility systems. This study introduces a novel methodology for assessing autonomous driving vulnerabilities and identifying urban traffic segments susceptible to autonomous driving risks in mixed traffic situations where autonomous and manual vehicles coexist. A microscopic traffic simulation network that realistically represents conditions in a living lab demonstration area was used, and twelve safety indicators capturing longitudinal safety and vehicle interaction dynamics were employed to compute an integrated risk score (IRS). The promising weighting of each indicator was derived through decision tree method calibrated with real-world traffic accident data, allowing precise localization of vulnerability hotspots for autonomous driving. The analysis results indicate that an IRS-based hotspot was identified at an unsignalized intersection, with an IRS value of 0.845. In addition, analytical results were examined comprehensively from multiple perspectives to develop actionable improvement strategies that contribute to long-term sustainability, encompassing roadway and traffic facility enhancements, provision of infrastructure guidance information, autonomous vehicle route planning, and enforcement measures. Furthermore, this study categorized and analyzed the characteristics of high-risk road sections with similar geometric features to systematically derive effective traffic safety countermeasures. This research offers a systematic, practical framework for safety evaluation in autonomous driving living labs, delivering actionable guidelines to support infrastructure planning and validate sustainable autonomous mobility. Full article

Event camera vision systems have recently been gaining traction as swift and agile sensing devices in the field of unmanned aerial vehicles (UAVs). Despite their inherent superior capabilities covering high dynamic range, microsecond-level temporary resolution, and robustness to motion distortion which allow them to capture fast and subtle scene changes that conventional frame-based cameras often miss, their utilization has yet to be widespread. This is due to challenges like insufficient real-world validation, unstandardized simulation platforms, limited hardware integration and a lack of ground truth datasets. This systematic review paper presents an investigation that seeks to explore the dynamic vision sensor christened event camera and its integration to (UAVs). The review synthesized peer-reviewed articles between 2015 and 2025 across five thematic domains, datasets, simulation tools, algorithmic paradigms, application areas and future directions, using the Scopus and Web of Science databases. This review reveals that event cameras outperformed traditional frame-based systems in terms of latency and robustness to motion blur and lighting conditions, enabling reactive and precise UAV control. However, challenges remain in standardizing evaluation metrics, improving hardware integration, and expanding annotated datasets, which are vital for adopting event cameras as reliable components in autonomous UAV systems. Full article

The paper introduces a real-time digital-twin controller that manages evacuation routes while operating GEEM for emergency energy management during building fires. The system consists of three interconnected parts which include (i) a physics-based hazard surrogate for short-term smoke and temperature field prediction from sensor data (ii), a router system that manages path updates for individual users and controls exposure and network congestion (iii), and an energy management system that regulates the exchange between PV power and battery storage and diesel fuel and grid electricity to preserve vital life-safety operations while reducing both power usage and environmental carbon output. The system operates through independent modules that function autonomously to preserve operational stability when sensors face delays or communication failures, and it meets Industry 5.0 requirements through its implementation of auditable policy controls for hazard penalties, fairness weight, and battery reserve floor settings. We evaluate the controller in co-simulation across multiple building layouts and feeder constraints. The proposed method achieves superior performance to existing AI/RL baselines because it reduces near-worst-case egress time (\(T_{95}\) and worst-case exposure) and decreases both event energy \(E_{\mathrm{event}}\) and CO₂-equivalent \(CO_{\mathrm{2event}}\) while upholding all capacity, exposure cap, and grid import limit constraints. A high-VRE, tight-feeder stress test shows how reserve management, flexible-load shedding, and PV curtailment can achieve trade-offs between unserved critical load \(U_{\mathrm{energy}}\) and emissions. The team delivers implementation details together with reporting templates to assist researchers in reaching reproducibility goals. The research shows that emergency energy systems, which integrate evacuation systems, achieve better safety results and environmental advantages that enable smart-city integration through digital thread operations throughout design, commissioning, and operational stages. Full article

The rapid evolution of autonomous vehicles necessitates increasingly sophisticated cognitive capabilities to handle complex, unstructured environments. This study explores the cognitive potential of Large Language Models (LLMs) in autonomous navigation and swarm control systems, addressing the limitations of traditional rule-based approaches. The research investigates whether multimodal LLMs, specifically a customized version of LLaVA 7B (Large Language and Vision Assistant), can serve as a central decision-making unit for autonomous vehicles equipped with cameras and distance sensors. The developed prototype integrates a Raspberry Pi module for data acquisition and motor control with a main computational unit running the LLM via the Ollama platform. Communication between modules combines REST API for sensory data transfer and TCP sockets for real-time command exchange. Without fine-tuning, the system relies on advanced prompt engineering and context management to ensure consistent reasoning and structured JSON-based control outputs. Experimental results demonstrate that the model can interpret real-time visual and distance data to generate reliable driving commands and descriptive situational reasoning. These findings suggest that LLMs possess emerging cognitive abilities applicable to real-world robotic navigation and lay the groundwork for future swarm systems capable of cooperative exploration and decision-making in dynamic environments. These insights are particularly valuable for researchers in swarm robotics and developers of edge-AI systems seeking efficient, multimodal navigation solutions. Full article

The operational efficiency and precision of boom sprayers, as critical equipment for protecting field crops, are vital to global food security and agricultural sustainability. In precision agriculture systems, achieving uniform pesticide application fundamentally depends on maintaining stable boom posture during operation. However, severe boom vibration not only directly causes issues like missed spraying, double spraying, and pesticide drift but also represents a critical bottleneck constraining its functional realization in cutting-edge applications. Despite its importance, achieving absolute boom stability is a complex task. Its suspension system design faces a fundamental technical contradiction: effectively isolating high-frequency vehicle vibrations caused by ground surfaces while precisely following large-scale, low-frequency slope variations in the field. This paper systematically traces the evolutionary path of self-balancing boom technology in addressing this core contradiction. First, the paper conducts a dynamic analysis of the root causes of boom instability and the mechanism of its detrimental physical effects on spray quality. This serves as a foundation for the subsequent discussion on technical approaches for boom support and balancing systems. The paper also delves into the evolution of sensing technology, from “single-point height measurement” to “point cloud morphology perception,” and provides a detailed analysis of control strategies from classical PID to modern robust control and artificial intelligence methods. Furthermore, this paper explores the deep integration of this technology with precision agriculture applications, such as variable rate application and autonomous navigation. In conclusion, the paper summarizes the main challenges facing current technology and outlines future development trends, aiming to provide a comprehensive reference for research and development in this field. Full article

Urban mobility in Lisbon faces persistent constraints driven not only by congestion, parking scarcity, and emissions but also by deeper structural issues such as fragmented governance and limited cross-peripheral public transport connectivity. These shortcomings hinder integrated mobility planning and motivate the exploration of Shared Autonomous Vehicles (SAVs) as a complementary urban transport solution. Existing SAV frameworks rarely integrate governance coordination, data interoperability, and contextual adaptation for medium-sized European cities. This study addresses this gap by designing and validating a reference model for the deployment of SAVs in Lisbon using a design–science approach combining a literature review, enterprise architecture modelling, and stakeholder validation. The proposed model contributes the following: (i) a governance coordination framework for multi-actor urban mobility ecosystems; (ii) an integrated digital and application architecture supporting multimodal services and user trust mechanisms; and (iii) a technology layer enabling V2X communication and interoperable mobility data flows. The model is demonstrated through Lisbon-specific scenarios aligned with local sustainable mobility strategies. Scenario interpretation is informed by literature-based performance benchmarks—including travel-time reductions of 13–42%, energy-use reductions of 12%, and GHG reductions of 5.6%—which are used as reference indicators rather than simulation outputs. The resulting framework bridges strategic policy and implementable system architecture, supporting the transition towards integrated, sustainable, and autonomous mobility in medium-sized European cities. Full article

This systematic literature review explores the impacts of autonomous and connected mobility systems on sustainable road transportation. The evaluation process involves a multifaceted analysis, encompassing the assessment of their capacity to mitigate accidents, energy consumption, emissions, and urban traffic congestion. As a novel approach, this paper analyses the parameters of user acceptance of technology and how these are reflected in the overall impacts of automated and connected driving. Thus, based on a behavioral intention to use the new technology model, we aim to analyze the state of the art of the overall impacts that may be correlated with individual interests. To this end, a multi-factor approach is applied and potential interactions between factors that may arise are studied in a holistic and quantitative assessment of their combined effects on transportation systems. This impact assessment is a significant challenge, as numerous factors come into play, leading to conflicting effects. Since there is no significant penetration of vehicles with medium or high levels of automation, conclusions are often obtained through simulations or estimates based on hypotheses that must be considered when analyzing the results and can lead to significant dispersion. The results confirm that these technologies can substantially improve road safety, traffic efficiency, and environmental performance. However, their large-scale deployment will critically depend on the establishment of coherent regulatory frameworks, infrastructural readiness, and societal acceptance. Comprehensive stakeholder collaboration, incorporating industry, regulatory authorities, and society, is essential to successfully address existing concerns, facilitate technological integration, and maximize the societal benefits of these transformative mobility systems. Full article

Autonomous underwater cleaning in water pools requires reliable perception, efficient coverage path planning, and robust control. However, existing autonomous underwater vehicle (AUV) cleaning systems often suffer from fragmented software frameworks that limit end-to-end performance. To address these challenges, this paper proposes an integrated vision-based autonomous underwater cleaning system that combines global-camera AprilTag localization, YOLOv8-based dirt detection, and a multi-scale A* coverage path planning algorithm. The perception and planning modules run on a host computer system, while a NanoPi-based controller executes motion commands through a lightweight JSON-RPC protocol over Ethernet. This architecture ensures real-time coordination between visual sensing, planning, and hierarchical control. Experiments conducted in a simulated pool environment demonstrate that the proposed system achieves accurate localization, efficient planning, and reliable cleaning without blind spots. The results highlight the effectiveness of integrating vision, multi-scale planning, and lightweight embedded control for autonomous underwater cleaning tasks. Full article

Object detection plays a pivotal role in safety-critical applications, including autonomous driving, intelligent surveillance, and unmanned aerial systems. However, many state-of-the-art detectors remain highly resource-intensive; their large parameter sizes and substantial floating-point operations make it difficult to balance accuracy and efficiency, particularly under constrained computational budgets. To mitigate this accuracy–efficiency trade-off, we propose FE-YOLOv8, a lightweight yet more effective variant of YOLOv8 (You Only Look Once version 8). Specifically, two architectural refinements are introduced: (1) C2f-Faster (Cross-Stage-Partial 2-Conv Faster Block) modules embedded in both the backbone and neck, where PConv (partial convolution) prunes redundant computations without diminishing representational capacity; and (2) an EfficientHead detection head that integrates EMSConv (Efficient Multi-Scale Convolution) to enhance multi-scale feature fusion while simplifying the head design and maintaining low computational complexity. Extensive ablation and comparative experiments on the SODA-10M dataset show that FE-YOLOv8 reduces the parameter count by 31.09% and the computational cost by 43.31% relative to baseline YOLOv8 while achieving comparable or superior mean Average Precision (mAP). Generalization experiments conducted on the BDD100K dataset further validate these improvements, demonstrating that FE-YOLOv8 achieves a favorable balance between accuracy and efficiency within the YOLOv8 family and provides new architectural insights for lightweight object detector design. Full article

The exponential growth in the usage of textual data across industries and data sharing across institutions underscores the critical need for frameworks that effectively balance data utility and privacy. This paper proposes an innovative agentic AI-based framework specifically tailored for textual data, integrating user-driven qualitative inputs, differential privacy, and generative AI methodologies. The framework comprises four interlinked topics: (1) A novel quantitative approach that translates qualitative user inputs, such as textual completeness, relevance, or coherence, into precise, context-aware utility thresholds through semantic embedding and adaptive metric mapping. (2) A differential privacy-driven mechanism optimizing text embedding perturbations, dynamically balancing semantic fidelity against rigorous privacy constraints. (3) An advanced generative AI approach to synthesize and augment textual datasets, preserving semantic coherence while minimizing sensitive information leakage. (4) An adaptable dataset-dependent optimization system that autonomously profiles textual datasets, selects dataset-specific privacy strategies (e.g., anonymization, paraphrasing), and adapts in real-time to evolving privacy and utility requirements. Each topic is operationalized via specialized agentic modules with explicit mathematical formulations and inter-agent coordination, establishing a robust and adaptive solution for modern textual data challenges. Full article

Reliable detection of unknown signals is essential for ensuring the robustness of underwater acoustic sensing systems, particularly in maritime security and autonomous navigation. However, Conventional deep learning models often exhibit overconfidence when encountering unknown signals and are unable to quantify predictive uncertainty due to their deterministic inference process. To address these limitations, this study proposes a novelty detection framework that integrates an out-of-distribution detector for neural networks (ODIN) with Monte Carlo (MC) dropout. ODIN mitigates model overconfidence and enhances the separability between known and unknown signals through softmax probability calibration, while MC dropout introduces stochasticity via multiple forward passes to estimate predictive uncertainty—an element critical for stable sensing in real-world underwater environments. The resulting probabilistic outputs are modeled using Gaussian mixture models fitted to ODIN-calibrated softmax distributions of known classes. The Kullback–Leibler divergence is then employed to quantify deviations of test samples from known class behavior. Experimental evaluations on the DeepShip dataset demonstrate that the proposed method achieves, on average, a 9.5% and 5.39% increase in area under the receiver operating characteristic curve, and a 7.82% and 2.63% reduction in false positive rate at 95% true positive rate, compared to the MC dropout and ODIN baseline, respectively. These results confirm that integrating stochastic inference with ODIN significantly enhances the stability and reliability of novelty detection in underwater acoustic environments. Full article

Unmanned Aerial Vehicle (UAV) operating in complex environments require guaranteed safety mechanisms while maintaining high performance. This study addresses the challenge of ensuring strict flight safety during policy execution by implementing a Control Barrier Function (CBF) as a real-time action filter, thereby providing a rigorous, formal guarantee. The methodology integrates the primary Proximal Policy Optimization (PPO) algorithm with a Demonstration-Guided Reinforcement Learning (DGRL), which leverages Proportional–Integral–Derivative (PID) expert trajectories to significantly accelerate learning convergence and enhance sample efficiency. Comprehensive results confirm the efficacy of the hybrid architecture, demonstrating a significant reduction in constraint violations and proving the framework’s ability to substantially accelerate training compared to PPO. In conclusion, the proposed methodology successfully unifies formal safety guarantees with efficient, adaptive reinforcement learning, making it highly suitable for safety-critical autonomous systems. Full article