Search Results (78)

Compression–ignition engines emit particulate matter (PM) (soot), prompting the widespread use of diesel particulate filters (DPFs) in the automotive sector. An alternative method for PM reduction involves the use of ultrasonic waves to disperse and modify the structure of exhaust particles. This article presents experimental results of the effects of ultrasonic emitter parameters, including the number, arrangement, and power, along with the engine speed, on the exhaust smoke density. Tests were conducted on a laboratory prototype equipped with six ultrasonic emitters spaced 0.17 m apart. The exhaust source was a diesel engine from a construction excavator, based on the MTZ-80 tractor design, delivering 80 HP and a displacement of 4750 cm³. A regression model was developed to describe the relationship between the engine speed, emitter power and spacing, and smoke density. The optimal configuration was found to involve an emitter power of 319.35 W and a spacing of 1.361 m for a given engine speed. Under the most effective conditions—an engine speed of 1500 rpm, six active emitters, and a total power of 600 W—smoke emissions were reduced by 18%. These findings support the feasibility of using ultrasonic methods as complementary or alternative exhaust gas filtration techniques for non-road diesel engines. Full article

This study addresses the ’last-mile’ transportation challenges that arise in steep and narrow forest terrain by proposing a novel robotic palanquin system called roCaGo. It is inspired by the mechanical principles of two-wheel-steering and two-wheel-drive (2WS/2WD) bicycles. The roCaGo system integrates front- and rear-wheel-drive mechanisms, as well as a central suspension structure for carrying loads. Unlike conventional forestry machinery, which requires wide, well-maintained roads or permanent rail systems, the roCaGo system enables flexible, operator-assisted transport along narrow, unprepared mountain paths. A dynamic model of the system was developed to design a stabilization control strategy, enabling roCaGo to maintain transport stability and assist the operator during navigation. Numerical simulations and preliminary physical experiments demonstrate its effectiveness in challenging forest environments. Furthermore, the applicability of roCaGo has been extended to include use as a mobile third-person viewpoint platform to support the remote operation of existing forestry equipment; specifically the LV800crawler vehicle equipped with a front-mounted mulcher. Field tests involving LiDAR sensors mounted on roCaGo were conducted to verify its ability to capture the environmental data necessary for non-line-of-sight teleoperation. The results show that roCaGo is a promising solution for improving labor efficiency and ensuring operator safety in forest logistics and remote-controlled forestry operations. Full article

Forest fires have become one of the most destructive natural disasters worldwide, causing catastrophic losses, sometimes with the loss of lives. Therefore, some countries have created legislation to enforce mandatory fuel management within buffer zones in the vicinity of buildings and roads. The purpose of this study is to investigate whether inexpensive off-the-shelf drones equipped with standard RGB cameras could be used to detect the excess of trees and vegetation within those buffer zones. The methodology used in this study was the development and evaluation of a complete system, which uses AI to detect the contours of buildings and the services provided by the CHAMELEON bundles to detect trees and vegetation within buffer zones. The developed AI model is effective at detecting the building contours, with a mAP50 of 0.888. The article analyses the results obtained from two use cases: a road surrounded by dense forest and an isolated building with dense vegetation nearby. The main conclusion of this study is that off-the-shelf drones equipped with standard RGB cameras can be effective at detecting non-compliant vegetation and trees within buffer zones. This can be used to manage biomass within buffer zones, thus helping to reduce the risk of wildfire propagation in wildland–urban interfaces. Full article

This paper presents a new approach to optimizing route selection in urban road networks with sparsely placed traffic counters. By leveraging graph theory and Dijkstra’s algorithm, we propose a new method to determine the shortest path between origins and destinations in city traffic networks with sparsely placed counters. The method is based on the similarities between traffic flows recorded at the counter and the streets that generate traffic for a given counter. The advantage of this method is the use of a secondary counter function to obtain data that are built into the shortest path determination model and the free choice of the time of day for which the path is searched. The proposed method is implemented using the programming language AutoLISP 2022 and program AutoCAD 2022, providing a valuable tool for transportation engineers and urban planners. This paper presents a model of the shortest path that integrates one-way streets, the average speed of the car, as well as the delay time at traffic-lighted and non-traffic intersections. The model was applied to the traffic network of the city of Sarajevo (Bosnia and Herzegovina), but there are no restrictions for application to any network equipped with traffic counters. The obtained results show a high agreement with the Google Maps service as a reference system. Full article

Timber heritage buildings reflect the character and specifics of the region in which they are located and in which they were built. They form part of memory and history, preserving the traditions and culture of a community. The fact that their building material is timber makes them more susceptible to fire. The purpose of the article is to evaluate the current state of fire protection of timber heritage buildings. Having established this status, we will analyze the results and list the main problems we have identified. We will propose measures to reduce the risk of fire occurrence and spread. For the purposes of our research, we followed the developed methodologies for fire protection assessment of heritage buildings. We developed a checklist which we used for data collection. We analyzed the results, and then used synthesis to look for areas of correlation between the different buildings. The most common shortcomings in the fire protection of sacral timber buildings are the absence of fire protection coatings, missing or non-functioning electric fire alarms, and the absence of a stable fire extinguishing system. The presence of combustible materials in the building or its immediate vicinity, water sources, access roads or the travel time of the fire brigade to the building were also problematic. The main challenge to increasing fire protection of heritage timber buildings in Slovakia is the lack of funding. Without funds, it will not be possible to equip the buildings with fire-fighting equipment and the sustainability of these objects for future generations will not be possible. Full article

The increasing volume of traffic has led to severe challenges, including traffic congestion, heightened energy consumption, increased air pollution, and prolonged travel times. Addressing these issues requires innovative approaches for optimizing road network utilization. While Deep Reinforcement Learning (DRL)-based methods have shown remarkable effectiveness in dynamic scenarios like traffic management, their primary focus has been on single-agent setups, limiting their applicability to real-world multi-agent systems. Managing agents and fostering collaboration in a multi-agent reinforcement learning scenario remains a challenging task. This paper introduces a cooperative multi-agent federated reinforcement learning algorithm named FLDQN to address the challenge of agent cooperation by solving travel time minimization challenges in dynamic multi-agent reinforcement learning (MARL) scenarios. FLDQN leverages federated learning to facilitate collaboration and knowledge sharing among intelligent agents, optimizing vehicle routing and reducing congestion in dynamic traffic environments. Using the SUMO simulator, multiple agents equipped with deep Q-learning models interact with their local environments, share model updates via a federated server, and collectively enhance their policies using unique local observations while benefiting from the collective experiences of other agents. Experimental evaluations demonstrate that FLDQN achieves a significant average reduction of over 34.6% in travel time compared to non-cooperative methods while simultaneously lowering the computational overhead through distributed learning. FLDQN underscores the vital impact of agent cooperation and provides an innovative solution for enabling agent cooperation in a multi-agent environment. Full article

In response to the rising frequency of traffic accidents and growing concerns regarding driving safety, the identification and analysis of dangerous driving behaviors have emerged as critical components in enhancing road safety. In this paper, the research progress in the recognition methods of dangerous driving behavior based on deep learning is analyzed. Firstly, the data collection methods are categorized into four types, evaluating their respective advantages, disadvantages, and applicability. While questionnaire surveys provide limited information, they are straightforward to conduct. The vehicle operation data acquisition method, being a non-contact detection, does not interfere with the driver’s activities but is susceptible to environmental factors and individual driving habits, potentially leading to inaccuracies. The recognition method based on dangerous driving behavior can be monitored in real time, though its effectiveness is constrained by lighting conditions. The precision of physiological detection depends on the quality of the equipment. Then, the collected big data are utilized to extract the features related to dangerous driving behavior. The paper mainly classifies the deep learning models employed for dangerous driving behavior recognition into three categories: Deep Belief Network (DBN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). DBN exhibits high flexibility but suffers from relatively slow processing speeds. CNN demonstrates excellent performance in image recognition, yet it may lead to information loss. RNN possesses the capability to process sequential data effectively; however, training these networks is challenging. Finally, this paper concludes with a comprehensive analysis of the application of deep learning-based dangerous driving behavior recognition methods, along with an in-depth exploration of their future development trends. As computer technology continues to advance, deep learning is progressively replacing fuzzy logic and traditional machine learning approaches as the primary tool for identifying dangerous driving behaviors. Full article

Roadway mortality increased during COVID-19, reversing a multi-decade downward trend. The Fatality Analysis Reporting System (FARS) was used to examine contributing factors pre-COVID-19 and in the COVID-19 era using the five pillars of the Safe System framework: (1) road users; (2) vehicles; (3) roadways; (4) speed; and (5) post-crash care. Two study time periods were matched to control for seasonality differences pre-COVID-19 (n = 1725, 1 April 2018–31 December 2019) and in the COVID-19 era (n = 2010, 1 April 2020–31 December 2021) with a three-month buffer period between the two time frames excluded. Four of the five pillars of the safe system had road safety indicators that worsened during the pandemic. Mortality was 19.7% higher for motor vehicle occupants and 45.1% higher for riders of motorized two-wheeled vehicles. In adjusted analyses, failure to use safety equipment (safety belts/helmets) was associated with 44% higher mortality. Two road user groups, non-motorized bicyclists and pedestrians, did not contribute significantly to higher mortality. Urban roadway crashes were higher compared to rural crashes. Additional scientific inquiry into factors associated with COVID-19-era mortality using the Safe System framework yielded important scientific insights to inform prevention efforts. Motorized two-wheeled vehicles contribute disproportionately to pandemic-era higher mortality and constitute an emerging road safety issue that deserves further attention. Full article

Recent technological advancements and evolving regulatory frameworks are catalysing the integration of renewable energy sources in construction equipment, with the objective of significantly reducing greenhouse gas emissions. The electrification of non-road mobile machinery (NRMM), particularly self-propelled Rough-Terrain Variable Reach Trucks (RTVRT) equipped with telescopic booms, presents notable stability challenges. The transition from diesel to electric propulsion systems alters, among other factors, the centre of gravity and the inertial matrix, necessitating precise load capacity determinations through detailed load charts to ensure operational safety. This paper introduces a virtual model constructed through multiphysics modelling utilising the bond graph methodology, incorporating both scalar and vector bonds to facilitate detailed interconnections between mechanical and hydraulic domains. The model encompasses critical components, including the chassis, rear axle, telescopic boom, attachment fork, and wheels, each requiring a comprehensive three-dimensional treatment to accurately resolve spatial dynamics. An illustrative case study, supported by empirical data, demonstrates the model’s capabilities, particularly in calculating ground wheel reaction forces and analysing the hydraulic self-levelling behaviour of the attachment fork. Notably, discrepancies within a 10% range are deemed acceptable, reflecting the inherent variability of field operating conditions. Experimental analyses validate the BG-3D simulation model of the telehandler implemented in 20-SIM establishing it as an effective tool for estimating stability limits with satisfactory precision and for predicting dynamic behaviour across diverse operating conditions. Additionally, the paper discusses prospective enhancements to the model, such as the integration of the virtual vehicle model with a variable inclination platform in future research phases, aimed at evaluating both longitudinal and lateral stability in accordance with ISO 22915 standards, promoting operator safety. Full article

Collecting pavement texture information is crucial to understand the characteristics of a road surface and to have essential data to support road maintenance. Traditional texture assessment techniques often require expensive equipment and complex operations. To ensure cost sustainability and reduce traffic closure times, this study proposes a rapid, cost-effective, and non-invasive surface texture assessment technique. This technology consists of capturing a set of images of a road surface with a mobile phone; then, the images are used to reconstruct the 3D surface with photogrammetric processing and derive the roughness parameters to assess the pavement texture. The results indicate that pavement images taken by a mobile phone can reconstruct the 3D surface and extract texture features with accuracy, meeting the requirements of a time-effective documentation. To validate the effectiveness of this technique, the surface structure of the pavement was analyzed in situ using a 3D structured light projection scanner and rigorous photogrammetry with a high-end reflex camera. The results demonstrated that increasing the point cloud density can enhance the detail level of the real surface 3D representation, but it leads to variations in road surface roughness parameters. Therefore, appropriate density should be chosen when performing three-dimensional reconstruction using mobile phone images. Mobile phone photogrammetry technology performs well in detecting shallow road surface textures but has certain limitations in capturing deeper textures. The texture parameters and the Abbott curve obtained using all three methods are comparable and fall within the same range of acceptability. This finding demonstrates the feasibility of using a mobile phone for pavement texture assessments with appropriate settings. Full article

Fishery plays an important role in world trade; however, the embodied energy associated with fishery remains incompletely quantified. In this study, we applied the multi-regional input-output (MRIO) model and logarithmic mean Divisia index (LMDI) approach to understand the evolution and drivers of embodied energy in the intermediate and final fishery trade between China and countries along the 21st century Maritime Silk Road (MSR) from 2006 to 2021. The findings are as follows: (1) Embodied energy in the intermediate fishery trade averaged 92.2% of embodied energy from the total fishery trade. China has gradually shifted from being a net exporter to a net importer of embodied energy in intermediate, final, and total fishery trade with countries along the MSR. (2) From a regional perspective, the embodied energy in China’s fishery trade with Japan, South Korea, and Southeast Asia comprises the majority of the embodied energy from China’s total fishery trade (82.0% on average annually). From a sectoral perspective, petroleum, chemical and non-metallic mineral products, and transport equipment were prominent in the embodied energy of China’s intermediate fishery trade (64.0% on average annually). (3) Economic output increases were the main contributors to the increasing embodied energy in all types of fishery trade in China. The improvement in energy efficiency effectively reduced the embodied energy in all types of fishery trade in China, but its negative driving force weakened in recent years owing to minor energy efficiency improvements. Understanding the embodied energy transactions behind the intermediate and final fishery trade with countries along the MSR can provide a theoretical reference for China to optimize its fishery trade strategy and save energy. Full article

Proper road network maintenance is essential for ensuring safety, reducing transportation costs, and improving fuel efficiency. Traditional pavement condition assessments rely on specialized equipment, limiting the frequency and scope of inspections due to technical and financial constraints. In response, crowdsourcing data from connected and autonomous vehicles (CAVs) offers an innovative alternative. CAVs, equipped with sensors and accelerometers by Original Equipment Manufacturers (OEMs), continuously gather real-time data on road conditions. This study evaluates the feasibility of using CAV data to assess pavement condition through the International Roughness Index (IRI). By comparing CAV-derived data with traditional pavement auscultation results, various thresholds were established to quantitatively and qualitatively define pavement conditions. The results indicate a moderate positive correlation between the two datasets, particularly in segments with good-to-satisfactory surface conditions (IRI 1 to 2.5 dm/km). Although the IRI values from CAVs tended to be slightly lower than those from auscultation surveys, this difference can be attributed to driving behavior. Nonetheless, our analysis shows that CAV data can be used to reliably identify pavement conditions, offering a scalable, non-destructive, and continuous monitoring solution. This approach could enhance the efficiency and effectiveness of traditional road inspection campaigns. Full article

This article presents the authors’ considerations regarding the possibilities of developing fuel equipment for modern compression ignition engines used in special and non-road vehicles. The paper discusses the process of fuel combustion and atomization in the chamber of a piston combustion engine. The paper then presents the concept of modifying the atomizer of a modern fuel injector for operation using hydrogen-containing fuels of plant origin. The authors present a review of tests performed using an engine dynamometer on a modern engine with a Common Rail system running on biofuel. The CI engine operated with standard and modified fuel injectors. During the tests, the external ecological characteristics of the engine were analyzed as a function of rotational speed; the values of injection doses at individual rotational speeds and their effects on the characteristics were read from the current parameters, and the pressure and temperature in the engine’s combustion chamber were measured. The research results show that implementing the changes proposed by the authors of this work is a good direction for the development of compression ignition engines. Full article

With the transformation and development of the automotive industry, low-cost and seamless indoor and outdoor positioning has become a research hotspot for modern vehicles equipped with in-vehicle infotainment systems, Internet of Vehicles, or other intelligent systems (such as Telematics Box, Autopilot, etc.). This paper analyzes modern vehicles in different configurations and proposes a low-cost, versatile indoor non-visual semantic mapping and localization solution based on low-cost sensors. Firstly, the sliding window-based semantic landmark detection method is designed to identify non-visual semantic landmarks (e.g., entrance/exit, ramp entrance/exit, road node). Then, we construct an indoor non-visual semantic map that includes the vehicle trajectory waypoints, non-visual semantic landmarks, and Wi-Fi fingerprints of RSS features. Furthermore, to estimate the position of modern vehicles in the constructed semantic maps, we proposed a graph-optimized localization method based on landmark matching that exploits the correlation between non-visual semantic landmarks. Finally, field experiments are conducted in two shopping mall scenes with different underground parking layouts to verify the proposed non-visual semantic mapping and localization method. The results show that the proposed method achieves a high accuracy of 98.1% in non-visual semantic landmark detection and a low localization error of 1.31 m. Full article

Non-exhaust road transport emissions in cities contribute to poor air quality and have an impact on human health. This paper presents a new study of particles emitted by tyre wear in real driving conditions and gives their emission factors. The most frequently emitted particles were collected in urban, suburban and road areas. They were identified and analysed physically and chemically. Their level of toxicity is well known. An overall analysis of the measured pollutants was carried out to assess their emission factors in real driving situations. The highest emitting pollutants, considered separately, were found to have high emission factors. The values obtained exceed the Euro standard for vehicles but are below those of vehicles not equipped with particle filters. Significant test analysis confirmed that the inertia of chemical pollutants is homogeneous. Emission factors have also been provided for PM₁₀ and PM_2.5. These results should contribute to the emergence of future regulations of non-exhaust emissions and should help to analyse the exposure-impact relationship for particles from tyre wear. Full article