Search Results (116)

This work focuses on the possibilities of generating synthetic data for machine learning in hazard prediction in area-based speed monitoring systems. The purpose of the research conducted was to develop a methodology for generating realistic synthetic data to support the design of a continuous vehicle speed monitoring system to minimize the risk of traffic accidents caused by speeding. The SUMO traffic simulator was used to model driver behavior in the analyzed area and within a given road network. Data from OpenStreetMap and field measurements from over a dozen speed detectors were integrated. Preliminary tests were carried out to record vehicle speeds. Based on these data, several simulation scenarios were run and compared to real-world observations using average speed, the percentage of speed limit violations, root mean square error (RMSE), and percentage compliance. A new metric, the Combined Speed Accuracy Score (CSAS), has been introduced to assess the consistency of simulation results with real-world data. For this study, a basic hazard prediction model was developed using LoRaWAN sensor network data and environmental contextual variables, including time, weather, location, and accident history. The research results in a method for evaluating and selecting the simulation scenario that best represents reality and drivers’ propensities to exceed speed limits. The results and findings demonstrate that it is possible to produce synthetic data with a level of agreement exceeding 90% with real data. Thus, it was shown that it is possible to generate synthetic data for machine learning in hazard prediction for area-based speed control systems using traffic simulators. Full article

This paper presents a behavior tree-based control architecture for end-to-end mission planning of an autonomous underwater vehicle (AUV) collaborating with a moving mothership in dynamic marine environments. The framework is organized into three phases—prepare and launch, execute the mission, and retrieval and docking—each encapsulated in an independent sub-tree to enable modular error handling and seamless phase transitions. The AUV and mothership operate entirely underwater, with real-time docking to a moving platform. An extended Kalman filter (EKF) fuses data from inertial, pressure, and acoustic sensors for accurate navigation and state estimation. At the same time, obstacle avoidance leverages forward-looking sonar (FLS)-based potential field methods to react to unpredictable underwater hazards. The system is implemented on the robot operating system (ROS) and validated in the Stonefish physics engine simulator. Simulation results demonstrate reliable mission execution, successful dynamic docking under communication delays and sensor noise, and robust retrieval from injected faults, confirming the validity and stability of the proposed architecture. Full article

Lithium–ion (Li–ion) batteries are fundamental for advancing intelligent and sustainable transportation, particularly in electric vehicles, due to their long lifespan, high energy density, and strong power efficiency. Ensuring the safety and reliability of EV batteries remains a critical challenge, as undetected faults can lead to hazardous failures or gradual performance degradation. While numerous studies have addressed battery fault detection, most existing reviews adopt isolated perspectives, often overlooking interdisciplinary and intelligent approaches. This paper presents a comprehensive review of advanced battery fault detection using modern machine learning, deep learning, and hybrid methods. It also discusses the pressing challenges in the field, including limited fault data, real-time processing constraints, model adaptability across battery types, and the need for explainable AI. Furthermore, emerging AI approaches such as transformers, graph neural networks, physics-informed models, edge computing, and large language models present new opportunities for intelligent and scalable battery fault detection. Looking ahead, these frameworks, combined with AI-driven strategies, can enhance diagnostic precision, extend battery life, and strengthen safety while enabling proactive fault prevention and building trust in EV systems. Full article

From the point of view of the intelligent operation and maintenance of high-speed train tracks, this paper examines the research status of high-speed train rail damage detection technology in the field of high-speed train track operation and maintenance detection in recent years, summarizes the damage detection methods for high-speed trains, and compares and analyzes different detection technologies and application research results. The analysis results show that the detection methods for high-speed train rail damage mainly focus on the research and application of non-destructive testing technology and methods, as well as testing platform equipment. Detection platforms and equipment include a new type of vortex meter, integrated track recording vehicles, laser rangefinders, thermal sensors, laser vision systems, LiDAR, new ultrasonic detectors, rail detection vehicles, rail detection robots, laser on-board rail detection systems, track recorders, self-moving trolleys, etc. The main research and application methods include electromagnetic detection, optical detection, ultrasonic guided wave detection, acoustic emission detection, ray detection, vortex detection, and vibration detection. In recent years, the most widely studied and applied methods have been rail detection based on LiDAR detection, ultrasonic detection, eddy current detection, and optical detection. The most important optical detection method is machine vision detection. Ultrasonic detection can detect internal damage of the rail. LiDAR detection can detect dirt around the rail and the surface, but the cost of this kind of equipment is very high. And the application cost is also very high. In the future, for high-speed railway rail damage detection, the damage standards must be followed first. In terms of rail geometric parameters, the domestic standard (TB 10754-2018) requires a gauge deviation of ±1 mm, a track direction deviation of 0.3 mm/10 m, and a height deviation of 0.5 mm/10 m, and some indicators are stricter than European standard EN-13848. In terms of damage detection, domestic flaw detection vehicles have achieved millimeter-level accuracy in crack detection in rail heads, rail waists, and other parts, with a damage detection rate of over 85%. The accuracy of identifying track components by the drone detection system is 93.6%, and the identification rate of potential safety hazards is 81.8%. There is a certain gap with international standards, and standards such as EN 13848 have stricter requirements for testing cycles and data storage, especially in quantifying damage detection requirements, real-time damage data, and safety, which will be the key research and development contents and directions in the future. Full article

In recent years, C-ITS services have been extensively specified, tested, and deployed, leading to their first commercial applications. While technical advancements are progressing, the human factor remains crucial for widespread system implementation. The paper presents results of two field studies on user acceptance evaluations focusing on six use cases. Eighteen drivers participated in highway tests, while over 70 individuals responded to an online survey. The empirical results are discussed considering related literature. A structured literature review was conducted, starting with 426 papers, of which 32 were deeply analysed. The key findings of the activities are that the compliance rate is extremely high for safety-related services like hazard warning. However, compliance rates differ depending on the use case. People trust information coming from road operators compared to other sources of traffic information. In-vehicle information does not distract drivers from driving and must be clear and easy to understand. While user acceptance is high, particularly for safety-related services, there remains a need for clearer communication about C-ITS benefits to enhance transparency and trust. Full article

This article presents the design, implementation, and validation of a Remotely Piloted Safety Cone (RPSC), an autonomous robotic system developed to enhance safety and operational efficiency in road maintenance. The RPSC addresses challenges associated with road works, including workers’ exposure to traffic hazards and inefficiencies of traditional traffic cones, such as manual placement and retrieval, limited visibility in low-light conditions, and inability to adapt to dynamic changes in work zones. In contrast, the RPSC offers autonomous mobility, advanced visual signalling, and real-time communication capabilities, significantly improving safety and operational flexibility during maintenance tasks. The RPSC integrates sensor fusion, combining Global Navigation Satellite System (GNSS) with Real-Time Kinematic (RTK) for precise positioning, Inertial Measurement Unit (IMU) and encoders for accurate odometry, and obstacle detection sensors within an optimised navigation framework using Robot Operating System (ROS2) and Micro Air Vehicle Link (MAVLink) protocols. Complying with European regulations, the RPSC ensures structural integrity, visibility, stability, and regulatory compliance. Safety features include emergency stop capabilities, visual alarms, autonomous safety routines, and edge computing for rapid responsiveness. Field tests validated positioning accuracy below 30 cm, route deviations under 15 cm, and obstacle detection up to 4 m, significantly improved by Kalman filtering, aligning with digitalisation, sustainability, and occupational risk prevention objectives. Full article

Camellia oleifera harvester is a compact agricultural vehicle utilized in plantations located in China’s red soil hilly regions. To enhance its functionality and off-road performance, additional electronic devices and a more powerful powertrain system have been integrated within the engine compartment. However, the increased component density has resulted in constrained heat dissipation space, leading to critical issues including insufficient engine power, delayed control response, and reduced vibration frequency of the harvesting device. These thermal problems significantly compromise operational efficiency and pose safety hazards to operators. To address these heat dissipation challenges, this study proposes a collaborative optimization approach integrating computational fluid dynamics (CFD) simulation with an Adaptive Particle Swarm Optimization (APSO) algorithm. Initially, preliminary experiments, coupled with CFD simulations, were conducted to analyze the airflow distribution and temperature field within the engine compartment. Based on these findings, the component arrangement was reconfigured to improve thermal performance. Subsequently, an “engine compartment cover parameters–temperature” correlation model was established, and the dimensional parameters of the engine compartment cover were optimized using the APSO algorithm. Experimental results demonstrate that the optimized configuration achieves an average surface temperature reduction of approximately 17.82% for critical components, enabling prolonged stable operation and significantly enhanced operational reliability of the harvester. Full article

Navigation for autonomous robots in hazardous environments demands robust localization solutions. In challenging environments such as tunnels and urban disaster areas, autonomous robots and vehicles are particularly important for search and rescue operations. However, especially in these environments, sensor failures and errors make the localization task particularly difficult. We propose a robust sensor fusion algorithm that integrates data from a thermal camera, a LiDAR sensor, and a GNSS to provide reliable localization, even in environments where individual sensor data may be compromised. The thermal camera and LiDAR sensor employ distinct SLAM and odometry techniques to estimate movement and positioning, while an extended Kalman filter (EKF) fuses all three sensor inputs, accommodating varying sampling rates and potential sensor outages. To evaluate the algorithm, we conduct a field test in an urban environment using a vehicle equipped with the appropriate sensor suite while simulating an outage one at a time, to demonstrate the approach’s effectiveness under real-world conditions. Full article

Radio-frequency (RF) microwave wireless power transfer (WPT) offers an efficient means of delivering energy to a wide array of devices over long distances. Previous RF WPT systems faced significant challenges, including complex hardware and control systems, software deficiencies, insufficient rectification power, lack of high-performance substrate materials, and electromagnetic radiation hazards. Addressing these issues, this paper proposes the world’s first watt-level RF WPT system capable of intelligent continuous tracking and occlusion judgment. Our 5.8 GHz band RF WPT system integrates several advanced technologies, such as millimeter-precision lidar, the multi-object image recognition algorithm, the accurate 6-bit continuous beamforming algorithm, a compact 16-channel 32 W high-power transmitting system, a pair of ultra-low axial ratio circularly polarized antenna arrays, ultra-low-loss high-strength ceramic substrates, and a 2.4 W high-power Schottky diode array rectifier achieving a rectification efficiency of $66.8\%$. Additionally, we construct a platform to demonstrate the application of the proposed RF WPT system in battery-free vehicles, achieving unprecedented ${360}^{\circ }$ uninterrupted power supply to the battery-free vehicle. In summary, this system represents the most functionally complete RF WPT system to date, serving as a milestone for several critical fields such as smart living, transportation electrification, and battery-less/free societies. Full article

Rainstorm-induced landslides are a widespread geomorphological hazard that can lead to major emergencies, causing severe damage to life and property. Due to the extent of the areas usually affected by these phenomena (up to thousands of km²) and/or their typical high areal density, in the early stages of the emergency it can be useful to reconstruct a comprehensive, albeit preliminary, overview of the landslides. With this aim, in this work we provide an outline of the landslides that occurred in the eastern part of the Emilia-Romagna region (northern Italy) after two severe rainstorms in May 2023. By combining information collected during the emergency through direct field inspections and UAV (unmanned aerial vehicle) surveys with preliminary analyses of historical rainfall/landslide data, we inferred the main characteristics of the landslides (e.g., movement type, involved materials, triggering mechanisms) and the relation with antecedent landslide phenomena, rainfall exceptionality, and anthropogenic activities. The latter were found to have likely contributed to landslides triggering by increasing water discharge and, in turn, infiltration and runoff erosion (i.e., inadequate drainage devices) and steepening slope gradients (e.g., road cuts). The vastness of the territory hit by the May 2023 landslides and their exceptional areal density can be explained not only with the extreme rainfall intensity (>500 years at several rainfall stations), but also with the widespread occurrence of slope materials which are very sensitive to sudden changes in hydraulic conditions. The high landslide susceptibility of the area is confirmed by the fact that many of the May 2023 landslides occurred at or close to previously identified and mapped landslide sites. Full article

Rockfall is considered the main geohazard in mountainous areas with steep morphology. The main objective of this study is to assess the rockfall hazard in the cultural heritage site of the Monastery of Agia Paraskevi, Monodendri, in northern Greece, where a recent rockfall event occurred, destroying a small house and the protective fence constructed to protect the Monastery of Agia Paraskevi. To evaluate the rockfall potential, engineering geological-oriented activities were carried out, such as geostructurally oriented field measurements, aiming to simulate the rockfall path and to compute the kinetic energy and the runout distance. In addition, using remote sensing tools such as Unmanned Aerial Vehicles (UAVs), we were able to inspect the entire slope face and detect the locations of detached blocks by measuring their volume. As a result, it was concluded that the average volume of the expected detached blocks is around 1.2 m³, while the maximum kinetic energy along a rockfall trajectory ranges from 1850 to 2830 kJ, depending on the starting point (source). Furthermore, we discussed the level of similarity between the outcomes arising from the data obtained by the traditional field survey and the UAV campaigns regarding the structural analysis of discontinuity sets. Full article

Field observations provide valuable information for rockfall assessments, but estimating physical and statistical quantities related to rockfall propagation directly is challenging. Simulations are commonly used to infer these quantities, but their subjectivity can result in varying hazard land use zonation extents for different projects. This paper focuses on the application of simulated trajectories for rockfall hazard assessments, with an emphasis on reducing subjectivity. A quantitative guiding rockfall hazard methodology based on earlier concepts is presented and put in the context of legislated requirements. It details how the temporal hazard component, related to the likelihood of failure, can be distributed spatially using simulated trajectories. The method can be applied with results from any process-based software and combined with various prediction methods of the temporal aspect, although this aspect is not the primary focus. Applied examples for static objects and moving objects, such as houses and vehicles, are shown to illustrate the important effect of the object size. For that purpose, the methodology was applied at an indicative level over Norway utilizing its 1 m detailed digital terrain model (DTM) acquired from airborne LiDAR. Potential rockfall sources were distributed in 3D where slopes are steeper than 50°, as most rockfall events in the national landslide database (NSDB) occurred in such areas. This threshold considerably shifts toward gentler slopes when repeating the analysis with coarser DTMs. Simulated trajectories were produced with an adapted version of the simulation model stnParabel. Comparing the number of trajectories reaching the road network to the numerous related registered rockfall events of the NSDB, an indicative averaged yearly frequency of released rock fragments of 1/25 per 10,000 m² of cliff was obtained for Norway. This average frequency can serve as a starting point for hazard assessments and should be adjusted to better match local conditions. Full article

In this paper, an enhanced deep reinforcement learning approach is presented for unmanned aerial vehicles (UAVs) operating in dynamic and potentially hazardous environments. Initially, the capability to discern obstacles from visual data is achieved through the application of the Yolov8-StrongSort technique. Concurrently, a novel data storage system for deep Q-networks (DQN), named dynamic data memory (DDM), is introduced to hasten the learning process and convergence for UAVs. Furthermore, addressing the issue of UAVs’ paths veering too close to obstacles, a novel strategy employing an artificial potential field to adjust the reward function is introduced, which effectively guides the UAVs away from proximate obstacles. Rigorous simulation tests in an AirSim-based environment confirm the effectiveness of these methods. Compared to DQN, dueling DQN, M-DQN, improved Q-learning, DDM-DQN, EPF (enhanced potential field), APF-DQN, and L1-MBRL, our algorithm achieves the highest success rate of 77.67%, while also having the lowest average number of moving steps. Additionally, we conducted obstacle avoidance experiments with UAVs with different densities of obstacles. These tests highlight fast learning convergence and real-time obstacle detection and avoidance, ensuring successful achievement of the target. Full article

The widespread adoption of electric vehicles (EVs) has elevated the importance of rigorous safety standards, particularly for fire resistance in post-crash scenarios. Existing testing protocols, such as Regulation No. 100, utilize petrol pool fires to simulate real-world fire hazards but lack comprehensive analysis regarding their repeatability and reliability. This study addresses this critical gap by evaluating the variability and consistency of fire-resistance tests performed on multiple battery energy storage systems (BESSs) under standardized conditions. A custom-built measurement system incorporating thermocouples, anemometers, and hygrometers provided high-resolution data on flame dynamics, ambient conditions, and pool fire efficiency. Statistical evaluations following ISO 5725 series guidelines revealed substantial inconsistencies, including unstable exposure temperatures and sensitivity to local turbulence. These findings call into question the robustness of current testing methods, and we propose an alternative approach employing LPG burners for improved precision and repeatability. By identifying significant flaws in existing standards and offering scientifically grounded enhancements, this work contributes a novel perspective to the field of EV safety, advancing global fire-resistance testing protocols. Full article

The eastern segment of the Sunan-Qilian Fault (ES-SQF) is located within the seismic gap between the 1927 M8.0 Gulang earthquake and the 1932 M7.6 Changma earthquake in China. It also aligns with the extension direction of the largest surface rupture zone associated with the 2022 Mw6.7 Menyuan earthquake. Understanding the activity parameters of this fault is essential for interpreting strain distribution patterns in the central–western segment of the Qilian–Haiyuan fault zone, located along the northeastern margin of the Tibetan Plateau, and for evaluating the seismic hazards in the region. High-resolution Google Earth satellite imagery and UAV (Unmanned Aerial Vehicle)-based photogrammetry provide favorable conditions for detailed mapping and the study of typical landforms along the ES-SQF. Combined with field geological surveys, the ES-SQF is identified as a continuous, singular-fault structure extending approximately 68 km in length. The fault trends in the WNW direction and along its trace, distinctive features, such as ridges, gullies, and terraces, show clear evidence of synchronous left lateral displacement. This study investigates the Qingsha River and the Dongzhong River. High-resolution digital elevation models (DEMs) derived from UAV imagery were used to conduct a detailed mapping of faulted landforms. An analysis of stripping trench profiles and radiocarbon dating of collected samples indicates that the most recent surface-rupturing seismic event in the area occurred between 3500 and 2328 y BP, pointing to the existence of an active fault from the Holocene epoch. Using the LaDiCaoz program to restore and measure displaced terraces at the study site, combined with geomorphological sample collection and testing, we estimated the fault’s slip rate since the Holocene to be approximately 2.0 ± 0.3 mm/y. Therefore, the ES-SQF plays a critical role in strain distribution across the central–western segment of the Qilian–Haiyuan fault zone. Together with the Tuolaishan fault, it accommodates and dissipates the left lateral shear deformation in this region. Based on the slip rate and the elapsed time since the last event, it is estimated that a seismic moment equivalent to Mw 7.5 has been accumulated on the ES-SQF. Additionally, with the significant Coulomb stress loading on the ES-SQF caused by the 2016 Mw 5.9 and 2022 Mw 6.7 Menyuan earthquakes, there is a potential for large earthquakes to occur in the future. Our results also indicate that high-resolution remote sensing imagery can facilitate detailed studies of active tectonics. Full article