Capabilities Of Vehicles Research Articles

ОБҐРУНТУВАННЯ ПОКАЗНИКА ЕФЕКТИВНОСТІ ПРОВЕДЕННЯ ЗАВАНТАЖУВАЛЬНО-РОЗВАНТАЖУВАЛЬНИХ РОБІТ ПІД ЧАС ЛОГІСТИЧНОГО ЗАБЕЗПЕЧЕННЯ ПІДРОЗДІЛІВ НАЦІОНАЛЬНОЇ ГВАРДІЇ УКРАЇНИ

The necessity of improving the system of organization of accounting for the use of motor vehicles in military subunits, units and joint units of the National Guard of Ukraine is shown. For successful performance of assigned tasks by units, it is necessary to use the capabilities of motor vehicles to the maximum extent through rational planning of road transportation, to systematize the accounting and evaluation of the work performed by the vehicles To assess the performance and technical condition of motor vehicles, special indicators are used, which are a numerical expression of the measurements or their ratio, namely: fleet utilization factor; working time utilization factor; mileage utilization factor; load capacity utilization factor; speed, etc. However, they do not allow a generalized assessment of the level of efficiency of the use of vehicles. Therefore, there was a need to obtain a dependence for assessing the efficiency of transportation by military vehicles. It is proposed to improve the system of accounting for the use of automobile equipment in units and military units of the National Guard of Ukraine by introducing an indicator of the efficiency of loading and unloading operations It is proposed as an indicator of the efficiency of loading and unloading operations to use an indicator that allows you to assess the level of efficiency of transportation by determining the probability of cargo delivery at a given time.

MAFNet: Segmentation of Road Potholes With Multimodal Attention Fusion Network for Autonomous Vehicles

Road potholes can cause discomforts to passengers and even traffic accidents to vehicles. Accurate segmentation of road potholes is an important capability for autonomous vehicles to ensure driving safety. Some methods on road-pothole segmentation use single-modal data (i.e., RGB images). The main challenge faced by these methods is that the visual appearance of road potholes is often close to road areas, making these networks difficult to distinguish them. Recent methods resort to fusing RGB images with depth/disparity images for pothole segmentation. However, their performance is still not satisfactory in real-world applications. To achieve superior results, this paper proposes a novel data fusion network for road-pothole segmentation, where a channel attention fusion module and a dual attention fusion module are designed to hierarchically fuse the RGB and disparity data. We evaluate our proposed network using a public dataset, and the experimental results demonstrate the superiority over the state-of-the-art networks.

РОЗВИТОК БЕЗПІЛОТНОЇ АВІАЦІЇ У СВІТІ ТА УКРАЇНІ: АНАЛІЗ ОСОБЛИВОСТЕЙ ТА ТЕХНІЧНИХ ХАРАКТЕРИСТИК

The capabilities of unmanned aerial vehicles are constantly growing, the scope of their use is expanding, combat survivability is increasing. Most of them are small, low optical and radar contrast, so they are less vulnerable to enemy air defenses. At the same time, in the armed forces of the world's leading countries, in particular the Armed Forces of Ukraine, unmanned aerial vehicles are increasingly used instead of manned aircraft to perform combat tasks of reconnaissance, battlefield surveillance, destruction of ground targets, creation of false air targets and more. In general, the assessment of modern armed conflicts shows an increase in the intensity of mass use of unmanned aerial vehicles to solve combat tasks at various levels (tactical, operational and strategic), which significantly changes the course of armed struggle in the air. Unmanned aerial vehicles have been and remain one of the main means, which determines not only the enthusiasm of the initiative, but also the result of the confrontation in general. Analysis of publications allows us to understand the use of unmanned aerial vehicles, the effectiveness of unmanned aerial vehicles in solving various problems, as well as their advantages and disadvantages, but does not reveal ways and experience of using unmanned aerial vehicles in today's military conflict. This article reveals the trends in the combat use of unmanned aerial vehicles, analyzes the use of unmanned aerial vehicles based on the experience of their combat use in combat conflict with the Russian Federation, which, in particular, showed that the struggle of air defense forces and forces in the armed conflict Aviation is ineffective due to a number of factors, in particular due to the advanced development of these air attack means in relation to existing air defense firepower. In addition, the paper identifies the importance of unmanned aerial vehicles on the peculiarities of warfare, its further nature of action and trends in the further application and features of modern martial arts with the leading role of aviation. Thus, the air operation (both for defense and offensive purposes) is an important component of the operation to repel armed aggression and aggravate the military-political conflict and full-scale armed conflict of the Russian Federation requires a more detailed analysis of combat applications of modern unmanned aerial vehicles.

Applied routing problem for a fleet of delivery drones using a modified parallel genetic algorithm

More and more experts agree that in the near future, most freight traffic will be carried out using automated systems, and of them drone delivery is considered to be the most promising. Drone delivery would benefit by independence from the limitations of transport infrastructure and road conditions and would ensure cargo delivery with rapid turnaround times, as well as a significant reduction of environmental impact. The technical capabilities of unmanned aerial vehicles improve year by year, so the task of coordinating drones and effectively planning routes is relevant and in great demand. The development of such technologies will help reduce transportation costs and improve customer service through faster delivery. This article discusses the applied routing problem for a fleet of drones with limited load capacity for the delivery of heterogeneous goods with the possibility of loading in multiple warehouses from an international optimization competition. The solution includes new approach based on a mixed dimensional parallel genetic algorithm (MDPGA) for finding rational routes for delivering goods to various customers and an assignment problem to reduce the dimension depending on the number of warehouses.

Judgements of Autonomous Vehicle Capability Determine Attribution of Blame in Road Traffic Accidents

Judgements of Autonomous Vehicle Capability Determine Attribution of Blame in Road Traffic Accidents

Judgements of Autonomous Vehicle Capability Determine Attribution of Blame in Road Traffic Accidents

Judgements of Autonomous Vehicle Capability Determine Attribution of Blame in Road Traffic Accidents

Identification of Operational Design Domain for Autonomous Truck Mounted Attenuator System on Multilane Highways

The Autonomous Truck Mounted Attenuator (ATMA) vehicle system is a technology that leverages connected and automated vehicle (CAV) capabilities for maintenance of transportation infrastructure. Promoted by FHWA and state departments of transportation (DOTs), it is a niche CAV application in leader–follower style, intended to remove DOT workers from the following maintenance truck, to reduce fatalities in work zones. Because practicable guidance for deployment of this technology is largely missing in MUTCD, state DOTs have been making their own deployment criteria. In this manuscript, we focus on the operational design domain (ODD) problem—under what traffic conditions should ATMA be deployed. Modeling efforts are first focused on the derivation of an effective discharge rate that can be associated with a moving bottleneck caused by slow-moving ATMA vehicles on a multilane highway. Then, based on the demand input and discharge rates, microscopic traffic flow models calculate vehicle delay and density, which the Highway Capacity Manual (HCM) suggests are key indicators of a multilane highway’s level of service (LOS). In this way, the linkage between AADT and LOS is analytically established. NGSIM data is used for the model validation and shows that the developed model correctly captures the effective discharge rate discount caused by moving bottlenecks. The modeling results demonstrate that roadway performance is sensitive to the K factor and D factor, as well as the operating speed of ATMA and, if LOS = C is a desirable design objective, a good AADT threshold to use would be around 40,000 vehicles per day.

Thermodynamic Study of a Cooled Micro Gas Turbine for a Range Extended Electric Vehicle

Carbon dioxide released by the transportation sector has a significant impact on the environment. Nowadays, the research efforts are thoroughly investing in the capability of series hybrid electric vehicles in reducing CO2 and NOX emissions. Many regulations are encouraging automakers to substitute internal combustion engines with electric vehicles. Although the latter are zero-emissions energy sources, they are expensive and have a limited autonomy range. Therefore, recent studies are interested in gas turbines (GTs) in series hybrid electric vehicles (SHEV). A gas turbine (GT) coupled to an alternator is considered as an auxiliary power unit capable of charging the battery of the electric vehicle once depleted. The microturbine is generally composed of a centrifugal compressor and a radial turbine mounted on the same shaft with a recuperator and air bearings. Based on previous researches and thermodynamic analysis, the most suitable gas turbine cycle with regard to the efficiency and the net specific work is composed of two compression stages with an intercooler, a regenerator, and two expansion stages with a reheater. It can be deduced that by increasing the turbine inlet temperature, the system efficiency increases. However, the inlet temperature is limited by turbine materials constraints and specifications. The present paper focuses on developing a more efficient GT cycle by reaching higher inlet temperatures by cooling the turbine blade. It consists of using compressed gas from the compressor and introduce them into the turbine blades. This technology takes into consideration the influence of the extracted mass of compressed air, the effectiveness of the coolant, and the turbine blade temperature on thermal efficiency. An increase of the cycle efficiency of 4.78 points is obtained for a turbine inlet temperature of 1,450 °C.

Fuzzy Classification Based Driving Distance Estimation for Electric Vehicles

Electric vehicle technology is an essential research field for improving full-electric vehicle (FEVs) capabilities. Different subsystem parameters in the FEVs should be monitored on a regular basis. The better these subsystems are used, the better the FEVs' performance, life, and range become. Nowadays, estimation of the state of charge (SoC) of the batteries and the driving distance is the area not been standardized sufficiently. In this study, a novel fuzzy classification method (FCM) is proposed to make the exact driving distance estimation of FEVs. The proposed FCM considers the consumed power and parameters of the battery under dynamic conditions. A test location was selected for the proposed FCM and tested under 3 different test conditions, namely, no-load, half-load and full-load conditions. Also, the performance of FCM is studied under several slope conditions, and the result shows that if the battery voltage decreases then the power consumed by the vehicle is improved in the uphill travel and the battery voltage is normal and the power consumption of the vehicle is decreased in the downhill drive. Finally, the drive distance of the proposed FCM is determined. HIGHLIGHTS Fuzzy classification based driving distance estimation for full-electric vehicle is proposed Parameters of battery and power consumption has been considered under dynamic condition CAN communication is established between different subsystems of electric vehicle Three test conditions (no-load, half load, and full load) have been considered

Synthetic cells in biomedical applications.

Synthetic cells are engineered vesicles that can mimic one or more salient features of life. These features include directed localization, sense-and-respond behavior, gene expression, metabolism, and high stability. In nanomedicine, many of these features are desirable capabilities of drug delivery vehicles but are difficult to engineer. In this focus article, we discuss where synthetic cells offer unique advantages over nanoparticle and living cell therapies. We review progress in the engineering of the above life-like behaviors and how they are deployed in nanomedicine. Finally, we assess key challenges synthetic cells face before being deployed as drugs and suggest ways to overcome these challenges. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Biology-Inspired Nanomaterials > Lipid-Based Structures.

A Peer Review of Hybrid Electric Vehicle Based on Step-Up Multi-input Dc-Dc Converter and renewable energy source

Due to the rapid increase of environmental pollution caused by automobiles. To decrease pollution and to save our resources, there is an alternator to use an electric vehicle instead of a gasoline engine. The main drawback of a gasoline engine of compared to the electric vehicle can polluter noise efficiency durability. When it comes to durability, efficiency, and acceleration capabilities of electric vehicles, they are more impressive. The electric vehicles involve HEVs and BEVs. Generally, ultra-capacitor, solar Photovoltaic (PV) system, batters, regenerative braking systems and flywheel are utilized in HEVs as energy storage devices. All energy storage devices are linked to this distinct dc-dc converter scheme for raising input sources’ voltage. In past few decades, most HEVs have incorporated multi-input converters in order to enhance their reliability and efficiency. There are several distinct multi-input dc-dc converter schemas utilized in HEVs. This research discusses their current and future trends as well as energy storage devices.

Decision support for wildfire asset protection: A two-stage stochastic programming approach

During uncontrollable wildfires, decision-makers dispatch vehicles for tasks aimed at reducing the hazard to key assets. The decision-making process is complicated by the need for vehicle capabilities to match asset requirements within time windows determined by the progression of the fire. This is often further complicated by a wind change that is expected but with uncertainty in the timing. This paper aims to provide a decision support approach for determining plans for the deployment of resources under various scenarios. To this end, we solve the Asset Protection Problem (APP) in the context of Australia’s Black Saturday bushfires by developing a two-stage stochastic model. In this problem, we consider uncertainties in the timing of a change in wind velocity in defining various scenarios. We present a dynamic rerouting model and an Adaptive Large Neighbourhood Search (ALNS) algorithm to solve the model in a time efficient manner for decision-makers. A new set of instances were generated using realistic parameters. Thereafter, we evaluate the performance of the proposed approaches through extensive computational experiments. We solve both the two-stage stochastic program and the more limited dynamic rerouting model exactly for small instances. We observe that the restrictions of the dynamic rerouting model yields solutions within a few percent of the two-stage stochastic program. Moreover, the ALNS solution is a good approximation to its exact equivalent but with faster solution times. After further tests, it became apparent that with larger asset numbers the ALNS is the more practical method for operational purposes.

Towards Secure, Decentralised, and Privacy Friendly Forensic Analysis of Vehicular Data.

The automotive industry has been transformed through technological progress during the past decade. Vehicles are equipped with multiple computing devices that offer safety, driving assistance, or multimedia services. Despite these advancements, when an incident occurs, such as a car crash, the involved parties often do not take advantage of the technological capabilities of modern vehicles and attempt to assign liability for the incident to a specific vehicle based upon witness statements. In this paper, we propose a secure, decentralized, blockchain-based platform that can be employed to store encrypted position and velocity values for vehicles in a smart city environment. Such data can be decrypted when the need arises, either through the vehicle driver’s consent or through the consensus of different authorities. The proposed platform also offers an automated way to resolve disputes between involved parties. A simulation has been conducted upon a mobility traffic dataset for a typical day in the city of Cologne to assess the applicability of the proposed methodology to real-world scenarios and the infrastructure requirements that such an application would have.

Bio-inspired, intelligent flexible sensing skin for multifunctional flying perception

Developing multifunctional flying perceptibility, by mimicking the biological system, is meaningful for aircrafts to adapt to the complicated and ambiguous flow environment. In this work, we show that the comprehensive capabilities (skin, synapse, immune system, and brain) of flying creatures can serve as bio-inspirations to develop an intelligent flexible sensing (iFlexSense) skin with similar components, i.e., skin-like mechanosensing, neuron-like data transmission, immune system-like impact monitoring and brain-like artificial intelligence. The iFlexSence skin brings substantial improvements to the aircrafts by the multifunctionalities including airflow perception, state awareness and self-diagnosis of complex surfaces, validated by the wind tunnel tests mounted on a NACA 0012 airfoil, in which it accurately measures the surface pressure, temperature, wall shear stress and flutter, locates the sudden impacts and correctly predicts the occurrence of separation and stall. It shows great potential for future wind tunnel tests and to extend the capabilities of unmanned air vehicles and underwater vehicles.

Multimodal Earth observation data fusion: Graph-based approach in shared latent space

Multiple and heterogenous Earth observation (EO) platforms are broadly used for a wide array of applications, and the integration of these diverse modalities facilitates better extraction of information than using them individually. The detection capability of the multispectral unmanned aerial vehicle (UAV) and satellite imagery can be significantly improved by fusing with ground hyperspectral data. However, variability in spatial and spectral resolution can affect the efficiency of such dataset's fusion. In this study, to address the modality bias, the input data was projected to a shared latent space using cross-modal generative approaches or guided unsupervised transformation. The proposed adversarial networks and variational encoder-based strategies used bi-directional transformations to model the cross-domain correlation without using cross-domain correspondence. It may be noted that an interpolation-based convolution was adopted instead of the normal convolution for learning the features of the point spectral data (ground spectra). The proposed generative adversarial network-based approach employed dynamic time wrapping based layers along with a cyclic consistency constraint to use the minimal number of unlabeled samples, having cross-domain correlation, to compute a cross-modal generative latent space. The proposed variational encoder-based transformation also addressed the cross-modal resolution differences and limited availability of cross-domain samples by using a mixture of expert-based strategy, cross-domain constraints, and adversarial learning. In addition, the latent space was modelled to be composed of modality independent and modality dependent spaces, thereby further reducing the requirement of training samples and addressing the cross-modality biases. An unsupervised covariance guided transformation was also proposed to transform the labelled samples without using cross-domain correlation prior. The proposed latent space transformation approaches resolved the requirement of cross-domain samples which has been a critical issue with the fusion of multi-modal Earth observation data. This study also proposed a latent graph generation and graph convolutional approach to predict the labels resolving the domain discrepancy and cross-modality biases. Based on the experiments over different standard benchmark airborne datasets and real-world UAV datasets, the developed approaches outperformed the prominent hyperspectral panchromatic sharpening, image fusion, and domain adaptation approaches. By using specific constraints and regularizations, the network developed was less sensitive to network parameters, unlike in similar implementations. The proposed approach illustrated improved generalizability in comparison with the prominent existing approaches. In addition to the fusion-based classification of the multispectral and hyperspectral datasets, the proposed approach was extended to the classification of hyperspectral airborne datasets where the latent graph generation and convolution were employed to resolve the domain bias with a small number of training samples. Overall, the developed transformations and architectures will be useful for the semantic interpretation and analysis of multimodal data and are applicable to signal processing, manifold learning, video analysis, data mining, and time series analysis, to name a few.

Supervision of a self-driving vehicle unmasks latent sleepiness relative to manually controlled driving

Human error has been implicated as a causal factor in a large proportion of road accidents. Automated driving systems purport to mitigate this risk, but self-driving systems that allow a driver to entirely disengage from the driving task also require the driver to monitor the environment and take control when necessary. Given that sleep loss impairs monitoring performance and there is a high prevalence of sleep deficiency in modern society, we hypothesized that supervising a self-driving vehicle would unmask latent sleepiness compared to manually controlled driving among individuals following their typical sleep schedules. We found that participants felt sleepier, had more involuntary transitions to sleep, had slower reaction times and more attentional failures, and showed substantial modifications in brain synchronization during and following an autonomous drive compared to a manually controlled drive. Our findings suggest that the introduction of partial self-driving capabilities in vehicles has the potential to paradoxically increase accident risk.

Low-Speed Vehicle Path-Tracking Algorithm Based on Model Predictive Control Using QPKWIK Solver

Abstract Automated valet parking is a part of autonomous vehicles. Path tracking is a vital capability of autonomous vehicles. In the scenario of automatic valet parking, the existing control algorithm will produce a high tracking error and a high computational burden. This paper proposes a path-tracking algorithm based on model predictive control to adapt to low-speed driving. By using the model predictive control method and vehicle kinematics model, a path tracking controller is designed. Combining the dual algorithm to further optimize the solver, a new quadratic programming (QP) knows what it knows (QPKWIK) solver is proposed. The simulation results show that the solution time of the QPKWIK solver is 25% less than that of the QP solver, and the tracking error is reduced by up to 41% compared with the QP solver. In the parking lot, the tracking performance is tested under four common scenarios, and the experimental results show that this controller has better tracking performance.

Automated system of freight traffic optimisation in the interaction of various modes of transport

Advances in information technology have contributed to increased transport efficiency. The use of the latest information technologies allows automating all the information technology activities of transport enterprises that take part in the processes of organising freight traffic. Automation of transport logistics provides increased efficiency and optimisation of transportation. Due to the introduction of automated routing, accounting and planning systems at transport enterprises, transport logistics is reaching a new level. The purpose of the study is to develop an automated control system for the operation of the production and transport complex, based on the use of the latest information technology – Embarcadero RAD Studio XE10 visual programming system. It will help to optimise the transportation of mineral construction materials using the road, railway, and water modes of transport, the change of which occurs at the goods transshipment points. To solve this problem, a mathematical economic model and an automated control system for the operation of the production and transport complex have been developed. The proposed method would take into account the technical and technological capabilities of land and water modes of transport and the capacity of transshipment points, at which the cost of transporting goods will be minimal. Using the software package, the base for the transportation of mineral construction materials was calculated. The obtained initial transportation plan for each type of delivery was optimised, taking into account the production capacities of manufacturers and the needs of customers. The results of the optimal distribution of goods between different modes of transport are given (taking into account their production capacities), their change takes place in transshipment points. The results obtained allow optimal use of the technical and technological capabilities of vehicles and handling mechanisms. Based on the mathematical economic model, an automated control system for the operation of the production and transport complex has been developed, it has the form of a software package. The calculation of the initial (reference) freight traffic plan was carried out and the optimisation of the initial plan for these transportations was performed for each of the delivery options. The operability of the developed software package has been confirmed experimentally, which gives reason to offer it for the use in industrial production associated with the movement of sizable freight traffic.

An Auction Bidding Approach to Balance Performance Bonuses in Vehicle Routing Problems with Time Windows

In the field of operations research, the vehicle routing problem with time windows (VRPTW) has been widely studied because it is extensively used in practical applications. Real-life situations discussed in the relevant research include time windows and vehicle capabilities. Among the constraints in a VRPTW, the practical consideration of the fairness of drivers’ performance bonuses has seldom been discussed in the literature. However, the shortest routes and balanced performance bonuses for all sales drivers are usually in conflict. To balance the bonuses awarded to all drivers, an auction bidding approach was developed to address this practical consideration. The fairness of performance bonuses was considered in the proposed mathematical model. The nearest urgent candidate heuristic used in the auction bidding approach determined the auction price of the sales drivers. The proposed algorithm both achieved a performance bonus balance and planned the shortest route for each driver. To evaluate the performance of the auction bidding approach, several test instances were generated based on VRPTW benchmark data instances. This study also involved sensitivity and scenario analyses to assess the effect of the algorithm’s parameters on the solutions. The results show that the proposed approach efficiently obtained the optimal routes and satisfied the practical concerns in the VRPTW.

Reimagining Autonomous Underwater Vehicle Charging Stations with Wave Energy

The vast capabilities of autonomous underwater vehicles (AUVs)—such as in assisting scientific research, conducting military tasks, and repairing oil pipelines—are limited by high operating costs and the relative inaccessibility of power in the open ocean. Wave powered AUV charging stations may address these issues. With projected increases in usage of AUVs globally in the next five years, AUV charging stations can enable less expensive and longer AUV missions. This paper summarizes the design process and investigates the feasibility of a wave powered, mobile AUV charging station, including the choice of a wave energy converter and AUV docking station as well as the ability to integrate the charging station with an autonomous surface vehicle. The charging station proposed in this paper meets many different commercial, scientific, and defense needs, including continuous power availability, data transmission capabilities, and mobility. It will be positioned as a hub for AUV operations, enabling missions to run autonomously with no support ship. The potential market for this design is very promising, with an estimated $1.64 million market size just for AUV technologies by 2025.