The key to ensuring the sustainability of transportation operations on the roads is to manage traffic flows homogeneously. By homogenizing the behavior of various drivers, traffic operation can be optimized, and traffic safety can be improved. However, the advent of autonomous vehicles is expected to have a major impact on the homogeneity of sustainable transportation operations. In this study, a method of driving in harmony with surrounding vehicles was studied to minimize the impact of autonomous vehicles on current traffic operation. In particular, in this study, a methodology was developed to optimize the driving behavior of autonomous vehicles by analyzing the driving behavior of following vehicles using spectrum analysis. Specifically, a method for calculating three indicators that can analyze the driving behavior of a following vehicle, such as reaction time, stimulus adaptation index, and collision risk avoidance index, was proposed. These indices produced consistent and robust results for all traffic conditions. If these indicators are used, it is expected that sustainable traffic management will be possible even when autonomous vehicles and human drivers are mixed on the road.

The Internet of Vehicles (IoV) is an emerging field that has attracted significant attention due to its potential to transform transportation and mobility. IoV enables the development of new services and applications that can improve transportation systems' efficiency, safety, and sustainability. However, ensuring secure and trustworthy communication among the different components of an IoV system is a critical challenge that needs to be addressed. This paper proposes a blockchain-enabled communication framework for secure and trustworthy IoV applications. The proposed framework leverages the security and decentralization features of blockchain technology to provide secure communication channels for different components of the IoV system, including vehicles, infrastructure, and service providers. The proposed framework provides an identity management system to authenticate and authorize users and devices, thereby preventing unauthorized access and data breaches. To evaluate the effectiveness of the proposed framework, we conducted experiments using a real-world IoV scenario, and the results demonstrate that the proposed framework can provide secure and trustworthy communication for IoV applications. The proposed blockchain-enabled communication framework offers a promising approach to address the security and trust challenges in IoV communication systems.

This paper provides a comprehensive overview of recent advancements in autonomous electric vehicle (AEV) within the specified region. It elaborates on the progress and comparative analysis of diverse subsystems, including energy storage, cell balancing for battery systems, vehicle charger layouts, electric vehicle motor mechanisms, and braking systems. Furthermore, this paper showcases several prototype autonomous electric vehicles as conclusive study findings.

Recent empirical work has revealed that drivers have different lane change strategies. These strategies help to understand driver heterogeneity and decision stochasticity, which are both often lacking in microscopic models. In this paper we generalize the lane change strategies in a theory for social interactions. The theory introduces a mechanism by which two new driver traits, ego-speed sensitivity and socio-speed sensitivity, endogenously influence other driver traits that govern lane change behaviour such as desired speed, desired headway and lane change desire. A key concept in this mechanism is that drivers are not just affected by what happens in front of them, but also by what they observe in their rear-view mirrors. The idea is that drivers reduce their headways to exert (social) pressure onto leading drivers (on the same or adjacent lanes), which may force them to increase speed, change lanes, or conversely, to refrain from changing lanes. We refer to this reduction of the headway as tailgating. Through an example implementation of the theory, we demonstrate that these social interactions may have a profound impact on several key (mesoscopic) traffic flow characteristics. Our results show that these new driver interactions influence the number of lane changes, platoon lengths, and headway variance. As such, the theory improves our description and understanding of traffic flow and consequently it paves the path for more realistic traffic models.

In the last decades, technological advances related to RNA manipulation enabled and expanded its application in vaccine development. This approach comprises synthetic single-stranded mRNA molecules that direct the translation of the antigen responsible for activating the desired immune response. The success of RNA vaccines depends on the delivery vehicle employed. Among the systems, yeasts emerge as a new approach to a natural delivery platform. The presence of β-glucans and mannans in its wall is responsible for the adjuvant action of this system. Yeasts are already employed to deliver protein antigens, with success and efficacy demonstrated through pre-clinical and clinical trials. Yeast β-glucan capsules, microparticles, and nanoparticles are capable of modulating host immune responses and have a high capacity to carry RNA and small molecules, with bioavailability upon oral immunization and with targeting to receptors present in anti-gen-presenting cells (APCs). Besides, yeasts are interesting vehicles for the protection and specific delivery of therapeutic vaccines based on shRNA or dsRNA. In this review, we present an overview of the attributes of yeast or its derivatives for the delivery of RNA-based vaccines, discussing their current challenges and prospects for using this promising strategy.

Rapid advancements in the fifth generation (5G) communication technology and mobile edge computing (MEC) paradigm lead to the proliferation of unmanned aerial vehicles (UAV) in urban air mobility (UAM) networks, which provide intelligent services for diversified smart city scenarios. Meanwhile, the widely deployed internet of drones (IoD) in smart cities also brings up new concerns on performance, security, and privacy. The centralized framework adopted by conventional UAM networks is not adequate to handle high mobility and dynamicity. Moreover, it is necessary to ensure device authentication, data integrity, and privacy preservation in UAM networks. Thanks to characteristics of decentralization, traceability, and unalterability, Blockchain is recognized as a promising technology to enhance security and privacy for UAM networks. In this paper, we introduce LightMAN, a lightweight microchained fabric for data assurance and resilience-oriented UAM networks. LightMAN is tailored for small-scale permissioned UAV networks, in which a microchain acts as a lightweight distributed ledger for security guarantees. Thus, participants are enabled to authenticate drones and verify the genuineness of data that is sent to/from drones without relying on a third-party agency. In addition, a hybrid on-chain and off-chain storage strategy is adopted that not only improves performance (e.g,.latency and throughput) but also ensures privacy preservation for sensitive information in UAM networks. A proof-of-concept prototype is implemented and tested on a Micro Air Vehicle Link (MAVLink) simulator. The experimental evaluation validates the feasibility and effectiveness of the proposed LightMAN solution.

The use of cobalt-iron (VaCoFe) core is investigated as an alternative to silicon-iron (FeSi) on the design of interior permanent magnet synchronous motors (IPMSM). A spoke-type IPMSM geometry is optimized considering FeSi and VaCoFe cores for a torque range up to 40 N.m, providing a general comparative analysis between materials, considering the application of a 4-motor competition vehicle’s powertrain. A genetic optimization algorithm is applied over a hybrid analytical/finite-element model of the motor to provide sufficiently accurate electromagnetic and thermal results within a feasible time. VaCoFe can result in an estimated increase of up to 5 % in efficiency for the same torque, or up to 64 % torque increase for the same efficiency level. After optimization, and using a detailed time-dependent model, a potential 3.2 % increase in efficiency, a core weight reduction of 4.1 %, and a decrease of 9.6 % in the motor’s core volume was found for the VaCoFe at 20 Nm. In addition, for the same motor volume, the VaCoFe allows an increase of 51.9 % of torque with an increase of 1.1 % of efficiency, when compared with FeSi.

The copper end paste used in multilayer ceramic capacitors sintered in nitrogen atmosphere will lead to carbon residue of organic vehicle, which will lead to the reduction of electrode conduc-tivity and high scrap rate. With an attempt to leave no residue in the sintering, the compatibility of solvents and thickeners should be improved because it has an important influence on the hi-erarchical volatilization and carbon residue of organic vehicles. In this work, the volatility of different solvents was compared and several solvents were mixed in a definite proportion to prepare an organic vehicle with polyacrylate resins. The hierarchical volatility and solubility parameters of mixed solvents were adjusted effectively by changing proportions of different components, the thermogravimetric curves of resins and organic vehicles were measured by thermogravimetric analyzer, the effect of solubility parameter on the dissolvability of resins in the solvent and the residual of organic vehicles were studied. Results showed that the hierar-chical volatilization of solvents can be obtained by mixing different solvents; the intrinsic vis-cosity of the organic vehicle is higher and the thermal decomposition residue of polyacrylate resins is lower when the solubility parameters of mixed solvents and polyacrylate resins are closer. The low residual sintering of organic vehicles can be achieved by using the mixed solvent with hierarchical volatility and approximate solubility parameters as resins.

Under the goal of carbon peak and carbon neutrality, the carbon emission reduction of the automobile industry has attracted more and more attention in recent years. Electric vehicle has the dual attributes of power load and energy storage unit. With the increase of the number of electric vehicles, reducing carbon emissions through the collaborative interaction between electric vehicle and power network will become an important way to control carbon emissions in the automotive field. In this study, an optimization model of emission reduction benefits based on integrated development of electric vehicle and power grid is proposed, which explores the best technical way of synergy between power grid and electric vehicle, achieves the best carbon reduction effect and provides a model basis for large-scale demonstration application. Numerical simulations based on the real case in Beijing are conducted to validate the effectiveness of the proposed method.

The article presents development of the more electric turbofan engine in distributed architecture with a design thrust in the range from 3 to 7.5 and from 7.5 to 30 kN for small and medium-sized unmanned aerial vehicles. The engine subsystems are considered as separate smart modules with a built-in control system, exchanging data via a digital channel with the central engine control and diagnostics unit. The key smart engine units are combined in the following subsystems: starter and turbine generators, oil pumps, actuator of guide vanes, fuel pumps, fuel metering unit, control and diagnostic unit. All pumps and guide vane actuator are electrically driven. Control and monitoring signals are transmitted via a digital bus. Functional and reliability analysis, technical configuration design of each subsystem are presented. Based on analysis of the architecture of distributed control system for gearbox-free more electric engine, different configurations of described subsystems are proposed.

In this contribution, different lane-keeping control strategies for Autonomous Ground Vehicles (AGV) have been analyzed and compared. The AGV must be oriented and kept within a given reference path using the front wheel steering angle as the control action for a specific longitudinal velocity. While non-linear models can describe the lateral dynamics of the vehicle in an accurate manner, they might lead to difficulties when computing some real-time control laws such as Model Predictive Control (MPC). Linear Parameter Varying (LPV) models can provide a trade-off between computational complexity and model accuracy. Another way to reduce computational complexity is to explore other control strategies, for example, the one based on the Inverse Kinematic Bicycle model (IKIBI). Additionally, AGV sensors typically work at different measurement acquisition frequencies so that Kalman Filters (KF) are usually needed for sensor fusion. If these frequencies are slower than the actuation rate, a multi-rate KF may be needed. The two control strategies (MPC using a LPV model and IKIBI) have been compared in simulations over a circuit path in the presence of process and measurement Gaussian noise. The MPC controller has shown to provide a more accurate lane-keeping behavior than an IKIBI control strategy. Finally, it has been seen that Dual-Rate Extended Kalman Filters (DREKF) constitute an essential tool when only slow and noisy sensor feedback is available in an AGV lane-keeping application.

Manual evaluation of crop injury to herbicides is time-consuming. Unmanned aircraft systems (UAS) and high-resolution multispectral sensors and machine learning classification techniques have the potential to save time and improve precision in the evaluation of herbicide injury in crops, including grain sorghum (Sorghum bicolor L. Moench). The objectives of this research were to (1) evaluate three supervised classification algorithms (support vector machine, maximum likelihood, and random forest) for categorizing high-resolution UAS imagery to aid in data extraction and (2) evaluate the use of vegetative indices (VIs) collected from UAV imagery as an alternative to traditional methods of visual herbicide injury assessment in mesotrione-tolerant grain sorghum breeding trials. An experiment was conducted in a randomized complete block design using a factorial treatment arrangement of three genotypes by four mesotrione doses. Herbicide injury was rated visually on a scale of 0 (no injury) to 100 (complete plant mortality). The UAS flights were flown at 9, 15, 21, 27, and 35 days after treatment. Results show the SVM algorithm to be the most consistently accurate, and high correlations (r = -0.83 to -0.94; p < 0.0001) were observed between the normalized difference vegetative index (NDVI) and ground-measured herbicide injury. Therefore we conclude that VIs collected with UAS coupled with machine learning image classification, has the potential to be an effective method of evaluating mesotrione injury in grain sorghum.

Car parking is a challenging part of urban transportation and the traffic violations around it cause many problems for citizens. In recent years, due to the fast growth and development of urbanization, temporary and unauthorized stopping of cars along the streets, especially in large cities, has led to an increased traffic, urban disorders, dangers for citizens, and violation of rules. Studies have shown that there is a direct relationship between vehicle parking violations and urban places. GIScience capabilities and tools play an important role in analysing the spatial distribution of these violations. In this study, we investigated the spatial distribution of vehicle violations in a region of Tehran, Iran that is suffering from a heavy traffic load and heavily polluted air. Although two dissimilar urban segregations exist in the north and south of the study area, our analysis indicates a similar pattern of car parking violations. In both of the areas, about 70% of all curb parks are legal, while the remaining are illegal. Also, spatial analysis reveals a direct relationship between some POIs and the occurrence of car park violations so that the density of legal curb parks is high near some POIs, and less near some others and vice versa. For example, the number of vehicle park violation around the hospitals is more than the average of the study area. However, the number of park violations around the universities is less than the average. Our findings reveal that co-location of certain POIs, for instance a hotel and a supermarket will lead to an increase in the number of park violations. In other words, there is a strong correlation between the type of POIs and curb-parks violations. Our results also show that POIs have an impact radius that leads to violations occurring in that area. For example, the area of the impact of a hospital on the creation of car park violations was estimated at 125 meters. Our presented approach along with the discussed findings along with conclusions can be useful to a large range of stakeholders including urban planner, traffic police departments, local municipalities, law enforcement agencies, and environmentalists to have a better perspective of infrastructure planning.

This research was conducted to manufacture thermally expandable microspheres (TEMs) for vehicles’ underbody coating and to apply them on an industrial scale. TEMs heat resistance was studied depending on the ratios of a cross-linking agent and an initiator. This research focused on the content of a cross-linking agent and how it affected the results. The TEMs’ outer shell was thickened to solve the problem of the foam expansion ratio’s reduction that occurred due to the shrinkage after the maximum expansion (Tmax) was reached. After foaming, the cross-sectional thickness and surface of the sample with thickened outer shell were observed. The TEMs with the thickened shell showed the least shrinkage, which indicated excellent shrinkage stability, even after prolonged exposure to heat.

Smart cities and smart technologies have been incorporated into several axes to increase the comfort of life. The connected building's concept was introduced for this reason. However, it was utilized in power management for better organizing, greater buildings management, and monetary savings. Cars technologies and the number of vehicles are also involved; Nowadays, each house has at least one car. Technological evolution helped to make those cars intelligent and connected. In the latest versions, the majority of those cars were equipped with several sensors, several communication protocols and a principal electrical control unit (ECU), especially for the electric vehicle model. This type of architecture was an essential element in a smart city, thus, it helps to manage power and decide when a vehicle needs to be charged. Based on the smart city concept and using possible network communication between buildings and vehicles, EVs can share their own information related to the powerful experience on a specific path. This information can be gathered in a gigantic database and used for managing the power inside these vehicles. In this field, we propose in this paper a new approach for power management inside an electric vehicle based on bi-communication between vehicles and buildings. The proposed approach is founded on two essential parts; the first is related to vehicles’ classification and buildings’ recommendation according to different car positions. Two algorithms, related to the SVC and neural network was employed in this work for implementing the final process. Different possibilities and situations were discussed for this approach. The proposed method was tested and validated using Simulink/Matlab application. The state of charge of the used battery was compared at the end of this work, for two specified cases, for showing the contribution of this approach.

Location-Based Services (LBS) have been widely deployed for the connected vehicle (CV) applications such as vehicle navigation,vehicle tracking and location-based augmented reality. The current LBS deployments have limitations in supporting time-critical CV use cases, including vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle-to-people (V2P) safety applications. The paper presents the new LBS framework based on the publish-subscribe communication paradigm, to enable device-to-device (D2D) connections through use of selected application protocols in the application layer of the TCP/IP layered protocol model. Two publish-subscribe application protocols, Distributed Data Service (DDS) real-time publish and subscribe (DDS-RTPS) and Message Queue Telemetry Transport (MQTT), are introduced to support the LBS D2D applications. A number of test scenarios with Mosquitto MQTT and OpenDDS under 4G-mobile broadband (MBB) services are designed to assess the transmit/receive round-trip time (RTT) and packet-loss rate (PLR) with settings of a publisher to multiple subscribers, to simulate the connections to multiple vehicles. The transmission frequency is set for 10 Hz and the message sizes vary from 100 to 2000 Bytes. The PLRs are defined as the percentages of the delayed messages beyond a delay limit. Static test results with OpenDDS show that for the RTT delay beyond the limit of 100 ms, the total PLRs range between 5.25% and 8.76% for the message size of 50 to 2000 Bytes. Vehicle testing results with Mosquitto show that PLRs for the RTT delays between 200 ms and 1000 ms are 0.63%, 3.58% and 5.77%, for connections with 1, 4 and 10 vehicles, respectively. The results demonstrate the potential of the D2D LBS framework for medium-demanding CV safety applications such as V2P and V2I use cases, taking advantages of the 4G-MBB services and 5G extreme mobile broadband (eMBB) services and mobile devices generally available with all road users.

The abrupt increase of the electric vehicles in Bangladesh needs huge amount of power. As a result, alternative energy sources are emphasized due to limited fossil fuels in order to develop a sustainable energy sector with environment friendly resources. Bangladesh has an enormous potential in the field of renewable resources like biogas and biomass. This paper presents 20 kW electric vehicle charging station (EVCS) utilizing biogas resources where maximum energy requirement is 100 kWh. In this paper, the biogas based EVCS is designed using MATLAB Simulink and HOMER software. Daily 15-20 electric vehicles can be recharged their batteries using the proposed charging station. The proposed system offers lower cost of energy compared to the grid electricity. Moreover, the proposed charging station shows 68.75% reduction in CO₂ emission than grid based charging station. In addition, the proposed EVCS will save monthly $ 16.25 and $ 27.50 respectively for easy bike and auto rickshaw type electric vehicles in Bangladesh.

Analysis of 1382 measures of battery State of Health (SoH) from 283 Nissan Leafs (“Leaf/s”), manufactured between 2011 and 2017, has detected a faster rate of decline in this measure of energy-holding capacity for 30 kWh variants. At two years of age, the mean rate of decline of SoH of 30 kWh Leafs was 9.9% per annum (95% uncertainty interval of 8.7% to 11.1%; n = 82). This was around three times the rate of decline of 24 kWh Leafs which at two years averaged 3.1% per annum (95% uncertainty interval of 2.9% to 3.3%; n = 201). For both variants there was evidence for an increasing rate of decline as they aged, although this was much more pronounced in the 30 kWh Leafs. Higher use of rapid DC charging was associated with a small decrease in SoH. Additionally, while 24 kWh cars with greater distances travelled showed a higher SoH, in 30 kWh cars there was a reduction in SoH observed in cars that had travelled further. The 30 kWh Leafs sourced from United Kingdom showed slower initial decline than those from Japan, but the rate of decline was similar at two years of age. Improvements in the battery health diagnostics, continuous monitoring of battery temperatures and state of charge, and verification of a fundamental model of battery health are needed before causes and remedies for the observed decline can be pinpointed. If the high rate of decline in battery capacity that we observed in the first 2.3 years of a 30 kWh Leaf’s lifetime were to continue, the financial and environmental benefits of this model may be significantly eroded. Despite 30 kWh Leafs accounting for only 14% of all light battery electric vehicles registered for use on New Zealand roads at the end of February 2018, there is also the potential for the relatively poor performance of this specific model to undermine electric vehicle uptake more generally unless remedies can be found.

The technologies influencing alternative ways of transportation are augmenting in recent years as the need for transportation is increasing rapidly due to urbanization and motorization. In this paper, a solar powered electric auto-rickshaw (SPEA) is designed and developed for Indian conditions. The developed vehicle is comprehensively analyzed techno-economically for its viability in the Indian market. The performance analysis of SPEA results in an optimal charging rate of 2 kWh per day with an average solar irradiance of 325 W/m2. The discharging characteristics are studied based on different loading conditions. The vehicle achieved a maximum speed of 21.69 km/h with battery discharge rate of 296W at 90kg load and also reached a maximum discharge rate of 540W at 390kg loading with a maximum speed of 12.11 km/h. The environmental analysis of SPEA displayed yearly CO2 emissions of 1,777 kg, 1,987 kg and 1,938 kg using Compressed Natural Gas, Liquefied Petroleum Gas and gasoline engines respectively can be mitigated using SPEA. The results of financial analysis of SPEA were welcoming as the investor gets 24.44% lesser payback duration compared to gasoline run vehicle. Socio-Economic analysis of SPEA discussed its significant advantages and showed 18.73% and 3.9% increase in yearly income over gasoline driven and battery driven vehicles.

With the great development of intelligent transportation systems (ITS), travel time prediction has attracted the attentions of many researchers and a large number of prediction methods have been developed. However, as an unavoidable topic, the predictability of travel time series is the basic premise for travel time prediction has received less attention than the methodology. Based on the analysis of the complexity of travel time series, this paper defines travel time predictability to express the probability of correct travel time prediction and proposes an entropy-based method to measure the upper bound of travel time predictability. Multiscale entropy is employed to quantify the complexity of travel time series, and the relationships between entropy and the upper bound of travel time predictability are presented. Empirical studies are made with vehicle trajectory data in an express road section. The effectiveness of time scales, tolerance, and series length to entropy and travel time predictability are analysis, and some valuable suggestions about the accuracy of travel time predictability are discussed. Finally, the comparisons between travel time predictability and actual prediction results from two prediction models, ARIMA and BPNN, are conducted. Experimental results demonstrate the validity and reliability of the proposed travel time predictability.

This paper presents a new methodology to derive and analyze strategies for a fully decarbonized urban transport system which combines conceptual vehicle design, a large-scale agent-based transport simulation, operational cost analysis, and life cycle assessment for a complete urban region. The holistic approach evaluates technical feasibility, system cost, energy demand, transportation time and sustainability-related impacts of various decarbonization strategies. In contrast to previous work, the consequences of a transformation to fully decarbonized transport system scenarios are quantified across all traffic segments, considering procurement, operation and disposal. The methodology can be applied to arbitrary regions and transport systems. Here, the metropolitan region of Berlin is chosen as a demonstration case. First results are shown for a complete conversion of all traffic segments from conventional propulsion technology to battery electric vehicles. The transition of private individual traffic is analyzed regarding technical feasibility, energy demand and environmental impact. Commercial goods, municipal traffic and public transport are analyzed with respect to system cost and environmental impacts. We can show a feasible transition path for all cases with substantially lower greenhouse gas emissions. Based on current technologies and today’s cost structures our simulation shows a moderate increase in total systems cost of 13-18%.

The article presents investigations of rail vehicle bogie of Y25 type. The Y25 bogie family is one of the most commonly used freight car bogie designs. In addition to several significant advantages characterising this design, it also has several disadvantages observed since the beginning of more than fifty years of operation in several types of cargo vehicles. One of these defects observed in real systems is "unsatisfactory running stability", particularly at long straight tracks. The article used commercial engineering software VI-Rail to create a model of the gondola car (type 412W Eaos) with two Y25 bogies. The car model was tested empty and loaded (maximum permissible load). Motion along straight and curved tracks with different radii values was analysed. The vehicle velocity was changed from a few m/s to the maximum values for which stable solutions of the model existed. For each route, a non-linear critical velocity was determined, defining the maximum operating velocity of the modelled car. The model solutions were recorded, while just one was selected to present the results - the first wheelset lateral displacement ylw. The opinion about the "imperfect running quality" in curved tracks was confirmed. The possible appearance of self-exciting wheelset vibrations in the modelled car's operating velocity range in the laden state was also observed. The research results on the impact of the bogie suspension parameters change on the vehicle model stability are presented. The crucial parameter in the bogie suspension was indicated. Reducing its value by several percent about the nominal value increases the critical velocity of the car to values higher than the maximum operating velocity of the modelled vehicle.

We propose a novel unsupervised semantic segmentation method for fast and accurate flood area detection utilizing color images acquired from Unmanned Aerial Vehicles (UAVs). To the best of our knowledge, this is the first fully unsupervised method for flood area segmentation in color images captured by UAVs, without the need of pre-disaster images. The proposed framework addresses the problem of flood segmentation based on parameter-free calculated masks and unsupervised image analysis techniques. First, a fully unsupervised algorithm gradually excludes areas classified as non-flood utilizing calculated masks over each component of the LAB colorspace, as well an RGB vegetation index and the detected edges of the original image. Unsupervised image analysis techniques, such as distance transform, are then applied, producing a probability map for the location of flooded areas. Finally, flood detection is obtained by applying the hysteresis thresholding segmentation. The proposed method is tested and compared with variations, and other supervised methods in two public datasets, consisting of 953 color images in total, yielding high-performance results, with 87.4% and 80.9% overall accuracy and F1-Score, respectively. The results and computational efficiency of the proposed method show that it is suitable for on board data execution and decision-making during UAVs flight.

When wildlife and motor vehicles collide, the result for the animals is often death (roadkill). A commercial roadkill Virtual Fence (VF) mitigation device (iPTE Traffic Solutions) was used in a field trial to test its effectiveness along a 4.9-km segment of road on Bruny Island, Tasmania. A total of 585 days of monitoring of roadkill by species was conducted, with six sections alternatively switched on or off, according to a variation of a Crossover and Multiple Before-After-Control-Impact (MBACI) experimental designs that divided monitoring into “off-on” then “on-off” blocks of periods with each “on” period followed by a 16 d “wash-out” period. For the six sections over the four periods that exclude wash-outs, the 24 aggregated values of daily counts of roadkill of Tasmanian pademelons (Thylogale billardierii), that gave a total count of 222, were modelled. Other species were killed in insufficient numbers for analysis to be informative. The statistical analysis exploited the MBACI design to estimate the VF effect using a log-odds ratio parameter (LORP) while accounting for local spatio-temporal effects. Both versions of the analysis, either averaging over the three spatial replicates (paired sections) or two temporal replicates (blocks) gave no statistically significant effect of the VF judged as a LORP estimate not sufficiently below zero. Corresponding percentage reduction estimates of 9% and 16% were derived from the LORP estimates and given parameter uncertainty estimates the power to detect as significant a LORP corresponding to a 50% reduction in rate were 0.5 and 0.6, respectively. This study confirms results from a similar field trial carried out in 2018 also in southern Tasmania and adds to the evidence that this VF is likely to give if anything only a minor reduction in roadkill for a commonly killed species for which researchers have the best chance of detecting any practically useful reduction.

Background: Previous investigations have found a correlation between abnormal curvatures and a variety of patient complaints such as cervical pain and disability. However, no study has shown that loss of the cervical curve is a direct result of exposure to a motor vehicle collision (MVC). This investigation presents a retrospective consecutive case series of patients with both a pre-injury cervical lateral radiograph (CLR) and a post-injury CLR after exposure to a MVC. Computer analysis of digitized vertebral body corners on CLRs was performed to investigate the possible alterations in the geometric alignment of the sagittal cervical curve. Methods: Three spine clinic records were reviewed, over a 2-year period, looking for patients where both an initial lateral cervical x-ray and an examination were performed prior to the patient being exposed to a MVC; afterwards an additional exam and radiographic analysis were required. A total of 41 patients met the inclusion criteria. Examination records of pain intensity on numerical pain rating scores (NPRS) and neck disability index (NDI), if available, were analyzed. The CLRs were digitized and modeled in the sagittal plane using curve fitting and the least squares error approach. Radiographic variables included total cervical curve (ARA C2-C7), Chamberlain’s line to horizontal (skull flexion), horizontal translation of C2 relative to C7, segmental translations (retrolisthesis and anterolisthesis), and circular modelling radii. Results: There were 15 males and 26 females with an age range of 8-65 years. Most participants were drivers (28) involved in rear end impacts (30). The pre-injury NPRS was 2.7 while the post injury was 5.0; p &lt; 0.001. The NDI was available on 24 /41 (58.5%) patients and was found to increase following exposure to the MVC: 15.7% to 32.8%, p &lt; 0.001. An altered cervical curvature was identified following exposure to MVC, characterized by an increase in radius of curvature and an approximate 8° reduction of lordosis from C2-C7; p &lt; 0.001. The mid cervical spine (C3-C5) showed the greatest curve reduction with localized mild kyphosis at these levels on average. Several participants developed segmental translations that approached the instability value of translation &gt; 3.5mm. Conclusions: Abnormalities of cervical lordosis occurred following exposure to a MVC. The average loss of cervical curvature was 8° from C2-C7, with mild flexion of the mid cervical segments. Snap through type or dynamic buckling during the MVC may explain our findings. The primary contribution of this study to the motor vehicle collision literature is that altered cervical lordosis may result from MVC exposures and the consequences of an altered cervical alignment, in turn, may lead to future pain, disability and decreased health-related quality of life.

Efficient processing of end-of-life lithium-ion batteries of electric vehicles is important and a pressing challenge for a circular economy. Regardless of whether the processing strategy is recycling, repurposing or remanufacturing, the first processing step would usually involve disassembly. As battery disassembly is a dangerous task, efforts have been made to robotise it. In this paper, a robotic disassembly platform using four industrial robots is proposed to automate the non-destructive disassembly of a plug-in hybrid electric vehicle battery pack into modules. The work was conducted as a case study to demonstrate the concept of autonomous disassembly of an electric vehicle battery pack. A two-step object localisation method based on visual information is used to overcome positional uncertainties from different sources and is validated by experiments. Also, the unscrewing system is highlighted, and its functions, such as handling untightened fasteners, loosening jammed screws, and changing the nutrunner adapters with square drives, are detailed. Furthermore, the time required for each operation is compared with that taken by human operators. Finally, the limitations of the platform are reported and future research directions are suggested.

In the field of engineering, tracked vehicles play a significant role in accessing challenging terrains. Maintaining trafficability is of the utmost importance when it comes to tracked vehicles traversing rugged terrains; it provides stability, traction, and adaptability for a wide range of applications, including deep-sea exploration. The terrain characteristics pertain to the interactions that determine the mobility of tracked vehicles. Traditional Terramechanics models are in-adequate in depicting the correlation between tracks and soil. An important issue is the lack of consideration for the moisture content in the soil and the rate at which sinking occurs in these models. Examining sinkage speed helps describe the ability of tracked vehicles to move at high speeds. The focus is on the critical factors influencing the mobility of tracked vehicles, precisely the sinkage speed and its relationship with pressure. Experimental tests in a soil bin, utilizing Bentonite/Diatom sediment soil, aim to measure these terrain factors. The study introduces a rubber-tracked vehicle, pressure, and moisture sensors to monitor pressure sinkage and moisture, evaluating cohesive soils (Bentonite/Diatom) in combination with sand and gravel mixtures. Findings reveal that higher moisture content in Bentonite soil leads to increased track slippage and sinkage compared to Diatom soil. Regarding soil behavior, diatom soil exhibited notable compaction and sinkage characteristics in contrast to bentonite soil. The distinct behaviors of these soils underscore the complexity of terrains, emphasizing the need for a meticulous understanding of soil properties before applying them in natural environments. Furthermore, this study contributes precision in assessing natural terrains, enhancing tracked vehicle design, and advancing terrain mechanics comprehension for off-road exploration. This knowledge contributes to better vehicle design and a deeper comprehension of terrain mechanics.

This study assesses the applicability of different-resolution multispectral remote sensing images for mapping and estimating the aboveground biomass (AGB) of Carpobrotus edulis, a prominent invasive species in European coastal areas. The performance of three sets of multispectral images with different resolutions was compared: (i) 2.5 cm Ground Sample Distance (GSD), 5 cm GSD and 10 cm GSD images. The images were classified using the supervised classification algorithm Random Forest and later improved applying a sieve filter. The results show that the three tested image resolutions allow constructing reliable coverage maps of C. edulis, with Overall Accuracy values of 89%, 85% and 88% for the classification of the 2.5 cm, 5 cm and 10 cm GSD images, respectively. Samples were also collected, dried and weighed to estimate AGB using the relationship between the Dry Weight and Vegetation Indices (VI). The regressions were evaluated based on their R² and Normalised RMSE. The best-performing VI-DW regression models achieved: R² = 0.87 and NRMSE = 0.09 for the 2.5 cm resolution; R² = 0.77 and NRMSE = 0.12 for the 5 cm resolution; and, R² = 0.64 and NRMSE = 0.15 for the 10 cm resolution. C. edulis area and total AGB were: 3441.10 m² and 28,327.1 kg (with a Relative Error (RE) = 0.08), for the 2.5 cm resolution; 3070.04 m² and 29,170.8 kg (RE = 0.08), for the 5 cm resolution; and, 2305.06 m² and 22,135.7 kg (RE = 0.11), for the 10 cm resolution. Differences were analysed in detail, spatially, to determine their causes. Final analyses suggest that, for C. edulis, multispectral imagery of up to 5 cm GSD is adequate for the estimation of the species’ distribution and biomass.

Tracked vehicles play a vital role in accessing challenging terrains, offering stability, traction, and versatility for various applications, including deep-sea exploration. However, the seafloor and sediment contain valuable but poorly understood resources, making it a significant challenge for tracked vehicles during exploration. Consequently, it is imperative to conduct comprehensive studies before considering these terrains for off-road purposes. In this study, we developed a rigid, flexible track vehicle equipped with various sensors, focusing on monitoring pressure sinkage. Cohesive soils like Bentonite and Diatom soil, particularly when combined with sand and gravel mixtures, were used for the investigation and assessed for ground pressure, bearing capacity, and sinkage in rigid-flexible tracked vehicles on these characterized terrains under varying moisture conditions. Our findings indicated that increased moisture content in bentonite soil resulted in significantly higher track slippage and sinkage compared to diatomaceous earth soil. Regarding soil behavior, diatom soil exhibited notable compaction and sinkage characteristics compared to bentonite soil. These significant variations between these two soil types under changing moisture conditions highlight the complexity of such terrains. Therefore, understanding the soil and its physical properties is crucial before applying them to natural terrains. Furthermore, this study emphasizes the importance of precision in assessing the physical properties of natural terrains for the development of tracked vehicles for off-road exploration. This knowledge contributes to better vehicle design and a deeper comprehension of terrain mechanics.

The rapid growth of equipment tasks in 5G large-scale machine-type communication scenarios presents challenges due to user equipment’s (UE) limited computing power and power constraints. To address these limitations, task offloading to unmanned aerial vehicles (UAVs) has gained attention. However, traditional cloud computing centres far from the UE fail to meet latency requirements. Mobile Edge Computing (MEC) emerges as a solution by deploying computing servers at the edge of the cellular network to enhance service responsiveness. However, traditional MEC solutions need more flexibility. This paper explores the advantages of UAVs, including flexibility and rapid deployment, and considers UAV power constraints. It proposes a joint optimization approach for user offloading strategies and UAV trajectories to minimize the overall energy consumption of UE. By leveraging the benefits of UAVs and MEC, the proposed method aims to improve task execution efficiency in energy-constrained 5G machine-type communication scenarios.

In this work, we propose a Fog-enabled UAV-as-a-Service (FU-Serve) architecture to address the issues of data transmission delay for serving time-critical Internet of Things (IoT) applications. Traditionally, in a UAV-as-a-Service (UaaS) platform, different UAVs host heterogeneous sensors, which sense the physical phenomenon and transmit the sensed data to a centralized entity. Transmission of data from the sensors to the centralized entity and making any decision for an application consumes a significant amount of time. Consequently, the traditional UaaS architecture is unsuitable for serving time-critical IoT applications such as transportation, healthcare, and industries. To address these issues of service latency for time-critical IoT applications, we present the FU-Serve architecture by introducing the concept of fog computing in the UaaS platform. We discuss all the components of FU-Serve elaborately in this paper. Additionally, we architect optimal and dynamic fog node selection mechanisms for FU-Serve, which reduce the transmission delay in the networks. The simulation results show that the FU-Serve outperforms by $75\%$ compared to the traditional UaaS platform.

Traffic congestion and frequent traffic accidents have become main problems affecting urban traffic. Effective location prediction of vehicle trajectory can help alleviate traffic congestion, reduce the occurrence of traffic accidents, and optimize the urban traffic system. Vehicle trajectory is closely related to the surrounding Points of Interest (POI). POI can be considered as the spatial feature and can be fused with trajectory points to improve prediction accuracy. A Local Dynamic Graph Spatiotemporal- Long Short-Term Memory (LDGST-LSTM) is proposed in this paper to extract and fuse the POI knowledge and realize next location prediction. POI semantic information is learned by constructing the traffic knowledge graph, and spatial and temporal features are extracted by combining Graph Attention Network (GAT) and temporal attention mechanism. Moreover, the weights of POI that influence location prediction are visualized to improve the interpretability of the proposed model. The effectiveness of LDGST-LSTM is verified on two datasets, including Chengdu taxi trajectory data in October 2018 and August 2014. The accuracy and robustness of the proposed model are significantly improved compared with the benchmark models.

Traffic signal control plays a key role in managing urban traffic flow, enhancing safety, and minimizing congestion and conflicts. Effective green time allocation is a critical element of this control process. This research explores the utilization of LiDAR sensor technology in the optimization of green time allocation for one phase of a traffic signal at a signalized intersection. LiDAR sensors provide precise and real-time data on vehicle presence and traffic patterns, enabling a data-driven approach to traffic signal control. The study begins with an analysis of the limitations of traditional traffic signal control strategies, which often rely on fixed-time plans or rudimentary vehicle detection systems. These approaches can lead to suboptimal green time allocation, resulting in inefficient traffic management and increased vehicle delays.The integration of LiDAR sensors provides detailed information on vehicle queues, arrival rates, and vehicle types. The research presents a practical framework for green time allocation optimization, considering factors such as intersection geometry, traffic volumes, and signal coordination. An intelligent control algorithm was developed that uses LiDAR data to determine the optimal green time for a specific phase, thereby reducing unnecessary waiting times and enhancing intersection efficiency. The effectiveness of the proposed LiDAR-based green time allocation strategy is demonstrated through extensive field tests. The results indicate significant improvements in intersection throughput, reduced delays, and enhanced traffic safety. In conclusion, this research highlights the transformative potential of LiDAR sensor technology in traffic signal control, specifically in the context of optimizing green time allocation. The findings support the notion that adaptive and data-driven strategies, when integrated with LiDAR sensors, can contribute to more efficient, sustainable, and safe urban traffic management. This research aims to provide valuable insights for transportation engineers, policymakers, and researchers seeking innovative solutions for modern urban traffic control.

In response to the issue of vehicle escape guidance, this manuscript proposes a unified intelligent control strategy synthesizing optimal guidance and Meta Deep Reinforcement Learning (DRL). Optimal control with minor energy consumption is introduced to meet terminal latitude, longitude and altitude. Maneuvering escape is realized by adding longitudinal and lateral direction maneuver overloads. Maneuver command decision model is calculated based on Soft-Actor-Critic (SAC) networks. Meta learning is introduced to enhance autonomous escape capability, which improves generalization performance to time-varying scenarios not encountered in the training process. In order to obtain training samples at a faster speed, this manuscript uses the prediction method to solve reward values, which avoiding a large number of numerical integration. The simulation results manifest that the proposed intelligent strategy can achieve high precise guidance and effective escape.

The application of deep learning in the detection of Synthetic Aperture Radar (SAR) targets has been primarily limited to large objects such as ships and airplanes, with much less popularity in detecting SAR vehicles. The complexities of SAR imaging make it difficult to distinguish small vehicles from the background clutter, creating a barrier to data interpretation and the development of Automatic Target Recognition (ATR) in SAR vehicles. The scarcity of datasets has inhibited progress in SAR vehicle detection in the data-driven era. To address this, we introduce a new synthetic dataset called Mix MSTAR, which mixes target chips and clutter backgrounds with original radar data at the pixel level. Mix MSTAR contains 5,392 objects of 20 fine-grained categories in 100 high-resolution images, predominantly 1478x1784 pixels. The dataset includes various landscapes such as woods, grasslands, urban buildings, lakes, and tightly arranged vehicles, each labeled with Oriented Bounding Box (OBB). Notably, Mix MSTAR presents fine-grained object detection challenges by using the Extended Operating Condition (EOC) as a basis for dividing the dataset. Furthermore, we evaluate 9 benchmark rotated detectors on Mix MSTAR and demonstrate the fidelity and effectiveness of the synthetic dataset. To the best of our knowledge, Mix MSTAR represents the first public multi-class SAR vehicle dataset designed for rotated object detection in large-scale scenes with complex background. Mix MSTAR is available at: https://github.com/TheGreatTreatsby/Mix-MSTAR-mmrotate.

In the computational prediction of bridge vibrations due to high-speed train traffic, the most accurate results can be obtained by considering the interaction dynamics between the train, the superstructure, and the supporting structure. To achieve this, a detailed understanding of the coupling properties of all elements is crucial as they significantly influence the calculated vibrations. The studies in this article investigate the influence of different levels of modeling complexity on the computational acceleration results of single-span girder bridges with a ballasted superstructure. A numerical study on an extensive parameter field of single-span girder bridges is conducted to investigate the influence of modeling the bridge structures as coupling beams, i.e., by considering them as two vertically coupled beams representing the track (rails and sleepers) and the supporting structure. The connection between both beams reflects the stiffness and damping properties of the ballasted superstructure and can reproduce its load-distribution capacity. The excitation is applied as either a moving load or a multi-body model of the train, an Austrian Railjet, to evaluate interdependencies of interaction effects between the vehicle and track, and between track and bridge structure. The reference model is a simply-supported Bernoulli-Euler beam excited by moving axle loads. The comparison of acceleration results allows for identifying critical combinations of structural and train parameters for which the implementation of interaction dynamics has a particularly significant impact on the calculated vibrations and quantifying that impact. These findings provide the possibility of formulating structure-dependent recommendations concerning the targeted application of more complex modeling of the structure (coupling beam model) on the one hand and train (multi-body model) on the other.

This article addresses the problem of contact voltage increase caused by the low frequency oscillation of the train-grid system in the phase-separation process of the EMU. The article establishes the EMU-contact line-traction substation model, reveals the mechanism of low-frequency oscillation, and obtains the relationship between the separation phase angle of the pantograph with line and low-frequency oscillations. Methods to suppress the over-voltage during the low-frequency oscillation are proposed. The study shows that when the power phase angle of the pantograph-line separation is within the range of 60°~90° and 240°~270°, the voltage amplitude in the neutral zone is larger, the maximum amplitude can reach 52.2kV, and low-frequency oscillation will occur in the neutral zone. When low frequency oscillation occurs, the contact line voltage in the neutral zone is mainly 1/3 power frequency (16.7Hz). After installing an RC suppression device in the neutral zone, when the EMU enters the neutral zone, the absolute value of the voltage peak drops to below 20 kV. Compared with the situation where the suppression device is not connected, the absolute value of the voltage peak is reduced by nearly 60%. The study provides a basis for the design of the neutral zone of the contact line and the selection of the high-voltage equipment of the EMU.

In Thailand, parking time violation is a major problem, especially for mini-marts. At present the task of detecting parking time violation is mainly conducted manually using Closed-Circuit Television (CCTV). This method requires additional human labour to track incoming and outgoing vehicles. Therefore, low cost time violation tracking is needed. To the best of our knowledge, there has not been any research for parking violation detection and tracking conducted for parking time limits. This paper introduces a novel parking time violation detection algorithm using the Yolov8 and DeepSORT tracking algorithms to track vehicles in consecutive frames. The presented parking violation tracking algorithm can provide a guideline for research in parking time violation detection.

Vehicle HVAC systems can accumulate and recirculate highly infectious respiratory diseases via aerosols. Integrating UVC to complement automobile HVAC systems can protect occupants from developing allergies, experiencing inflammatory problems, or acquiring respiratory infectious diseases by inactivating pathogenic organisms. UVC can add little to no static pressure with minimal space, unlike mercury lamps which are larger and heavier. Additionally, UVC LEDs are effective at low voltage and have no mercury or glass. While previous experiments have shown UVC LED technology can reduce bacteriophage Phi6 concentrations by 1 log in 5 minutes (selected as the average time to clean the cabin air), those studies had not positioned LED within the HVAC itself or studied the susceptibility of the surrogate at the specific wavelength. This study aimed to assess the disinfection performance of UVC LEDs in automotive HVAC systems and determine the dose-response curve for bacteriophage Phi6, a SARS-CoV-2 surrogate. To achieve this, UVC LEDs were installed in a car HVAC system. To determine inactivation efficacy, a model chamber of 3.5 m3, replicating the typical volume of a car, containing the modified automobile HVAC system was filled with bacteriophage Phi6, and the HVAC was turned on with and without the UVC LEDs being turned on. The results revealed that HVAC complemented with UVC reduced bacteriophage Phi6 levels significantly more than the HVAC alone and reduced the viral concentration in the cabin by more than 1 LRV (90% viral reduction) in less than 5 minutes. The performance after 5 minutes is expected to be significantly better against SARS-CoV-2 because of its higher sensitivity to UVC, especially at lower wavelengths (below 270 nm). HVAC alone could not achieve a 90% viral reduction of bacteriophage Phi6 in 15 minutes. Applying UVC LEDs inside an HVAC system is an effective means of quickly reducing the number of aerosolized viral particles in the chamber, by inactivating microorganisms leading to improved cabin air quality.

This paper focuses on the comparative analysis of lithium-ion batteries (LIB) thermal management with aim to maintain working temperature in the range 15 ℃ – 35 ℃. This is to prevent thermal runaway and high temperature gradients. The proposed approach is to employ the biodiesel, situated inside the diesel/LIB powered hybrid electric vehicle, to supply as fuel and coolant. A 3S2P LIB module is simulated using Ansys-Fluent CFD software tool. The system without a coolant shows that LIB has exceeded the optimum maximum temperature, which leads to shortened life-cycle and poor performance. Four fatty acid methyl ester biodiesels are used as coolants, namely palm, karanja, jatropha, and mahua oils. When compared with conventional methods of cooling, using air and 3M Novec liquid, the palm biodiesel coolant proves to be the best option to maintain LIB temperature within the optimum working range. With the use of palm biodiesel, the system is estimated to lightweight the BTMS by 43%, compared to the case when 3M Novec is used to maintain the same temperature range.

This paper presents a cooperative parallel tabu search meta-heuristic for the vehicle routing problem with time windows. It is based on the scheme in which several search threads cooperate by asynchronously exchanging information on the best solutions identified. The exchanges are performed through a mechanism called adaptive memory which holds and manages a pool of solutions. This enforces the asynchronous strategy of information exchanges and ensures the independence of the individual search threads. Each of these independent threads implements a tabu search meta-heuristic. Comprehensive computational experiments and comparisons to best known solutions show that the proposed cooperative parallel tabu search algorithm is able to achieve 48 new best solutions on VRPTW benchmark instances.

This study provides a thorough review and analysis of the evolution of the Greek vehicle fleet over the last ~30 years, which is next used for the generation of high granularity fleet projections and for the estimation of relevant environmental benefits by 2030. The integrated methodology developed takes also into account vehicle clustering and the Brown’s Double Simple Exponential Smoothing technique that together with the adoption of COPERT based emission factors allow for the estimation of the anticipated emissions in 2030. Expected 2030 emissions levels suggest a reduction across all pollutants in comparison to 2018, ranging from 3.7% for PM10 to 54.5% for NMVOC (and 46% for CO, 14% for SO2, 28% for NOX and 21% for CO2). We find that Greece is on track with national goals concerning the reduction of air pollution from the transportation sector, stressing the positive contribution of EVs and new, "greener" vehicles, and setting new challenges for the further improvement of the sector beyond the 2030 outlook.

This paper investigates the path following control problem for a unmanned surface vehicle (USV) in the presence of unknown disturbances and system uncertainties. The simulation study combines two different types of sliding mode surface based control approaches due to its precise tracking and robustness against disturbances and uncertainty. Firstly, an adaptive linear sliding mode surface algorithm is applied, to keep the yaw error within the desired boundaries and then an adaptive integral non-linear sliding mode surface is explored to keep an account of the sliding mode condition. Additionally, a method to reconfigure the input parameters in order to keep settling time, yaw rate restriction and desired precision within boundary conditions is presented. The main strengths of proposed approach is simplicity, robustness with respect to external disturbances and high adaptability to static and dynamics reference courses without the need of parameter reconfiguration.

In this study, the external magnetic field emitted by a wireless power transfer (WPT) system and the internal electric field induced into human body models during recharging operations of a compact electric vehicle (EV) are evaluated. To this aim an ad-hoc formulation for the source modeling is coupled with a commercial software that performs numerical dosimetry. Specifically, two realistic anatomical models both in a driving position and in a standing posture are considered, and the chassis of the EV is modeled either as a currently employed aluminum alloys and as a futuristic carbon fiber composite panel. Aligned and misaligned coil configurations of the WPT system are considered as well. The analysis of the obtained results shows that the ICNIRP reference levels are exceeded in the driving position, especially for the carbon fiber chassis, whereas no exceedance is observed in terms of basic restrictions, at least for the considered scenarios.

Considering the significant advancements in autonomous vehicle technology, research in this field is of interest to researchers. To drive vehicles autonomously, controlling steer angle, gas hatch, and brakes need to be learned. The behavioral cloning method is used to imitate humans’ driving behavior. We created a dataset of driving in different routes and conditions and using the designed model, the output used for controlling the vehicle is obtained. In this paper, the Learning of Self-driving Vehicles Based on Real Driving Behavior Using Deep Neural Network Techniques (LSV-DNN) is proposed. We designed a convolutional network which uses the real driving data obtained through the vehicle’s camera and computer. The response of the driver is during driving is recorded in different situations and by converting the real driver’s driving video to images and transferring the data to an excel file, obstacle detection is carried out with the best accuracy and speed using the Yolo algorithm version 3. This way, the network learns the response of the driver to obstacles in different locations and the network is trained with the Yolo algorithm version 3 and the output of obstacle detection. Then, it outputs the steer angle and amount of brake, gas, and vehicle acceleration. The LSV-DNN is evaluated here via extensive simulations carried out in Python and TensorFlow environment. We evaluated the network error using the loss function. By comparing other methods which were conducted on the simulator’s data, we obtained good performance results for the designed network on the data from KITTI benchmark, the data collected using a private vehicle, and the data we collected.

This study aims to develop a hybrid approach based on backpropagation Artificial Neural Network (ANN) and spatial analysis techniques to predict particulate matter of size 2.5 µm (PM2.5) from vehicle exhaust emissions in the State of California using Aerosol Optical Depth (AOD) and several climatic indicators (relative humidity, temperature, precipitation, and wind speed). The PM2.5 data were generated using Motor Vehicle Emission Simulator (MOVES), the measured climatic variables and AOD were obtained from the California Irrigation Management Information System (CIMIS), and NASA’s Moderate Resolution Spectroradiometer (MODIS). The data were resampled to a seasonal format and downscaled over grids of 10 by 10 to 150 by 150, and precipitation was determined to be the most important independent variable. Coefficient of determination ( ), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) were used to assess the quality of the ANN prediction model. The model peaked at winter seasons with = 0.984, RMSE = 0.027, and MAPE = 25.311, whereas it had the lowest performance in summer with = 0.920, RMSE = 0.057, and MAPE = 65.214. These results indicate that the ANN model can accurately predict the PM2.5 concentration and can be used to forecast future trends.

Based on the theory of turbulence, equations are derived for estimations of turbulent fluctuations of the longitudinal and transverse components of the wind velocity during ideal hovering of a quadcopter in a turbulent atmosphere. We present the results of experiments which were carried out on the territory of the Geophysical Observatory of Institute of Monitoring of Climatic and Ecological Systems, Siberian Branch, Russian Academy of Sciences, located in Tomsk Akademgorodok on the territory with complex orography, in a parkland zone with buildings of research institutes and motorways. Time series of turbulent fluctuations of the longitudinal and transverse components of wind velocity fluctuations were received with the use of an automated weather station, and time series of estimates of these components, from data of a DJI Phantom 4 Pro quadcopter during hovering. According to the automated weather station data, anisotropy was observed in one experiment during measurements in the atmosphere, but this phenomenon was not observed in the other experiment: the fluctuation spectra of all components of wind speed fluctuations coincide. The spectra of fluctuations of the longitudinal and transverse wind velocity components based on the automated weather station data and UAV telemetry are presented. The fluctuation spectra of these components for the automated weather station data and quadcopter generally coincide. The behavior of the spectra coincides with the spectrum which corresponds to Kolmogorov–Obukhov “–5/3” law within the inertial range. The turbulent spectra of the wind velocity fluctuations obtained with the use of the automatic weather station and with the unmanned aerial vehicle differ in the high-frequency spectral region.

Over the past decade, wireless communication technologies have developed significantly for intelligent applications in road transport. This paper provides an overview of telecommunications-based intelligent transport systems with a focus on ensuring system safety and resilience. In vehicle-to-everything, these problems are extremely acute due to the specifics of the operation of transport networks, which requires the use of special protection mechanisms. In this regard, it was decided to use blockchain as a system platform to support the needs of transport systems for secure information exchange. This paper describes the technological aspects of implementing blockchain technology in vehicle-to-network; the features of such technology are presented, as well as the features of their interaction.

Terrestrial laser scanning (TLS) provides detailed three-dimensional representation of the surrounding forest structure. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees and especially the upper parts of forest canopy is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point cloud is complemented with a photogrammetric point cloud acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data was considered feasible especially in characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (H_g) and mean stem volume (V_mean). Most notably the root mean square error (RMSE) in H_g improved from 0.88 m to 0.58 m and the bias improved from -0.75 m to -0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands the mere TLS captured also the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, V_mean, H_g and basal area-weighted mean diameter with a relative RMSE less than 5.5% for all of the sample plots. Although the multisensorial close-range sensing approach mainly enhanced characterization of forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries.

Unmanned Aerial Vehicle (UAV) based remote sensing (RS) studies in glaciology are mainly focusing on obtaining accurate high-resolution data from UAV images. Studies for identifying and minimising the challenges faced during the UAV-based RS data acquisition survey on inaccessible and harsh terrains of mountain glaciers is limited. This study aims to examine the practical challenges faced during UAV surveys of glaciers and derive strategies to minimize them. To the authors' knowledge, this is the first study that addresses such problems over the Himalayan region. Here, the UAV surveys were conducted using a fixed-wing commercial-grade off-the-shelf UAV (eBee plus, SenseFly) on three glacier sites (East Rathong, Hamtah and Panchinala-A) located in different zones and climate regimes lying within the Indian part of Himalayas. From UAV collected photos, the study was able to generate ultra-high-resolution ortho-mosaicked images and Digital Elevation Models (DEMs) at 0.1m GSD. UAV-derived DEMs was able to achieve vertical (horizontal) accuracy of 0.45 and 0.21 m (0.15 and 0.1 m) with 3 and 6 ground control points (GCPs) for an area of 0.75 km2 and 1.38 km2. Accuracy assessment of UAV DEMs generated with and without GCPs indicate that GCPs are must to obtain decimetre level accurate DEM especially on glaciers with steep-valleyed terrains. The utility of the obtained ultra-high-resolution ortho-mosaicked images was demonstrated by generating glacier surface feature maps. Based on the challenges observed during UAV surveys, the study identifies and recommends best-suited locations on a glacier and its adjacent regions for conducting UAV surveys efficiently in the glaciated terrain of Himalayas and possibly beyond. Recommendations reported in this article shall minimise the challenges faced and involved risks for data acquisition and thus enable UAVs to cover more glaciated area successfully.

UAVNs (unmanned aerial vehicle networks) may become vulnerable to threats and attacks due to their characteristic features such as high mobility, highly dynamic network topology, and open-air wireless environments. Since previous work has focused on classical and metaheuristic-based approaches, none of these approaches have a self-adaptive approach. In this article, we examine the challenges of cyber detection methods to secure UAVNs and review exiting security schemes proposed in the current literature. Furthermore, we propose an agent-based self-protective method (ASP-UAVN) for UAVNs that is based on the Human Immune System (HIS). In ASP-UAS, the safest route from the source UAV to the destination UAV is chosen according to a self-protective system. In this method, a multi-agent system using an Artificial Immune System (AIS) is employed to detect the attacking UAV and choose the safest route. In the proposed ASP-UAVN, the route request packet (RREQ) is initially transmitted from the source UAV to the destination UAV to detect the existing routes. Then, once the route reply packet (RREP) is received, a self-protective method using agents and the knowledge base is employed to choose the safest route and detect the attacking UAVs. The method is evaluated here via extensive simulations carried out in the NS-3 environment. The experimental results of four scenarios demonstrated that the ASP-UAS increases the Packet Delivery Rate (PDR) by more than 17.4, 20.8, and 25.91%, and detection rate by more than 17.2, 23.1, and 29.3%, and decreases the Packet Loss Rate (PLR) by more than 14.4, 16.8, and 20.21%, the false-positive and false-negative rate by more than 16.5, 25.3, and 31.21% those of SUAS-HIS, SFA and BRUIDS methods, respectively.

Polyvinyl alcohol-engineering cementitious composites (PVA-ECC) has been widely applied in bridge deck repairing or widening, the common practice for doing this is that a portion of a bridge is left open to traffic while the closed portion is constructed, which expose the early age PVA-ECC to the vehicle-induced vibrations. However, whether vehicle-induced vibrations affect the tensile performance of early age PVA-ECC remains unknow. The purpose of this study was to conduct laboratory test programs on how much vehicle-induced vibrations during early ages affected the tensile performance of PVA-ECC. A self-improved device was used to simulate the vehicle-induced vibrations, and after vibrating with the designed variables, both a uniaxial tensile test and a grey correlation analysis were performed. The results indicated that: the effects of vehicle-induced vibrations on the tensile performance of early age PVA-ECC were significant, and they generally tended to be negative in this investigation. In particular, for all of the vibrated PVA-ECC specimens, the most negative age when vibrated occurred during the period between the initial set and the final set. We concluded that although vehicle-induced vibrations during the setting periods had no substantial effects on the inherent strain-hardening characteristics of PVA-ECC, the effects should not be ignored.

A new variant of two-echelon routing problem is investigated, where the truck and the drone are used to cooperatively complete the deliveries of all parcels. The truck not only acts as a tool for parcel delivery, but also serves as a moving depot for the drone. The drone can carry several parcels and take off from the truck, while returning to the truck after completing the delivery. The energy consumption model for the routing process of the drone is analyzed, when it is utilized to deliver multiple parcels. A two-stage route-based modelling approach is proposed to optimize both the truck’s main route and the drone’s adjoint flying routes. A hybrid heuristic integrating nearest neighbor and cost saving strategies is developed to quickly construct a feasible solution. The simulated annealing algorithm is applied to improve the quality of the solution, where a Tabu list is employed to improve the search efficiency. Random instances at different scales are used to test the performance of the proposed algorithm. A case study based on the practical road network in Changsha, China, is presented, through which the sensitivity analysis is conducted with respect to some critical factors.

Precision agriculture is a farm management technology that involves sensing and then responding to the observed variability in the field. Remote sensing is one of the tools of precision agriculture. The emergence of small unmanned aerial vehicles (sUAV) have paved the way to accessible remote sensing tools for farmers. This paper describes the comparison of two popular off-the-shelf sUAVs: 3DR Iris and DJI Phantom 2. Both units are equipped with a camera gimbal attached with a GoPro camera. The comparison of the two sUAV involves a hovering test and a rectilinear motion test. In the hovering test, the sUAV was allowed to hover over a known object and images were taken every second for two minutes. The position of the object in the images was measured and this was used to assess the stability of the sUAV while hovering. In the rectilinear test, the sUAV was allowed to follow a straight path and images of a lined track were acquired. The lines on the images were then measured on how accurate the sUAV followed the path. Results showed that both sUAV performed well in both the hovering test and the rectilinear motion test. This demonstrates that both sUAVs can be used for agricultural monitoring.

This work targets the development of a mobility control system for high-speed series hybrid electric tracked vehicles, which operate with independent traction motors for each track. The scope of the research encompasses the modeling of the series hybrid powertrain specific to military tracked vehicles, as well as an in-depth analysis of their dynamic behavior. Subsequently, the study conducts a critical review of the mobility control approaches sourced from literature, identifying key techniques relevant to high-inertia vehicular applications. Building on the foundational models, the study puts forward a robust closed-loop mobility control system aimed at ensuring precise and stable off-road vehicle operations. The system's resilience and adaptability to a variety of driving conditions are emphasized, with a particular focus on handling maneuvers such as steering and pivoting, which are challenging operations for tracked vehicle agility. The performance of the proposed mobility control system is tested through a series of simulations, covering a spectrum of operational scenarios. These tests are carried out in both offline simulation settings, which permit meticulous fine-tuning of system parameters, and real-time environments that replicate actual field conditions. The simulation results demonstrate the system's capacity to improve vehicular response and highlight its potential impact on the future designs of mobility control systems for the heavy-duty vehicle sector, particularly in defense applications.

The development of accurate dynamic battery pack models for electric vehicles (EVs) is critical for the ongoing electrification of the global automotive vehicle fleet, since these serve as a key element in the energy performance of an EV powertrain system. The Equivalent Circuit Model (ECM) technique at the cell level is commonly employed for this purpose, combining accuracy and efficiency in the representation of battery operation within the broader powertrain system. In this study, a second-order ECM model of a battery cell is being developed using Modelica, an equation-based modeling language. Parameter lookup tables at multiple levels of state of charge (SoC), extracted from Li-ion battery cells with 4 different commonly used cathode materials, have been utilized. This approach allows for a representation of the battery systems that are used in a wide range of commercial EV applications. To verify the model, an integrated EV model is developed and simulation results of the FTP75 driving cycle have been compared against an equivalent application in MATLAB Simulink. The findings demonstrate a close match between the results obtained from both software across different system points. Specifically, the maximum deviation of SoC is limited to 0.08% and the maximum value of relative voltage deviation is 1.4%. The verified model enables the exploration of multiple possible architecture configurations of EV powertrains with Modelica.

Due to the rapid development of unmanned aerial vehicles (UAVs) communication technology, UAVs are gradually competing with primary users (PUs) for spectrum resources. Cognitive radio (CR) is a promising solution to meet the needs. Cooperative spectrum sensing (CSS) is considered as an effective method to detect the PU signal and identify available spectrum resources for UAVs in a cognitive UAV network (CUAVN). However, the cooperative mode among multiple UAVs may incur a high overhead, resulting in performance degradation. Therefore, we introduce a differential sequential 1 (DS1), which incorporates a differential mechanism and leverages the sequential idea based on the classical voting rule to enhance cooperative performance and efficiency. In view of this, we formulate three scenarios to characterize the PU activity and introduce a multi-slot cooperative mode within a single UAV to realize cooperative gain. Further, only the information change about the PU status is sequentially calculated in DS1, and combined with a sequential idea, the efficiency of the voting rule is greatly improved. Moreover, the application of support vector machines (SVM) in dynamic selection enables the selection of the most suitable voting rule based on diverse sensing parameters. This dynamic selection process ensures optimal performance and efficiency by adapting the voting rule to the specific characteristics of the given scenario. Finally, simulation results demonstrate that the superiority of our proposal with respect to the detection performance, sample size and the energy efficiency is evident, which proves the high performance of the proposed policy.

The meticulously engineered powertrain mounting system of hybrid-electric vehicles plays a critical role in minimizing vehicle vibrations and noise, thereby enhancing the longevity of vital powertrain components. However, developing and designing such a system demands substantial time and financial investments due to intricate analysis and modeling requirements. To tackle this challenge, this study integrates data-mining technology into the design and optimization processes of the powertrain mount system. The research focuses on the powertrain mounting system of a transverse four-cylinder hybrid-electric vehicle, employing the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology to establish a data-mining prediction model for mounting stiffness. This model utilizes three data mining algorithms—Multi-SVR, MRTs, and MLPR—assessing their predictive accuracy concerning mounting system stiffness estimation. A comparative analysis reveals that the MRTs algorithm outperforms others as the most effective prediction model. The proposed predictive model elucidates the quantifiable correlation between vibration isolation performance and installation stiffness, overcoming complexities associated with traditional modeling approaches. Applying this model in powertrain mounting system design showcases the efficacy of the CRISP-DM-based approach, significantly enhancing design efficiency without compromising prediction accuracy.

An event-based dynamic output feedback control (DOFC) strategy for unmanned marine vehicle (UMV) systems is concerned. The whole UMV systems are composed of an UMV closed-loop system, a land-based control unit and the communication network. To increase the effectiveness of data transmission in the network channel and better enable the control unit against attack, an adaptive event-triggered mechanism (AETM) is applied. Moreover, a quantizer is installed between the sampler and the control unit. The quantizer further reduces the communication burden. The occurrence of aperiodic denial-of-service (DoS) attack is considered in the channel from the control unit to the UMV system. Sufficient criterion for ensuring global exponential stability of system with an expected H∞ disturbance attenuation index is obtained. Co-design of the dynamic output feedback controller and the AETM is derived. The effectiveness of the proposed approach is verified in the given illustrative simulation. The simulation results indicate that the reduction percentages of yaw angle amplitudes and yaw velocity accumulative error of the UMV system with the control strategy proposed in this paper are 43.2%and 45.9%, respectively, which are 0.3% and 5.8% improvement in both metrics compared to the previously published work.

This research focuses on the development of an automated medication delivery system using Unmanned Aerial Vehicle (UAV) Technology and the Internet of Things (IoT) to support telemedicine and community welfare in the KLU region. The system is designed to improve the quality of medical services and medication management for the community in the area. One of the main challenges faced is how the KLU community responds and accepts automatic medication delivery using UAVs and IoT. To overcome this challenge, this research suggests providing education and training to the community about the benefits and usage of the system, as well as displaying a working simulation of the system to the community.

The utilization of fuel cells (FC) in automotive technology has experienced significant growth in recent years. Fuel cell hybrid electric vehicles (FCHEVs) are powered by a combination of fuel cells, batteries, or/and ultracapacitors (UCs). By integrating power converters with these power sources, the FCHEV system can overcome the limitations of using them separately. The performance of an FCHEV is influenced by the efficiency of the power electronics converter controller, as well as the technical efficiency of the power sources. FCHEVs need intricate energy management systems (EMSs) to function effectively. Poor EMS can lead to low efficiency and accelerated fuel cell and battery degradation. The literature discussed various types of EMSs, such as equivalent consumption minimization strategy, classical PI controller, fuzzy logic controller, and Mutative Fuzzy Logic Controller (MFLC). However, comparing different EMSs can be challenging due to the varying vehicle and system parameters, which might lead to false claims being made regarding system performance. This research aims to categorize and discuss the various topologies of FCHEVs, highlighting their pros and cons, and comparing several EMSs based on performance metrics such as State of Charge (SOC) and FC deterioration.

A friction loss model for brake testing platform is constructed to revise the braking force test results, which can largely improve the accuracy of automotive brake performance testing. Specifically, the influence of tire pressure, vehicle axle load, placement angle and vehicle speed on the friction loss is tested and analyzed on a wheelbase adjustable automotive anti-force brake testing platform. The friction loss model for the platform is constructed based on the defined relative slip and placement angle, which is validated through actual tests on four types of vehicles. The results showed that the maximum relative error between the values of the model calculated simulation and the measured was 2.2%, indicating the friction loss model is valid and can provide a basis for revising the braking force of automotive brake. Meanwhile, the maximum braking force increases from 3982 N to 4772 N, increased by 19.8%, when the placement angle increased from 36.9° to 47.0°. Accordingly, in order to improve the accuracy of detection results of the automotive anti-force rolling brake testing platform, the friction loss caused by the change in the placement angle should be revised, and then braking rate of the vehicle should be judged according to GB 7258-2017 and GB 38900-2020.

Battery-powered electric vehicles (EVs) have emerged as an environmentally friendly and efficient alternative to traditional internal combustion engine vehicles, while their single-charge driving distances under cold conditions are significantly limited due to the high energy consumption of heating systems. Heat pumps can provide an effective heating solution for EVs, but their coefficient of performance (COP) is hampered by heat transfer deterioration due to frost accumulation. This study proposes a solution to this issue by introducing a microchannel heat exchanger (MHE) with superhydrophobic surface treatment (SHST) as a heat pump evaporator. A computational-fluid-dynamics MHE model and a dynamic heat pump model are developed and rigorously validated to examine the detrimental impact of frost accumulation on heat transfer, airflow resistance, and heat pump performance. When the frost layer thickness is 0.8 mm at a given air-side velocity of 1.0 m/s, the air-side heat transfer coefficient can be reduced by about 75%, and the air-side pressure drop sharply increases by 28.4 times. As frost thickness increases from 0 to 0.8 mm, the heating effect drops from 3.97 to 1.82 kW, and the system COP declines from 3.17 to 2.30. Experimental results show that the frost thickness of the MHE with SHST reaches approximately 0.4 mm after 30 minutes, compared to that of 0.8 mm of the MHE without SHST, illustrating the defrosting capability of the superhydrophobic coating. The study concludes by comparing the performance of various heating methods in EVs to highlight the advantages of SHST technology. As compared to traditional heat pumps, the power consumption of the proposed system is reduced by 48.7% due to the defrosting capability of the SHST. Moreover, the single-charge driving distance is extended to 327.27 km, an improvement of 8.99% over the heat pump without SHST.

Single and multi-vehicle crashes are a significant issue that causes economic and social costs and has therefore gained attention. This study explored the factors associated with injury severity for both single- and multi-vehicle crashes using over 550,000 crash data points in Japan between 2019 and 2021. We identified the determinants of road infrastructure and traffic control while controlling for driver, vehicle, environmental, and accident characteristics by applying ordered logit and bias-reduced binomial regression models. Our findings are as follows. 1) Traffic control variables did not affect single-vehicle crashes. 2) Guardrails had a higher severity in both single-vehicle crashes and multi-vehicle crashes at intersections. 3) The impact of the centerline differed between intersections and non-intersections for multi-vehicle crashes.  These results of this study provide transportation agencies with important guidance as to the road infrastructure and transport control.

To validate the accuracy and reliability of onboard sensors for object detection and localization in driver assistance, as well as autonomous driving applications under realistic conditions (indoors and outdoors), a novel tracking system is presented. This tracking system is developed to determine the position and orientation of a slow-moving vehicle (e.g. car during parking maneuvers), independent of the onboard sensors, during test maneuvers within a reference environment. One requirement is a 6 degree of freedom (DoF) pose with a position uncertainty below 5 mm (3σ), an orientation uncertainty below 0.3° (3σ) at a frequency higher than 20 Hz, and a latency smaller than 500 ms. To compare the results from the reference system with the vehicle’s onboard system, a synchronization via Precision Time Protocol (PTP) and a system interoperability to Robot Operating System (ROS) is implemented. The developed system combines motion capture cameras mounted in a 360° panorama view set-up on the vehicle with robotic total stations. A point cloud of the test site serves as a digital twin of the environment, in which the movement of the vehicle is simulated. Results have shown that the fused measurements of these sensors complement each other, so that the accuracy requirements for the 6 DoF pose can be met, while allowing a flexible installation in different environments.

Private vehicle travel is the most basic mode of transportation, and the effective control of the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic development as well as achieving a green low-carbon society. Therefore, the impact factors of individual carbon emission must be elucidated. This study builds five different models to estimate real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the Light Gradient Boosting Machine (LightGBM) model performs better than the linear regression, Naïve Bayes regression, Neural Network regression, and Decision Tree regression models, with mean absolute error of 0.911 L/100 km, mean absolute percentage error of 10.4%, mean square error of 1.536, and R squared (R2) of 0.642. This study also assesses a large number of factors, from which three most important factors are extracted, namely, reference fuel consumption rate value, engine power and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with greater impact on real-world fuel consumption rate are vehicle brand, engine power, and engine displacement. Average air pressure, average temperature, and sunshine time are the three most important climate factors.

Due to the renewed variation in government and political systems inside and outside countries, and with the high tariffs at borders, the latter have become an outlet for terrorism and smugglers. Therefore, each country seeks to develop its own protection system, and the technologies used in these systems vary according to the severity and the importance of the installations to be protected, it is found that some of them are expensive and unnecessary, but other have good and variable levels of efficiency. Consequently, the idea of designing a surveillance system that can monitor and control access becomes indispensable. In the same context, this work is of crucial strategic and geopolitical importance. It combines pre-existing alarm and monitoring methods and revolutionary Internet of Things (IoT) application products, of which Wireless Sensor Networks (WSN) and Optical Fiber Sensors (OFS) are part of this application. This article presents the distribution of wireless radar nodes accompanying with a Bragg fiber sensor to identify each rolling intruder incoming the zone to be monitored, from the determination of its speed, weight and wheelbase distance.

This article describes techniques employed in the production of a synthetic dataset of driver telematics emulated from a similar real insurance dataset. The synthetic dataset generated has 100,000 policies that included observations about driver’s claims experience together with associated classical risk variables and telematics-related variables. This work is aimed to produce a resource that can be used to advance models to assess risks for usage-based insurance. It follows a three-stage process using machine learning algorithms. The first stage is simulating values for the number of claims as multiple binary classifications applying feedforward neural networks. The second stage is simulating values for aggregated amount of claims as regression using feedforward neural networks, with number of claims included in the set of feature variables. In the final stage, a synthetic portfolio of the space of feature variables is generated applying an extended SMOTE algorithm. The resulting dataset is evaluated by comparing the synthetic and real datasets when Poisson and gamma regression models are fitted to the respective data. Other visualization and data summarization produce remarkable similar statistics between the two datasets. We hope that researchers interested in obtaining telematics datasets to calibrate models or learning algorithms will find our work valuable.

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stages of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

A lot of previous research have proposed various frameworks and algorithms to optimize routes to reduce the total transportation cost, which accounts for over 70% of overall logistics cost. However, it is very hard to find the cases applied the mathematical models or algorithms to the practical business environment cases, especially daily operating logistics services like convenient stores. Most of previous research have considered the developing an optimal algorithm which can solve the mathematical problem within the practical time while satisfying all constraints such as the capacity of delivery and pick-up, and time windows. For the daily pick-up and delivery service like supporting several convenient stores, it is required to consider the unit transporting container as well as the demand, capacity of trucks, traveling distance and traffic congestion. Especially, the reusable transporting container, trays, should be regarded as the important asset of logistics center. However, if the mathematical model focuses on only satisfying constraints related delivery and not considering the cost of trays, it is often to leave the empty trays on the pick-up points when there is not enough space in the track. In this research, it has been proposed to build the mathematical model for optimizing pick-up and delivery plans by extending the general vehicle routing problem of simultaneous delivery and pickup with time windows while considering left-over cost. With the numerical experiments, it has been proved that the proposed model may reduce the total delivery cost. It may be possible to apply the proposed approach to the various logistics business which uses the reusable transporting container like shipping containers, refrigerating containers, trays, and pallets.

Aiming at the problem of model error and tracking dependence in the process of intelligent vehicle motion planning, an intelligent vehicle model transfer trajectory planning method based on deep reinforcement learning is proposed, which obtain an effective control action sequence directly. Firstly, an abstract model of the real environment is extracted. On this basis, Deep Deterministic Policy Gradient (DDPG) and vehicle dynamic model are adopted to jointly train a reinforcement learning model, and to decide the optimal intelligent driving maneuver. Secondly, the actual scene is transferred to equivalent virtual abstract scene by transfer model, furthermore, the control action and trajectory sequences are calculated according to trained deep reinforcement learning model. Thirdly, the optimal trajectory sequence is selected according to evaluation function in the real environment. Finally, the results demonstrate that the proposed method can deal with the problem of intelligent vehicle trajectory planning for continuous input and continuous output. The model transfer method improves the model generalization performance. Compared with the traditional trajectory planning, the proposed method output continuous rotation angle control sequence, meanwhile, the lateral control error is also reduced.

Wetlands benefits can be summarized but are not limited to their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Over the past few decades, remote sensing and geographical information technologies has proven to be a useful and frequent applications in monitoring and mapping wetlands. Combining both optical and microwave satellite data can give significant information about the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing data from different sensors, such as radar and optical remote sensing data, can increase the wetland classification accuracy. In this paper we investigate the ability of fusion two fine spatial resolution satellite data, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, for mapping wetlands. As a study area in this paper, Balikdami wetland located in the Anatolian part of Turkey has been selected. Both Sentinel-1 and Sentinel-2 images require pre-processing before their use. After the pre-processing, several vegetation indices calculated from the Sentinel-2 bands were included in the data set. Furthermore, an object-based classification was performed. For the accuracy assessment of the obtained results, number of random points were added over the study area. In addition, the results were compared with data from Unmanned Aerial Vehicle collected on the same data of the overpass of the Sentinel-2, and three days before the overpass of Sentinel-1 satellite. The accuracy assessment showed that the results significant and satisfying in the wetland classification using both multispectral and microwave data. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, with an overall classification accuracy of approximately 90% in the object-based classification. Compared with the high resolution UAV data, the classification results give promising results for mapping and monitoring not just wetlands, but also the sub-classes of the study area. For future research, multi-temporal image use and terrain data collection are recommended.

This paper proposes an electric vehicle routing problem with considering states of charging stations and suggests solution strategies. Charging of electric vehicles is a main issue in the field of electric vehicle routing problem. There are many studies to find locations of charging stations, recharging functions for batteries of vehicles, and so on. However, states of charging stations significantly affect the routes of electric vehicles, which is not much explored. The states may include open or close of charging stations, occupied or empty of charging slots, and so on. This paper investigates how the states of charging stations are estimated and routing strategies are determined. We formulate a mixed integer programming model and suggest how to solve the problem with exact method. Numerical examples provide the optimal routing strategies of electric vehicles in the changing environments of the states of charging stations.

The thermal degradation of brakes has a significant impact on the stability of pure electric commercial vehicles during long downhill braking. Based on road slope and using fuzzy control method, design strategies for motor regenerative braking force and friction braking force distribution, with the aim of reducing friction braking force, improving braking stability, and recovering braking energy. By establishing driving conditions on roads with different slopes and using numerical analysis methods to verify the proposed control strategy. the results show that under the 6% constant slope driving condition, the vehicle maintains a constant speed of 30km/h downhill driving, and the braking energy recovery rate reaches 50.93%; Under the condition of 3% -6% gradient driving, the vehicle speed fluctuates slightly, but it can still be maintained at a constant speed of 30km/h for downhill driving through the adjustment of the motor's reverse drag characteristic, with a braking energy recovery rate of 49.97%; During the process of driving downhill at a constant speed, the frictional braking force does not participate in braking; The braking energy recovery rate is determined by the slope and braking deceleration.

This work focuses on addressing the dynamic positioning and trajectory tracking problem for a 4 degree-of-freedom (DOF) unmanned underwater vehicle (UUV) in the presence of nonlinear dynamics, parametric uncertainties, system constraints, and time-varying external disturbances. To tackle this problem, a disturbance observer-based control (DOBC) scheme is proposed. The scheme is structured around the model predictive control (MPC) method integrated with an extended active observer (EAOB). Compared to the conventional disturbance observer, the EAOB has the unique ability to handle both external disturbances and system/measurement noises simultaneously. The EAOB leverages a combination of sensor measurements and a system dynamic model to estimate disturbances in real-time, which allows continuous estimation and compensation of time-varying disturbances back to the controller. The proposed disturbance observer-based MPC (DOBMPC) is implemented by feeding the estimated disturbances back into the MPC’s prediction model, which forms a robust adaptive controller with a parameter-varying model. The proposed control strategy is validated through simulations in a Gazebo and Robot Operating System (ROS) environment. The results show that it can effectively reject unpredictable disturbances and improve the UUV’s control performance.

Beginning with navigation system design, this paper presents a comprehensive strategy for enhancing the search and rescue capabilities of agile mobile robots. Towards this, the autonomous ground vehicle (AGV) utilizes surface classification to determine and prioritize the terrain it is traversing. Our developed system design incorporates real-time terrain data with task objectives at a high level, ensuring that the robot can effectively navigate complex and ever-changing environments. This design, in conjunction with the introduction of a novel lightweight surface classification model, forms the basis of our adaptive terrain perception and decision-making systems, enabling robots such as Jackal to adapt rapidly and make the decisions necessary to complete the task. Subsequently, we exhaustively validated these systems through a series of extensive experiments in a variety of terrains, including normal and mixed terrains, demonstrating their robustness and efficacy in real-life situations.

Transportation electrification has been identified as a crucial shift to be made for achieving energy security and emission reduction targets. Electric Vehicles (EVs) play a significant role in achieving these targets; therefore, there is a growing trend to develop new EV technologies and associated infrastructure. Along with these developments, EVs' Quality of Service (QoS) aspects should be enhanced to increase the number of EVs on the road. Various QoS factors are defined to assess the QoS of EVs, and one of the two aims of this paper is to provide a comprehensive review of these factors. The other aim is to review recent developments of associated communication technologies which are essential for exchanging information between EVs and charging stations. This paper serves as a comprehensive review and useful reference for researchers working on QoS aspects of EVs and associated communication systems aiming to enhance the QoS.

The unsupervised domain-adaptive vehicle reidentification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain, however there are knowledge differences between the target domain and the source domain. To reduce domain differences, existing unsupervised domain-adaptive rerecognition methods generally require access to the source domain data to assist in re-training the target domain model. But for security reasons, in many cases, data between different domains cannot communicate. To this end, this paper proposes an unsupervised domain-adaptive vehicle re-identification method based on source-free knowledge transfer. First, by constructing a passive domain knowledge migration module, the target domain is consistent with the source domain model output to train a generator to generate the "source-like samples". Then, it can effectively reduce the model knowledge difference and improve the model generalization performance. According to the experiment and testing of VeRi776 and VehicleID, two mainstream public data sets in this field, the rank-k and mAP indexes obtained are both improved, and are suitable for object re-recognition tasks without source data.

In order to solve the problems of complicated steering control of unmanned vehicles in the field and difficult steering on complex roads, we designed a wheel-track composite vehicle equipped with a novel power differential steering mechanism with dual driving, which drove the steering of the vehicle through the differential rotation of the rear two wheels. The unmanned vehicle was simple to control, small in size, and able to work under the conditions of complex roads, such as hills, mountains, and muddy land. Firstly, a steering mechanism with both differential speed and force was designed to prevent the vehicle from skidding into muddy land and stopping motionless. Secondly, the kinematics model and dynamics model of the two drive shafts and the two output shafts (wheel shafts) were established. Thirdly, according to the relationship between the rotational speed of the two output shafts and the steering radius of the vehicle, the kinematic model of the rotational speed of the two input shafts and the steering radius of the wheel-track composite vehicle was obtained. Finally, according to the test data, the mathematical model of rotational speeds of the two input shafts and the actual steering radius of the vehicle was obtained by neural network fitting, and the maximum relative error between the model results and the actual steering radius value was 3.53%. The combination of power differential steering mechanism and wheel-track composite unmanned vehicle increased the adhesion with the ground and could better adapt to the complex road environment. In conclusion, the unmanned vehicle had the advantages of continuous radius steering, deceleration and torsion increase, differential lock, etc. It was suitable for all-terrain military and civilian vehicles and various special equipment mobile platforms of the walking device, and the research results could provide theoretical references for the steering control and structural optimization of the wheel-track composite vehicle under the environment of complex road surface.

Existence of hybrid electric vehicles nowadays is one of the emerging technologies worldwide. Different countries are using this technology on their transportation, and nowadays, a lot of other countries are gradually adapting on using this kind of innovation in their transport system. Philippines is among these countries which aims to have a sustainable mobility by embracing the use of hybrid - electric vehicles but still lots of consumers are lacking awareness on this technology. Lack of information on the social, environmental, and economic potential benefits are among the main issues of Philippines consumers that really affect its uptake. The goal of this study was to evaluate the consumer’s perception and purchase intention on the transition of hybrid - electric vehicles for sustainable mobility and transportation here in the Philippines. It aimed to assess consumer awareness with hybrid - electric vehicles and its potential benefits to human beings in terms of social, environmental, and economic aspects. The study also gauged the impact of this perception on consumer’s purchase intention on hybrid - electric vehicles. These objectives were attained using the Descriptive Statistics and Correlation Analysis. Using these methods, the results revealed that majority of the consumers believed on the positive benefits of transitioning to hybrid - electric vehicles in terms of social, environmental, and economic aspects. This study also showed that these benefits have high impact on the consumer’s intention of buying or having a hybrid – electric vehicle. Electric vehicle is one of the most effective measure of decarbonizing the transport system. Furthermore, government and automotive businesses must strengthen their drive in spreading awareness of hybrid – electric vehicle’s benefits to consumers to further support country’s effort for development.

This article presents a state-of-the-art literature review to understand people’s perceptions and opinions of Autonomous Vehicles (AVs) and the factors that influence AV adoption. A strategic literature search was conducted to select articles for this review. Most of the articles were published within the last five years and they used a household questionnaire survey to collect data. Mostly, they used statistical and econometric methods to evaluate the factors that affect people’s intentions to adopt AVs. The results show that psychological factors often appear as the most important internal factors of people’s willingness to adopt AVs. Additionally, other internal factors such as the socioeconomic profile of individuals and their household, and knowledge and familiarity with AV technologies would affect AV adoption tendencies. User attributes also indirectly affect AV adoption by influencing the psychological factors of users. We identify several critical external factors such as opportunities (e.g., safety and security, low congestion, energy use) and challenges (e.g., system failure, privacy breach, and legal issues), while another influential group includes transportation factors (e.g., travel mode, distance, and time), urban form (e.g., urban/rural, density, land use diversity), affinity to new technology, and the institutional regulatory environment. We discuss some recommendations for policy makers, auto industries, and private stakeholders to formulate policies and strategies to increase market share of AVs. Finally, we identify some limitations of previous studies and provide a blueprint for future research on AV adoption.

The vehicles number continuously growing lead to increasingly intense and congested traffic and it will additionally demand the braking system, and drivers behave more aggressively and as result is required that the braking system to be durable and efficient. For this is necessary the study the braking system behavior in conditions of intense and moderate traffic to increase the safety of traffic participants, respectively to demonstrate the need for more frequent replacement of some braking system elements. Thus, on a vehicle were performed a series of successive tests, through which the degree of wear of the brake pads and discs was monitored periodically and as a result the efficiency evolution of the braking system. The tests were carried out both in laboratory (on dynamometer) and in traffic to establish the efficiency of the braking system according to some parameters considered essential. The experimental tests showed that the recommendations regarding the frequency of replacement of brake pads and disks are inconsistent with their actual wear. Therefore, the aim of this paper is the establishment of the braking system efficiency of an auto-vehicle, subject to testing depending on the auto-vehicle mass, travel speed, distance driven, and braking time, based on experimental on stand and in-traffic tests, according the road safety regulations.

The goal of this paper is to investigate the aerodynamic and aerothermodynamic behaviour of the Schiaparelli capsule after the deployment of the supersonic disk-gap-band (DGB) parachute during its re-entry phase into the Martian atmosphere. Large-Eddy Simulations of compressible flows have been performed over the capsule and the flexible DGB parachute to analyse the behaviour of the turbulent wake behind the capsule and its interaction with the parachute flow-field. The simulations are performed at an altitude of 10 km and a Mach number around 2, i.e., a regime in which large canopy-area oscillations are observed. These simulations have shown a strong interaction between the bow shock, the recompression and expansion waves, high pressure, density and temperature gradients, heat flux towards the airstream and the body implying turbulence generation, ingestion, and amplification through the shock waves. This nonlinear turbulence flowfield explains the non-axisymmetric behaviour around and behind the parachute that caused the uncontrolled capsule oscillation and the mission failure.

The connectivity level of last-generation vehicles is constantly on the rise. The combined use of Vehicle-To-Everything (V2X) connectivity and autonomous driving can provide remarkable benefits through the optimization of the route and speed trajectory. In this framework, this paper focuses on vehicle eco-driving optimization in a connected environment. The virtual test rig of a premium segment passenger car was used for generating the simulation scenarios. The benefits, in terms of energy and time savings, that the introduction of V2X communication, integrated with cloud computing, can have in a real-world scenario were assessed. The Reference Scenario is a pre-defined Real Driving Emissions (RDE) compliant route, while the simulation scenarios were generated by assuming two different penetration levels of V2X technologies. The associated energy minimization problem is formulated and solved by means of a global optimization algorithm, i.e., Dynamic Programming (DP). The optimization framework includes information coming from the surrounding environment, e.g., traffic lights state, speed limits, distance to travel, etc. The simulations show that introducing a smart infrastructure along with optimizing the vehicle speed in a real-world route can potentially reduce the required energy by 54% while shortening the travel time by 38%. Finally, a sensitivity analysis is performed on the bi-objective optimization cost function to find a set of Pareto optimal solutions, between energy and travel time minimization.

With the current technological transformation in the automotive industry, autonomous vehicles are getting closer to the Society of Automative Engineers (SAE) automation level 5. This level corresponds to the full vehicle automation, where the driving system autonomously monitors and navigates the environment. With SAE-level 5, the concept of a Shared Autonomous Vehicle (SAV) will soon become a reality and mainstream. The main purpose of an SAV is to allow unrelated passengers to share an autonomous vehicle without a driver/moderator inside the shared space. However, to ensure their safety and well-being until they reach their final destination, it is required an active monitoring of all passengers. In this context, this article presents a microphone-based sensor system that is able to localize sound events inside an SAV. The solution is composed of a Micro-Electro-Mechanical System (MEMS) microphone array with a circular geometry connected to an embedded processing platform that resorts to Field-Programmable Gate Array (FPGA) technology to successfully process in hardware the sound localization algorithms.

This paper presents a study on the influence of the frequency variation of a nonlinear1 control allocation technique execution, developed by the author [1], named by Fast Control2 Allocation (FCA) for the Quadrotor Tilt-Rotor (QTR) aircraft. Then, through Software In The3 Loop (SITL) simulation, the proposed work considers the use of Gazebo, QGroundControl, and4 Matlab applications, where different frequencies of the FCA can be implemented separated in5 Matlab, always analyzing the QTR stability conditions from the virtual environment performed in6 Gazebo. TheresultsshowedthattheFCAneedsatleast200HzoffrequencyfortheQTRsafeflight7 conditions, i. e., 2 times smaller than the main control loop frequency, 400 Hz. Lower frequencies8 than this one would case instability or crashes during the QTR Operation.

Physical distancing and wearing a face mask are key interventions to prevent COVID-19. While this remains difficult to practice for millions of firefighters in fire engines responding to emergencies, the delayed forthcoming of evidence on the physical effectiveness of such safety interventions in this setting presents a major problem. In this field experimental study, we provided initial evidence to close this gap. We examined total aerosol burden in the cabin of a fire engine whilst manipulating crew size, natural ventilation, use of FFP2 respirators and use of SCBA full-face masks during 15-minute driving periods. At the same time, we controlled for crew activity and speaking, vehicle speed, cabin air temperature, pressure and humidity. Limiting the crew size, using FFP2 respirators and not donning SCBA full-face masks was associated with a reduction of the arithmetic mean of total aerosol burden of up to 49%. Natural ventilation as tested in this study was associated with both an increase and a decrease of total aerosol burden. This study provided initial evidence on the physical effectiveness of safety interventions in fire engines to reduce potential airborne transmission of SARS-CoV-2 through aerosols. More research about the physical and clinical effectiveness of such safety interventions is needed.

In recent years, weeds is responsible for most of the agricultural yield losses. To deal with this threat, farmers resort to spraying pesticides throughout the field. Such method not only requires huge quantities of herbicides but impact environment and humans health. One way to reduce the cost and environmental impact is to allocate the right doses of herbicide at the right place and at the right time (Precision Agriculture). Nowadays, Unmanned Aerial Vehicle (UAV) is becoming an interesting acquisition system for weeds localization and management due to its ability to obtain the images of the entire agricultural field with a very high spatial resolution and at low cost. Despite the important advances in UAV acquisition systems, automatic weeds detection remains a challenging problem because of its strong similarity with the crops. Recently Deep Learning approach has shown impressive results in different complex classification problem. However, this approach needs a certain amount of training data but, creating large agricultural datasets with pixel-level annotations by expert is an extremely time consuming task. In this paper, we propose a novel fully automatic learning method using Convolutional Neuronal Networks (CNNs) with unsupervised training dataset collection for weeds detection from UAV images. The proposed method consists in three main phases. First we automatically detect the crop lines and using them to identify the interline weeds. In the second phase, interline weeds are used to constitute the training dataset. Finally, we performed CNNs on this dataset to build a model able to detect the crop and weeds in the images. The results obtained are comparable to the traditional supervised training data labeling. The accuracy gaps are 1.5% in the spinach field and 6% in the bean field.

Unmanned Underwater Vehicles (UUVs) are pivotal in ocean exploration, research, and various industrial activities such as marine mining and offshore engineering. However, traditional methods of navigating these vehicles face significant challenges, mainly due to the inefficacy of electromagnetic waves in water, leading to signal loss. To address these limitations, researchers are increasingly turned learning, an artificial intelligence technique capable of learning from data-enhancing underwater navigation, mainly through visual Simultaneous Localization and Mapping (SLAM). This paper explores integrating deep learning methodologies and sensor technologies to revolutionize underwater navigation for UUVs. Proprioceptive sensors, along with exteroceptive sensors, are crucial in accurately measuring and comprehending the underwater environment. Additionally, the paper provides detailed insights into the processes of underwater SLAM, camera-based underwater positioning systems, sonar systems for underwater navigation, and the utilization of Lidar in underwater navigation. Furthermore, it delves into applying deep learning techniques in underwater SLAM, offering a comprehensive understanding of the innovative processes driving advancements in underwater vehicle navigation. By leveraging these advancements, this research aims to improve underwater navigation systems' precision, reliability, and adaptability, thereby unlocking new frontiers in ocean exploration and industrial applications for UUVs.

Amid the swift progression of the global automobile industry, the aspiration for lightweight vehicle design has surfaced as a crucial approach to boost fuel efficiency and curtail environmental impact. The objective of this study revolves around achieving holistic optimization of vehicle structure via detailed scrutiny of vehicle process characteristics, amalgamated with a Back Propagation neural network (BPNN) optimized by a genetic algorithm (GA) and a topology optimization (TO) method.The process initiates with an examination of the vehicle's technical characteristics, succeeded by an optimization protocol that couples GA with BPNN. This exhaustive optimization procedure derives the best-fit values of design parameters and identifies the globally optimal lightweight design approach. Subsequently, a comprehensive structural optimization is actualized by modifying the topological distribution of the structure.A simulation experiment of the conceptual car body beam skeleton is conducted as a conclusive step, and the anti-collision capacity of the car body beam skeleton is assessed by measuring deformation intrusion. The findings reveal that in the vehicle topology optimization, a comparative analysis of pre and post-optimization results indicates an improved topological density across diverse sections. The topological density of the vehicle's frontal structure escalates from 0.75 to 0.8. Following optimization, the stiffness assessment records a marginal increase (within the range of 0.02–0.05), implying that the lightweight design does not compromise the general rigidity of the structure.The optimized body structure manifests superior crashworthiness in the crash test: the deformation intrusion diminishes by 27% in front-end collisions. In side impacts, the deformation and intrusion at critical points are reduced by 13.15% to 10% respectively. The comprehensive method proposed herein proves to be an efficient strategy to actualize the lightweight design of vehicles.

Bandung and Lembang are the cities that are chosen by many tourists (local and foreign) as their destinations. Geographically, these cities are placed side-by-side and each city has its own characteristics. As in Bandung, there are many hotels and culinary spots, meanwhile, in Lembang, there are many scenery tourism spots. Tourists usually have limited time to visit all the destinations on holiday, which makes them choose several tourist destinations to visit. In this paper, the tourism itinerary recommendation system is proposed by calculating the most optimal route between destinations and approaching the Vehicle Routing Problem with Time Windows. The optimal route is defined by using Dijkstra the shortest path algorithm by using the collaboration between the road information (road length, traffic condition, travel time, and weather condition) and criteria weight that is determined using the Analytics Hierarchy Process as the graph weights. According to the simulation results, the criteria weights are 6.9%, 62.7%, 18.6%, and 11.9% for Route Length, Traffic Condition, Travel Time, and Weather Condition respectively. Moreover, the optimal number of tourism itinerary plans for the tourist in this research is 4 destinations. As the usage of computational resources, it takes 31.8% of CPU and 61.9% of Memory usage. For the time processing, it increases exponentially as the increment of number of requested stops. The output of this research is expected to be a solution to the tourist itinerary plan, especially in Bandung and Lembang.

Recently, the Third Generation Partnership Project (3GPP) introduced the New Radio (NR) technology for Vehicle-to-everything (V2X) communication to enable delay-sensitive and bandwidth-hungry applications in vehicular communication. The NR system is strategically crafted to complement the existing Long Term Evolution (LTE) Cellular-Vehicle-to-Everything (C-V2X) infrastructure, particularly to support advanced services such as the operation of automated vehicles. It is widely anticipated that the 5th generation (5G) NR system will surpass LTE C-V2X in terms of achieving superior performance in scenarios characterized by high throughput, low latency, and enhanced reliability, especially in the context of congested traffic conditions and a diverse range of vehicular applications. This article will provide a comprehensive literature review on vehicular communications from Dedicated Short Range Communication (DSRC) to NR V2X. Subsequently, it delves into a detailed examination of the challenges and opportunities inherent in NR V2X technology. Finally, we proceed to elucidate the process of creating and analyzing an open-source 5G NR V2X module in network simulation-3 (ns-3) and then demonstrate the NR V2X performance in terms of different key performance indicators implemented through diverse operational scenarios.

Disassembly is a pivotal technology to enable the circularity of electric vehicle batteries by applying circular economy strategies that extend the life cycle of their components, like remanufacturing and repurposing, or by using efficient recycling to reintegrate the gained materials in producing new battery systems. This paper aims to develop a multi-method self-configuring simulation model for investigating disassembly scenarios while considering the battery design as well as the configuration and layout of the disassembly station. We demonstrate the developed model in a case study using a battery of Mercedes-Benz and the automated disassembly station of the project DeMoBat at Fraunhofer IPA. Furthermore, we introduce two disassembly scenarios, the component-oriented and the accessibility-oriented disassembly, and compare them using the simulation model to determine several indicators, such as the frequency of tool change, the number and distribution of robot routes, the tool utilization, and the disassembly time.

Underwater robotics equipped with visual detection systems have the function of detecting un-derwater artifacts, which is of great significance to deep-sea archaeology. Underwater artifacts are located in complex environments with poor imaging conditions, and the targets have chal-lenging problems such as breakage, stacking, and sediment burial, which causes the marine cul-tural target detection failures. To solve these problems, this paper proposes an underwater cul-tural target detection algorithm based on the deformable deep aggregation network model for underwater robotics exploration. To fully extract the target feature information of underwater targets in complex environments, this paper designs a multi-scale deep aggregation network with deformable convolutional layers. Besides, the BAM attention module is designed for fea-ture optimization, which enhances the potential feature information of the target while weak-ening the background interference information. Finally, the target prediction is achieved through feature fusion at different scales. The proposed algorithm has been extensively validat-ed and analyzed on the collected underwater artifact datasets, and the precision, recall, and mAP of the algorithm have reached 93.1%, 91.4%, and 92.8%, respectively. In addition, the present al-gorithm has been practically deployed on the underwater robotics. In the deep-sea tests, the ar-tifact detection frame rate reaches up to 19 fps, which satisfies the real-time target detection。

In order to solve the problem of many constraints and complex navigation environment in the path planning of unmanned surface vehicle(USV), an improved Sparrow search algorithm com-bining Cubic chaotic map and Gaussian random walk strategy was proposed to plan it. Firstly, in the population initialization stage, Cubic chaotic map was used to replace the random generation method of the traditional sparrow search algorithm to optimize the uneven initial distribution of the population and improve the global search ability of the population. Secondly, in the late it-eration of the algorithm, the standard deviation of fitness is introduced to determine whether the population is trapped in the local optimum. If true, the Gaussian random walk strategy is used to perturb the optimal individual and assist the algorithm to escape the local optimum. Thirdly, the chosen water environment is modeled, and the navigation information of the original inland electronic navigation chart(ENC) is preprocessed, gridized, and the obstacle swelling is processed. Finally, the path planning experiments of USV are carried out in inland ENC grid environment. The experimental results show that, compared with the traditional sparrow search algorithm, the average fitness value of the path planned by improved sparrow search algorithm is reduced by 14.8%, and the variance is reduced by 49.9%. The path planned by the algorithm is of good quality and high stability, and combined with ENC, it can provide a reliable path for USV.

In recent years, unmanned aerial vehicles (UAVs) have become a valuable platform for many applications, including communication networks. UAV-enabled wireless communication faces challenges in complex urban and dynamic environments. UAVs can suffer from power limitations and path losses caused by non-line-of-sight connections, which may hamper better communication performance. To address these issues, reconfigurable intelligent surfaces (RIS) have been proposed as a helpful tool to enhance UAV communication networks. However, due to the high mobility of UAV, complex channel environments, and dynamic RIS configurations, it is challenging to estimate the link quality of ground users. In this paper, we propose a link quality estimation model using a gated recurrent unit (GRU) to assess the link quality of ground users for a multi-user RIS-assisted UAV-enabled wireless communication system. Our proposed framework uses a time series of user channel data and RIS phase shift information to estimate the quality of the link for each ground user. Simulation results showed that the proposed GRU model effectively and accurately estimates the link quality of the ground users in the RIS-assisted UAV-enabled wireless communication network.

Aiming at the problem of hypersonic morphing vehicle avoiding no-fly zones and reaching the target, an improved predictor-corrector guidance method is proposed. Firstly, the aircraft motion model and the constraint model are established. Then, the basic algorithm is given, the Q-learning method is used to design the attack angle and sweep angle scheme to ensure that the aircraft can fly over the low-altitude zones. The B-spline curve is used to design the location of flight path points and the bank angle scheme is designed according to the predictor-corrector method, so that the aircraft can fly around to avoid high-altitude zones. Next, Monte Carlo reinforcement learning(MCRL) method is used to improve predictor-corrector method and Deep Neural Network(DNN) is used to fit reward function. The improved method can generate trajectory with better performance. Simulation results verify the effectiveness of the proposed algorithm.

In response to reducing the energy cost of unmanned surface vehicles (USVs) while overcoming the low accuracy problem in surface target detection, a lightweight detection algorithm with multi-scale feature fusion is proposed. Based on the popular one-stage lightweight Yolov7-tiny target detection model, a lightweight extraction module is designed first by introducing the multiscale residual module to reduce the number of parameters and computational complexity while improving accuracy. The Mish and SiLU activation functions are used to enhance network feature extraction. Second, the path aggregation network employs coordinate convolution to strengthen spatial information perception. Finally, the dynamic head, which is based on the at-tention mechanism, improves the representation ability of object detection heads without any computational overhead. According to the experimental findings, the proposed model has 22.1% fewer parameters than the original model, 15% fewer GFLOPs, a 6.2% improvement in mAP@0.5, a 4.3% rise in mAP@0.5:0.95, and it satisfies the real-time criteria. According to the research, the suggested lightweight water surface detection approach includes a lighter model, a simpler computational architecture, more accuracy, and a wide range of generalizability. It performs bet-ter in a variety of difficult water surface circumstances.

This paper proposes a high-performance and low-cost in-flight collision avoidance system based on background subtraction for unmanned aerial vehicles (UAVs). The pipeline of proposed in-flight collision avoidance system is as follows: (i) dynamic background subtraction to remove the background and to detect moving objects, (ii) denoise using morphology and binarization methods, (iii) Euclidean clustering to cluster the moving object and to remove noise blobs, (iv) distinguish independent objects and track the movement using Kalman filter, and (v) collision avoidance using proposed decision-making techniques. This work focuses on the design and the demonstration of a vision-based fast moving object detection and tracking system with decision-making capabilities to perform evasive maneuvers to replace high vision system such as event camera. The development of high-performance, low-cost unmanned aerial vehicles paired with rapid progress in vision-based perception systems herald a new era of autonomous flight systems with mission-ready capabilities. One of the key features of an autonomous UAV is a robust mid-air collision avoidance strategy. The novelty of our method lies in the motion-compensating moving object detection framework, which accomplishes the task with background subtraction via 2-D transformation approximation. Clustering and tracking algorithms process detection data to track independent objects, and stereo-camera-based distance estimation is conducted to estimate the 3-D trajectory, which is then used during decision-making procedures. The examination of the system is conducted with a quadrotor UAV test vehicle, and appropriate algorithm parameters for various requirements are deduced.

This paper proposes a compact planar Wi-Fi antenna with optimized radiation patterns for small uncrewed aerial vehicle (UAV) applications in both urban and open areas. It is suitable for mounting on the outermost side of the non-metallic wing of small UAVs. It has small dimensions of 16.5 mm (L) by 30.3 mm (W) by 1.6 mm (h), and the measured results of its prototype are in agreement with simulated data. Its impedance bandwidths over the two frequency ranges are 2.11 to 2.58 GHz and 5.06 to 7.5 GHz ( ). At 5.8 GHz, it has stronger radiation below the small UAV to reduce interference from rare-use directions. Its maximum radiations, the directions of the maximum radiation in each elevation plane, are below the UAV and between 14° and 29° from the horizontal plane. At 2.4 GHz, it has quasi-omnidirectional radiation to ensure a stable link in all directions, and its maximum radiations are near the horizontal plane. The optimized radiation patterns at 5.8 and 2.4 GHz can provide more antenna gain when the small UAV flies farther in urban and open areas, respectively. In addition, it has good vertically polarized radiation for long-distance applications.