Vehicle to vehicle (V2V) and Vehicle to infrastructure (V2I) or briefly V2X communications are the one of hot topics in automotive industry. Therefore, this situation is providing many advantages of connected vehicles and infrastructures which bring to human life. For instance, vehicles and road infrastructures which shares information with each other, provides a neat flow regulation, more ordered traffic flow and therefore jammed traffic dependent accident’s percentage will be decreased. On the other hand, security is the most important issue for these systems because the operation of V2X networks is completely dependent on uninterrupted and accurate information sharing. In the light of these information, in this paper we review security issues and current solution architectures. We also propose some open problems in this lively field.

The number of people owning vehicles has been steadily growing, resulting in increased numbers of vehicles on the roads, making roads more congested, and increasing the risk of accidents. In addition, heavy rain, snow, and fog have increased due to abnormal weather caused by global warming. These bad weather conditions can also affect the safety of vehicles and drivers. The need to disseminate safety messages on the social internet of vehicles due to these problems has been steadily increasing. In this paper, we propose an efficient safety message dissemination scheme that focuses on urban environments with high vehicle density and mobility to address these problems. The proposed scheme reduces packet loss by considering frequent cluster departures and subscriptions through an efficient cluster management technique. In a vehicle-to-vehicle environment, the dissemination of safety messages is divided into an intra-cluster and an inter-cluster emergency as well as general safety message dissemination technique. In a vehicle to infrastructure environment, the proposed scheme reduces the number of processing requests and duplicate messages made to roadside units (RSUs) through a request operation process for each vehicle and an RSU scheduling technique. We conducted several performance evaluations of message packet loss and the number of RSU processing requests to demonstrate the superiority of the proposed scheme.

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.

As V2X technology develops, acute problems related to reliable and secure information exchange between network objects in real time appear. The article aims to solve the scientific problem of building a network architecture for reliable delivery of correct and uncompromised data within the V2X concept to improve the safety of road users, using blockchain technology and mobile edge computing (MEC). The authors present a formalized mathematical model of the system, taking into account the interconnection of objects and V2X information channels, and an energy-efficient algorithm of traffic offloading to the MEC server. The paper presents the results of application of blockchain technologies and mobile edge computing in the developed system, their description, evaluation of advantages and disadvantages of the implementation.

The increase in the emission of greenhouse gases (GHG) is one of the most important world problems. Decreasing of GHG emission is a big challenge in the future. Transportation sector uses a significant part of petroleum production in the world and it leads to an increase in the emission of GHG. The result of this issue is that population of the world befoul environment of transportation system automatically. Electric Vehicles (EV) have a potentiality to solve a big part of the GHG emission and energy efficiency issues such as stability and reality of energy. The energy actors and their research team determine some targets for 2050; hence, they hope to decrease the world temperature to 6 °C in the best and 2 °C in the normal condition. Fulfillment of these scenarios needs suitable grid infrastructure but in most of the countries, the grid does not have a suitable background to apply those scenarios. In this paper, some problems about energy scenarios, energy storage systems, grid infrastructure and communication systems in supply and demand side of the grid and its solutions have been investigated.

Air corridors are an integral part of the advanced air mobility infrastructure. They are the virtual highways in the sky for transportation of people and cargo in the controlled airspace at an altitude of around 1000 ft. to 2000 ft. above the ground level. This paper presents fundamental insights into the design of air corridors with high operational efficiency as well as zero collisions. It begins with the definitions of air cube, skylane or track, intersection, vertiport, gate, and air corridor. Then, a multi-layered air corridor model is proposed. Traffic at intersections is analyzed in detail with examples of vehicles turning in different directions. The concept of capacity of an air corridor is introduced along with the nature of distribution of locations of vehicles in the air corridor and collision probability inside the corridor are discussed. Finally, the results of simulations of traffic flows are presented.

The paper proposes a methodological way to compensate the imbalance between energy generation and consumption using the battery block of electric vehicles as energy reservoir through the well-known Vehicle to Grid system (V2G). The method is based on a simulation process developed by the authors that take into consideration the daily fluctuations of energy consumption as well as the power level generated by an energy source, either conventional, renewable or hybrid. The study shows that for very large electric vehicle fleet the system renders non-viable, since the remaining energy in the battery block to let the electric vehicle usable for running during daytime avoids to compensating for the energy grid imbalance, only allowing to cover a percentage of the energy imbalance, which the proposed methodology may optimize. The analysis of the proposed methodology also shows the viability of the system when being applied to a small fleet of electric vehicles, not only compensating for the energy imbalance but also preserving the required energy in the battery of the electric vehicle to make it run. The method allows predicting the optimum size of electric vehicle battery, which depends on the energy generation level, coverage factor of the energy imbalance and size of the electric vehicle fleet.

Aiming at the problem of low charging efficiency caused by the scattered sensor nodes in traditional wireless rechargeable sensor networks (WRSNs), a UAV-assisted WRSN Online Charging Strategy Based on Dynamic Queue and Improved K-means (UOCS) is proposed. The scheme assumes that the energy consumption of nodes is unpredictable, and only generates charging requests when the energy is lower than a threshold, and performs on-demand responses to nodes that issue charging requests. The scheme combines the characteristics of one-to-one charging of UAVs, the selection and allocation timing of waiting queues and the number of UAVs, and the improved K-means partitioning based on space-time coordination(SPKM), which simplifies the problem of coordinated charging of multiple UAVs and maximizes energy. Using the efficiency and charging success rate, the optimal charging trajectory can be found under the constraint that the node will not starve to death due to power shortage. Finally, a simulation comparison experiment is carried out with the existing UAV charging scheduling strategy. UOCS achieves the optimal node survival rate with low algorithm complexity.

- Power grids of the future will likely incorporate more renewable energy distributed generation (REDG), also known as alternative energy systems. REDG units are increasingly being used in electrical transmission networks because to the overall positive effects they have on power networks. REDG systems are the backbone of smart electric networks and are essential to the operation of the smart grid. These REDG systems can additionally improve system reliability by providing some customers with a backup generator in the event of power interruptions. The report offers a thorough evaluation of the existing body of information on the topic of electric vehicles' (EVs') future interactions with the smart grid. The combination of the EVs' potential deployment and the smart grid's conceptual goal presents challenges in electric grid infra-structure, communication, and control. The proposal for connecting EVs to the grid is based on research into cutting-edge smart metering and communication systems. In the context of the vehicle-to-grid (V2G) phenomenon, the possibilities, benefits, and limitations of various EV smart charging systems are also fully examined. There is a fast growing percentage of distributed energy that is wind and solar photovoltaic. Their variable power output, however, introduces fresh challenges for those responsible for organizing, operating, and controlling the power grid. While fluctuations in the electric grid are problematic, they may be mitigated by the entry of EVs into the energy market. As such, we do a comprehensive literature search to learn more about the exciting research gap that needs to be filled and the most recent possible answer that involves EVs. Additionally, we take a close look at the practicality of the sophisticated V2G technology. The smart grid is a developing concept for the world's energy infrastructure, and this study analyzes in depth how EVs interact with it.

Veterans face difficulties accessing vital health and community services, especially in rural areas. Autonomous vehicles (AVs) can revolutionize transportation by enhancing access, safety and efficiency. Yet, there is limited knowledge about how Veterans perceive AVs. This study fills this gap by assessing Veterans' AV perceptions before and after exposure to an autonomous shuttle (AS). Using a multi-method approach, 23 participants completed pre- and post-AS Autonomous Vehicle User Perception Survey (AVUPS), with 10 participants also taking part in post-AS focus groups. Following exposure to the AS, differences were observed for three out of the four AVUPS domains: an increase in Intention to Use (p < 0.01), a decrease in Perceived Barriers (p < 0.05), and an increase in Total Acceptance (p = 0.01); Well-being remained unchanged (p = 0.81). Feedback from focus groups uncovered six qualitative themes: Perceived Benefits (n=70), Safety (n=66), Shuttle Experience (n=47), AV Adoption (n=44), Experience with AVs (n=17), and Perception Change (n=10). This study underscores AVs' potential to alleviate transportation challenges faced by Veterans, contributing to more inclusive transportation solutions. The research offers insights for future policies and interventions aimed at integrating AV technology into the transportation system, particularly for mobility-vulnerable Veterans in rural and urban settings.

Recently, inter-vehicle communication, which helps to avoid collision accidents (by driving safety support system) and facilitate self-driving (by dissemination of road and traffic information), has attracted much attention. In this paper, in order to efficiently collect road / traffic information in the request / response manner, first a basic method, Content-centric network (CCN) for Vehicular network (CV), is proposed, which applies CCN cache function to inter-vehicle communication. Content naming and routing, which take vehicle mobility into account, are investigated. On this basis, the CV method is extended (named as ECV) to avoid the cache miss problem caused by vehicle movement, and is further enhanced (named as ECV+) to more efficiently exploit cache buffer in vehicles, caching content according to a probability decided by channel usage rate. Extensive evaluations on network simulator Scenargie, with realistic open street map, confirm that the CV method and its extensions (ECV, ECV+) effectively reduce the average number of hops of data packets (by up to 47%, 63%, 83% respectively) and greatly improve the content acquisition success rate (by up to 356%, 444%, 689%, respectively), compared to the method without cache mechanism.

Pedestrians, bicyclists, scooterists are Vulnerable Road Users (VRU) in traffic accidents. The number of fatalities and injuries in traffic accidents involving vulnerable road users has been steadily increasing in the last two decades in the U.S. even though road vehicles now have perception sensors like camera to detect risk and issue collision warnings or apply emergency braking. Perception sensors like camera are highly affected by lighting and weather conditions. Camera, radar and lidar cannot detect vulnerable road users in partially occluded and occluded situations. This paper proposes the use of Vehicle-to-VRU communication to inform nearby vehicles of VRUs having trajectories with potential collision risk. An Android smartphone app with low energy Bluetooth (BLE) advertising is developed and used for this communication. The same app is also used to collect motion data of VRUs for training. VRU motion data is smoothed using a Kalman filter and an LSTM neural network is used for future motion prediction. This information is used in an algorithm comparing Time-To-collision-Zone (TTZ) for the vehicle and VRU and issue a driver warning with different severity levels. The warning severity level is based on analysis of real data from a smart intersection for close vehicle and VRU interactions. The resulting driver warning system is demonstrated using proof-of-concept experiments. The method can easily be extended to a VRU collision mitigation system.

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.

Cooperative localization under complex urban environments has become a solution to replace the Global Navigation Satellite System (GNSS) positioning. Due to utilizing distance measured and information exchanged between vehicles, cooperative localization results in high computational complexity and heavy communication overhead. This paper proposes a cooperative localization method based on evolutionary coalitional game theory, which implements vehicles' location estimation with less communication cost. We select the neighboring vehicles to form a coalition based on the node's square position error bound and communication cost. The location is obtained via exchanging information between vehicles. It is evident from the simulations and results that the proposed method holds low communication overhead while maintaining localization accuracy.

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.

Unmanned aerial vehicle UAV has become an important tool for emergency communication. When disaster happens, the roads in the disaster area always are seriously damaged, and theground infrastructure communication facilities are damaged. Under emergency conditions, UAV can go into key areas as communication nodes to provide communication services for users in the area.But it's difficult for UAV to cover the entire disaster area and the efficient rescue time will be delayed. Therefore, how to make the UAV accurately perceive the regional situation and deployed in the right position is worth studying. In this paper, a virtual simulation environment is established reasonably and deep reinforcement learning algorithm is used to train UAV agents. At the same time, a series of problems such as sparse reward and long training time are still not solved in the development of reinforcement learning algorithms,and the reward function is designed to improve training efficiency. First of all, we set a specific mountain emergency communication scenario, and combined with the specific application of the UAV to carry out virtual simulation, and build a virtual environment. Furthermore, an additional shaped reward function is designed to address the sparse reward problem. Through the improvement of deep Q-learning network DQN algorithm and the reward design based on potential function, the final evaluation index is improved, and the effectiveness of the algorithm is verified. The experimental results demonstrate our work can effectively shorten the training time and increase convergencerate.

As a manifestation of technological innovation achievements, patents reflect the frontier of technological development in the field. This paper constructs a multi-entity patent collaboration network and analyzes its evolution patterns to provide a reference for innovation organizations to grasp the general direction of innovation development and select partners. Based on the data of collaboration patents of two and three organizations in China's new energy vehicles (NEV) industry from 2008 to 2021, this paper constructs higher-order interaction collaboration innovation networks in different periods to analyze the spatial evolution of networks and patent cooperation activities. The results show that the generalized degree distribution of nodes and edges conforms to the power-law distribution. The two-dimensional simplex networks gradually form triangle groups centered on the core nodes, and the importance of key edges is beginning to emerge. Key organizations and partners, including State Grid Corporation of China, Tsinghua University, and China Electric Power Research Institute, will largely determine the direction for the future development of cooperation innovation. The study also reveals that Beijing, Jiangsu, Zhejiang, Guangzhou, and Shanghai are in central positions in the cooperative innovation network, based on their cooperation breadth and depth measures. Additionally, key links formed by Beijing as hubs are not only the center of information exchange but also function as bridges that connect to other regions.

Blockchain technology has emerged as a necessity for the decentralization of payment methods and transactions, but it has brought with it many properties of distributed systems that have made it a crucial technology for overcoming some of society's challenges, especially in the context of decentralizing services, transparency of information, availability, and security. Its architecture and communication methods, although possessing some complex nuances to understand, particularly for the lay audience in the field of distributed systems, protocols, and computer networks. In this article, we will explore some topics of distributed systems related to blockchain technology.

Underwater natural gas pipelines constitute critical infrastructure for energy transportation. Any damage or leakage in these pipelines poses serious security risks, directly threatening marine and lake ecosystems and potentially causing operational issues and economic losses in the energy supply chain. Due to the difficulty in detecting deterioration over time and regularly inspecting these submerged pipelines by divers, the use of unmanned underwater vehicles (UUVs) becomes crucial in this field. In this study, an underwater pipeline tracking experiment was carried out by providing autonomous features to a remote-controlled unmanned underwater vehicle. During the tracking of the underwater pipeline, damages were identified, and the locations of these damages were determined. The navigation information of the underwater vehicle, including orientation in the x, y, z axes (roll, pitch, yaw) from a gyroscope integrated with magnetic compass, speed and position information in the three axes from an accelerometer, and the distance to the water surface from a pressure sensor, was integrated into the vehicle. Pre-tests determined the necessary pulse width modulation values for the thrusters of the vehicle, enabling autonomous operation by providing these values as input to the thruster motors. In this study, the vehicle moves in 3D was achived by the vertical thruster of the vehicle was activated to maintain a specific depth, and applying equal force to the vehicle’s right and left thrusters forward movement, while differential force induced deviation angles. In pool experiments, the unmanned underwater vehicle autonomously tracked the pipeline as intended, identifying damages on the pipeline using images captured by the vehicle's camera. The images for damage assessment were processed using a convolution neural network (CNN) algorithm, which is a deep learning method. The position of the damage relative to the vehicle was estimated from the pixel dimensions of the identified damage. The location of the damage relative to its starting point was obtained by combining these two positional pieces of information from the vehicle's navigation system. The all study was performed within the Python environment.

Speech recognition, or automatic speech recognition (ASR), is a technology designed to convert spoken language into text using software. However, conventional ASR methods involve several distinct components, including language, acoustic, and pronunciation models with dictionaries. This modular approach can be time-consuming and may influence performance. In this study, we propose a method that streamlines the speech recognition process by incorporating a unified recurrent neural network (RNN) architecture. Our architecture integrates a convolutional neural network (CNN) with an RNN and employs a connectionist temporal classification (CTC) loss function. Key experiments were carried out using a dataset comprising 576,656 valid sentences, using erosion techniques. Evaluation of the model performance, measured by the word error rate (WER) metric, demonstrated remarkable results, achieving a WER of 2%. This approach has significant implications for the realm of speech recognition, as it alleviates the need for labor-intensive dictionary creation, enhancing the efficiency and accuracy of ASR systems, and making them more applicable to real-world scenarios. For future enhancements, we recommend the inclusion of dialectal and spontaneous data in the dataset to broaden the model's adaptability. Additionally, fine-tuning the model for specific tasks can optimize its performance for targeted objectives or domains, further enhancing its effectiveness in those areas.

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 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.

The widespread adoption of electromobility constitutes one of the measures designed to reduce air pollution caused by traditional fossil fuels. However, several factors are currently impending this process, ranging from insufficient charging infrastructure, battery capacity, long queueing and charging time, to psychological factors. On top of range anxiety, the frustration of the EV drivers is further fueled by the lack the uncertainty of finding an available charging point on their route. To address this issue, we propose a solution that comes to bypass the limitations of the Reserve now function of the OCPP standard, enabling drivers to make charging reservations for the upcoming days, especially when planning a longer trip. We created an algorithm that generates reservation intervals based on the charging station's reservation and transaction history. Subsequently, we ran a series of test cases that yielded promising results, with no overlapping reservations.

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.

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.

Paraphrase generation is an important yet challenging task in NLP. Neural network-based approaches have achieved remarkable success in sequence-to-sequence(seq2seq) learning. Previous paraphrase generation work generally ignores syntactic information regardless of its availability, with the assumption that neural nets could learn such linguistic knowledge implicitly. In this work we make an endeavor to probe into the efficacy of explicit syntactic information for the task of paraphrase generation. Syntactic information can appear in the form of dependency trees which could be easily acquired from off-the-shelf syntactic parsers. Such tree structures could be conveniently encoded via graph convolutional networks(GCNs) to obtain more meaningful sentence representations, which could improve generated paraphrases. Through extensive experiments on four paraphrase datasets with different sizes and genres, we demonstrate the utility of syntactic information in neural paraphrase generation under the framework of seq2seq modeling. Specifically, our GCN-enhanced models consistently outperform their syntax-agnostic counterparts in multiple evaluation metrics.

Vehicle-to-Vehicle (V2V) communication plays a pivotal role in modern intelligent transportation systems, enabling seamless information exchange among vehicles to enhance road safety, traffic efficiency, and overall driving experience. However, the secure transmission of sensitive data between vehicles remains a critical concern due to potential security threats and vulnerabilities. This research paper focuses on investigating the security protocols employed in Vehicle-to-Vehicle communication systems. A comprehensive review and analysis of relevant literature and research papers is conducted to gather information on existing V2V communication security protocols and techniques. The analysis encompasses key areas, including authentication mechanisms, encryption algorithms, key management protocols, and intrusion detection systems specifically applicable to V2V communication networks. Within the context of real-world V2V environments, this study delves into the challenges and limitations associated with implementing these protocols. Moreover, to foster a deeper understanding, the paper investigates present communication protocols in the field of Internet of Things (IoT) that are tailored for V2V. Various parameters, such as band-width consumption, energy consumption, latency, and message size, are considered during the evaluation of these protocols to gauge their effectiveness. The research outcomes aim to provide a comprehensive understanding of the strengths and weaknesses of the current V2V communication security protocols. Furthermore, based on the findings, this paper will propose improvements and recommendations to enhance the security measures of the V2V communication protocol. Ultimately, this research will contribute to the development of more secure and reliable V2V communication systems, propelling the advancement of intelligent transportation technology.

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.

Smart agriculture is a new way of farming that uses advanced technology. It makes a significant contribution to improving the efficiency and productivity of farming practices. Systems that use drones to monitor pests and spray pesticides greatly reduce the burden on farmers. In addition, remote sensing technology facilitates the monitoring of crop growth conditions and the early detection of diseases and pests. However, the high cost of equipment to communicate sensor data is preventing farmers from adopting sensing systems. As a result, many studies have been conducted to make the sensing system available at a low cost. Nevertheless, many farmers have not yet adopted them. Therefore, this paper proposes an agricultural sensor network system using drones and optical camera communication (OCC). The proposed system aims to achieve high-density farm sensing at low cost. The proposed system uses OCC as the communication method for sensor data. Low cost light sources such as LED panels can be used as transmitters. In addition, existing agricultural drones equipped with cameras can be used as receivers. We also propose a trajectory control algorithm for the receiving drone to efficiently collect the sensor data. The proposed system is suitable for deploying sensor networks in areas with underdeveloped infrastructure and radio silence. In this paper, we conducted a preliminary experiment at a leaf mustard farm in Kamitonda-cho, Wakayama to demonstrate the effectiveness of the proposed system using a Mavic 2 Pro drone, launched by DJI Co., Ltd.

Speech recognition, also known as automatic speech recognition (ASR), is a technology that enables software to transcribe spoken language into text. However, existing Amharic ASR methods require multiple separate blocks, such as language, acoustic, and pronunciation models with dictionaries, which can be time-consuming and influence performance. This study proposes an approach that replaces much of the speech pipeline with a single recurrent neural network (RNN) architecture. Our proposed architecture is based on a hybrid approach that combines a convolutional neural network (CNN) with a recurrent neural network (RNN) and a connectionist temporal classification (CTC) loss function. We conducted several experiments with noisy audio data that contain 20,000 valid sentences. The model was evaluated using the word error rate (WER) metric, achieving impressive results of 7% WER on noisy data. This approach has significant implications for the field of speech recognition, as it reduces the human effort required to create dictionaries and improves the efficiency and accuracy of ASR systems, making them more practical for real-world applications.

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.

Accurate localization of road agents is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques are recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiment requiring numbers of devices is hard to be conducted, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, carrier-phase, 〖C/N〗_0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.

With the rapid development of Intelligent Transportation Systems (ITS), vehicle type classification, as a key link in Automatic Toll Collection systems (ATC), is of great significance in improving traffic efficiency and reducing economic losses. This study proposes an intelligent vehicle classification system based on deep learning and multi-sensor data fusion to address the accuracy issues existing in vehicle classification methods based on optical sensors (OS) and human observers. The system significantly improves the accuracy and robustness of vehicle classification by combining deep Convolutional Neural Networks (CNN), LiDAR sensors, and machine learning algorithms. We first constructed a large-scale annotated dataset containing multiple vehicle types and complex traffic scenes to improve our model's capability to identify different vehicle characteristics. Next, CNN models based on different architectures were designed to extract global and local features of the vehicle, respectively. In addition, the LiDAR sensor was used to achieve the spatial structure architectures of the vehicle and combined with the output of the CNN model to improve classification performance under occlusion and complex scenes.

Unmanned aerial vehicles (UAVs) have increasingly become integral to multi-access edge computing (MEC) due to their flexibility and cost-effectiveness, especially in the B5G and 6G eras. This paper aims to enhance the Quality of Experience (QoE) in large-scale UAV-MEC networks by minimizing the shrinkage ratio through optimal decision-making in computation mode selection for each user device (UD), UAV flight trajectory, bandwidth allocation, and computing resource allocation at edge servers. However, the interdependencies among UAV trajectory, binary task offloading mode, and computing/network resource allocation across numerous IoT nodes pose significant challenges. To address these challenges, we formulate the shrinkage ratio minimization problem as a mixed-integer nonlinear programming (MINLP) problem and propose a two-tier optimization strategy. To reduce the scale of the optimization problem, we first design a low-complexity UAV partition coverage algorithm based on the Welzl method and determine the UAV flight trajectory by solving a Traveling Salesman Problem (TSP). Subsequently, we develop a coordinate descent (CD) based method and an alternating direction method of multipliers (ADMM) based method for network bandwidth and computing resource allocation in the MEC system. The CD-based method is simple to implement and has a low computational complexity, while the ADMM-based method can further enhance the optimization result through joint optimization. Extensive simulations demonstrate that our proposed algorithms perform well in large-scale edge networks and outperform other representative benchmark methods.

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.

To satisfy the interests of multiple agents and those of comprehensive indicators such as peak-to-valley differences and load fluctuations occurring on the network side, this paper presents a flexible load demand-side response optimization method that considers the benefits of peak-to-valley smoothing. First, load aggregation modelling of air conditioning and electric vehicles was conducted, and the complementarity of the power consumption behaviour of different types of flexible loads was used to improve the responsiveness of the load aggregator. Second, considering demand-side responses and taking into account the interests of both supply and demand, the load fluctuation and peak-to-valley difference on the network side are reduced, and a flexible load double-layer optimization model incorporating the peak-to-valley smoothing benefit is established. Finally, the effectiveness of the proposed optimization model is verified by using the KKT condition and the big M method to evaluate this two-layer optimization problem as a single-layer optimization problem. Comparative examples demonstrate that the proposed two-layer optimization method can improve the revenue of the distribution network operators and the load aggregators by using the complementary nature of the load response characteristics of electric vehicles and air conditioners. Moreover, the proposed method can effectively reduce the load peak-to-valley difference and load fluctuation of the distribution network by introducing the peak-to-valley smoothing benefit model.

Autonomous systems require identifying the environment and it has a long way to go before putting it safely into practice. In autonomous driving systems, the detection of obstacles and traffic lights are of importance as well as lane tracking. In this study, an autonomous driving system is developed and tested in the experimental environment designed for this purpose. In this system, a model vehicle having a camera is used to trace the lanes and avoid obstacles to experimentally study autonomous driving behavior. Convolutional Neural Network models were trained for Lane tracking. For the vehicle to avoid obstacles, corner detection, optical flow, focus of expansion, time to collision, balance calculation, and decision mechanism were created, respectively.

Unmanned vehicles (UVs) have become increasingly important in various scenarios of civil and military operations. The present work aims at the conceptual design of a modular Amphibious Unmanned Ground Vehicle (A-UGV), that can be easily adapted for different types of land and/or water missions, with low monetary cost (<5k€, without sensors). Basing the design on the needs highlighted in the 2021 review of the Strategic Directive of the Portuguese Navy, the necessary specifications and requirements are established for two mission scenarios. Then a market research analysis focused on vehicles currently available, and their technological advances, is conducted to identify existing UVs solutions and respective characteristics/capabilities of interest to the current work. To study and define the geometry of the hull and the configuration of the A-UGV itself, preliminary computational structural and fluid analyses are carried out to ensure it complies with the specifications initially established. As a result, one obtains a fully electric vehicle, with approximate dimensions of 1050×670×450mm (length-width-height), enabled with 6×6 traction capable of reaching 20km/h in land, possesses amphibious capabilities of independent propulsion in water up to 8kts, and with an estimated autonomy of over 60 minutes.

The option of decarbonizing urban freight transport using Battery Electric Vehicle (BEV) seems promising.However, there is currently a strong debate whether Fuel Cell Electric Vehicle (FCEV) might be the bettersolution. The question arises as to how a fleet of FCEV influences the operating cost, the Greenhouse Gas(GHG) emissions and primary energy demand in comparison to BEVs and to Internal Combustion EngineVehicle (ICEV). To investigate this, we simulate the urban food retailing as a representative share of urbanfreight transport using a multi-agent transport simulation software. Synthetic routes as well as fleet size andcomposition are determined by solving a Vehicle Routing Problem (VRP). We compute the operating costsusing a total cost of ownership (Total Cost of Ownership (TCO)) analysis and the use phase emissions as wellas primary energy demand using the Well To Wheel (WTW) approach. While a change to BEV results in 17 -23% higher costs compared to ICEV, using FCEVs leads to 22 - 57% higher costs. Assuming today’s electricitymix, we show a GHG emission reduction of 25% compared to the ICEV base case when using BEV. Currenthydrogen production leads to a GHG reduction of 33% when using FCEV which however cannot be scaled tolarger fleets. Using current electricity in electrolysis will increase GHG emission by 60% compared to the basecase. Assuming 100% renewable electricity for charging and hydrogen production, the reduction from FCEVsrises to 73% and from BEV to 92%. The primary energy requirement for BEV is in all cases lower and forhigher compared to the base case. We conclude that while FCEV have a slightly higher GHG savings potentialwith current hydrogen, BEV are the favored technology for urban freight transport from an economic andecological point of view, considering the increasing shares of renewable energies in the grid mix.

Over the past few decades, the growth of the urban population has been remarkable. Nowadays, 50% of the population lives in urban areas, and forecasts point that by 2050 this number will reach 70%. Today, 64% of all travel made is within urban environments and the total amount of urban kilometers traveled is expected to triple by 2050. Thus, seeking novel solutions for urban mobility becomes paramount for 21st century society. In this work, we discuss the performance of vehicular networks. We consider the metric Delta Network. The Delta Network characterizes the connectivity of the vehicular network through the percentage of travel time in which vehicles are connected to roadside units. This article reviews the concept of Delta Network and extends its study through the presentation of a general heuristic based on the definition of scores to identify the areas of the road network that should receive coverage. After defining the general heuristic, we show how small changes in the score computation can generate very distinct (and interesting) patterns of coverage, each one suited to a given scenario. In order to exemplify such behavior, we propose three deployment strategies based on simply changing the computation of scores. The results show that the strategies derived from the general heuristic are very interesting, all of them deploying roadside units in a circle pattern around the traffic epicenter.

Electric vehicles (EV) are getting more commonplace in the transportation sector in recent times. As the present trend suggests, this mode of transport is likely to replace the internal combustion engine (ICE) vehicles in near future. Each of the main EV components has a number of technologies that are currently in use or can become prominent in the future. EVs can cause significant impacts on the environment, power system, and other related sectors. The present power system can face huge instabilities with enough EV penetration; but with proper management and coordination, EVs can be turned into a major contributor to the successful implementation of smart grid. There are possibilities of immense environmental benefits as well, as the EVs can extensively reduce the greenhouse gas emission from the transportation sector. However, there are some major obstacles for EVs to overcome before replacing the ICE vehicles totally. This paper is focused on reviewing all the useful data available on EV configurations, energy sources, motors, charging techniques, optimization techniques, impacts, trends, and possible directions of future developments. Its objective is to provide an overall picture of the current EV technology and ways of future development to assist in future researches in this sector.

Different methodologies are being used to study the effects of autonomous vehicle (AV) in the mixed traffic indicating the interaction among autonomous and human-driven vehicles. Micro-scopic simulation tools are popular in such assessment as it offers scope to experiment in eco-nomical, robust, and optimistic way. Lack of reliable real-world data to calibrate and evaluate the connected autonomous vehicles (CAV) simulation model is a major challenge. One interesting methodology could be dealing the CAVs as conventional human driven vehicles and predict its possible characteristics based on the simulation inputs. The conventional human driven vehicles from real world, in this methodology, come to aid as benchmark to offer the measure of effective-ness (MoE) for the calibration and validation. For the three most common driving modules, a sensitivity analysis of the driving behaviors of AVs and an effect assessment of CAVs in a mixed traffic environment were done to explore the human alike autonomous technology. The findings show that, up to a point, which is directly re-lated to the quantity of interacting vehicles, the impact of CAVs is typically favorable. This study validates the approach and supports past studies by showing that CAVs perform better in traffic than AVs for traffic performance and safety aspects. On top of that, the sensitivity analysis has shown that enhancements in technology are required for obtaining the maximum advantages.

In air quality management, at urban and regional levels, road transport remains the cornerstone of residential, commercial, and industrial activities, and the vehicle exhaust emissions maintain the position of one of the key contributing sources. In Indian cities, big and small, vehicle exhaust emissions and dust from vehicle movement on the roads, contribute to as much as 50% of particulate matter pollution in a year. So, having access to a vehicle exhaust emissions inventory that is reliable and replicable is critical for air quality management and this exercise starts with access to vehicle stock numbers. These numbers are typically obtained from vehicle registration databases, traffic surveys, and other governmental records, and often require time consuming data cleaning protocols before they can used for emissions and pollution baseline analysis. This paper provides a cleaned and open-access vehicle stock database, as registered and in-use fleet, for the period covering 1993 and 2018, for all India and states, along with an estimate of age-mix of the vehicles using survival functions.

The use of sensors in monitoring of lateral accelerations in delivery van transport focuses on measuring lateral accelerations on routes with roundabouts and curves to increase road safety. Using microelectromechanical system (MEMS) sensors, it measures the lateral accelerations acting on the vehicle and the load being transported during the test drives to study vehicle dynamics for cargo securing. Using an accelerometer and position tracking, accelerations can be detected when traversing curves and roundabouts at selected locations on the vehicle. The level of accelerations on the roof of the vehicle was found to be like those occurring on a lashed load with limited movements, where regression analysis can be used to determine the relationship between lateral accelerations at different sensor positions. If we compare the mean values of the lateral accelerations of the individual events between the sensors, the sensor on the side of the vehicle body at the height of the sensor on the load had approximately 5% lower mean values than the sensor on the roof. The sensor on the load measured approximately 5% higher mean values than the sensor on the roof. Hence, the mean lateral accelerations of the individual events for the sensor on the load are 10% higher than for the sensor at the same height on the vehicle body. From the above testing, we can say that the values of the mean lateral accelerations of the van from the sensor on the roof of the vehicle, are closer to the values of the accelerations of the sensor on the load than to the values of the sensor on the body of the vehicle at the same height.

To regulate the growth of battery-based electric vehicles (BEVs) in Indonesia and mitigate their impact on the power grid's supply-demand balance, the government can adjust BEV regulations and offer subsidies. The Jawa-Madura-Bali (Jamali) electrical system, being the largest in Indonesia [1], faces the challenge of accommodating the increasing number of vehicles [2]. Subsequent to analyzing Jamali's electricity supply using data from the National Electricity Company (RUPTL) [3], simulations are constructed to model the grid's demand side. Input variables such as Jamali's population, internal combustion engine (ICE) and electric vehicle populations, initial charging time (ICT), slow- and fast-charging ratios, and BEV charge load curves are simulated. Scenario variables like supply capacity growth speed, vehicle population growth speed, subsidy impact on EV attractiveness, ICT, and fast-charging ratio are subsequently simulated for 2024-2029. Four key simulation outcomes are identified: the best-case scenario (Scenario 1776), achieves the highest EV growth with minimal grid disruption, resulting in a 45.38% EV percentage in 2029 and requiring an annual allocation of 492 billion rupiah to match supply with demand. The worst outcome scenario leads to a 23.12% EV percentage, necessitating 47.566 billion rupiah for EV subsidies in 2029. Additionally, the most and least probable scenarios based on literature research are evaluated. This simulation and its results provide insights into EV growth's impact on the grid's balance in one presidential term from 2024 to 2029 [4], aiding the government in planning regulations and subsidies effectively. Improving the simulation's accuracy depends on refining input variables over time.

The field of Cooperative Intelligent Transport Systems and more specifically Pedestrians to Vehicles could be characterized as quite challenging, since there is a broad research area to be studied, with direct positive results to society. Pedestrians to Vehicles is a type of Cooperative Intelligent Transport System, within the group of Early Warning Collision/Safety System. In this article, we examine the research and applications carried out so far within the field of Pedestrians to Vehicles Cooperative Transport Systems by leveraging the information coming from Vulnerable Road Users’, smartphones. Moreover, an extensive literature review has been carried out in the fields of Vulnerable Road Users Outdoor Localisation via smartphones and Vulnerable Road Users Next Step/Movement Prediction, which are closely related to Pedestrian to Vehicle applications and research. We identify gaps that exist in these fields that could be improved/extended/enhanced or newly developed, while we address future research objectives and methodologies that could support the improvement/development of those identified gaps.

Abstract: This study investigates the effect of Electric Vehicle Charging Stations (EVCS) on light-duty Electric Vehicle sales and the Total California rebates. Electric Vehicle Charging Stations (EVCS) are infrastructure facilities that allow for the charging of electric vehicles (EVs). I applied the before and after model with California state county using its community-level attributes and used the difference-in-difference design to identify a strategy for estimating the causal effects of these attributes. These attributes include EVCS installations by time, EV sales by time, rebate applications by time, the number of multi-unit housing units, and the median income levels. The empirical ev-idence of this study shows the estimated relationship between public EV charging installation and EV sales overall by community income level, housing density, and other relevant factors, the result shows that EVCSs is highly correlated with light duty Electric Vehicle adoption. For the investors, policymakers, and other stakeholders, this study provides evidence of a threshold of EVCS on Light Duty Electric Vehicle adoption and rebate in California.

Wind loads have become one of the key influence factors for the running safety of vehicles and comfort of passengers. Investigation on the wind speed spectrum characteristics of a moving vehicle under turbulent crosswinds is of great influence. Expressions of the wind speed spectrum of a moving vehicle was obtained from the von Kármán spectrum based on Taylor’s frozen flow hypothesis. The influence factors, including the ratio of the vehicle speed to the wind speed and the wind yaw angle from 15° to 175°, were analyzed. The maximum value of the wind speed spectrum and the corresponding frequency were studied as well. The results show that the maximum values of the wind speed spectrum of the moving vehicle were larger than those of the static vehicle. The maximum value of the wind speed spectrum corresponding to the moving vehicle first increased and then decreased as the wind yaw angle increased. Some of the frequencies corresponding to the longitudinal wind speed spectrum values of the moving vehicles were smaller than those of the static vehicle. For moving vehicles, the frequency values corresponding to the maximum values of the longitudinal wind speed spectrum first increased and then decreased as the ratio of the vehicle speed to the wind speed and the wind yaw angle increased.

In this paper, we present a novel Two-Echelon Ground Vehicle and Its Mounted Unmanned Aerial Vehicle Cooperated Routing Problem (2E-GUCRP). The 2E-GUCRP arises in the field of Unmanned Aerial Vehicle (UAV) Routing Problem such as those encountered in the context of city logistics. In a typical cooperated system, the UAV is launched from the Ground Vehicle (GV) and automatically flies to the designated target. Meanwhile, acting as a mobile base station, the GV can charge or change the UAV’s battery on the designated landing points to enable the UAV to continue its mission. The objective is to design efficient GV and UAV routes to minimize the total mission time while meeting the operational constraints. A Mixed Integer Programming (MIP) model, which could be solved by commercial software, is constructed to describe this problem. In order to quickly solve the medium-scale problems, two existing heuristics to solve 2E-VRP are improved. The computational experiments are set up to compare our model with the 2E-VRP. The results indicate that the 2E-GUCRP obtains a better efficiency. Further discussion of the practical instance points out that the increase in efficiency is related to the speed relationship between the GV and the UAV.

The salt marshes, lying at the land-sea temperate interface, furnish a plethora of ecosystems services such as biodiversity niche support, ocean-climate change regulation, ornithology recreo-tourism or plant gathering by hand. They undergo significant worldwide losses due to their conversion into crop fields and to their spatial compression between the rising sea-level and the armoring shoreline. Their monitoring however requires to use a suite of remote sensing sensors to embrace the regional scale while capturing the plant details. This research innovatively adopts a multiscale approach using a cascading spaceborne and airborne process, from the 10-m Sentinel-2, through the 3-m Dove, to the 0.03-m unmanned airborne vehicle (UAV) imageries. The high to very high temporal resolution of the Sentinel-2 and Dove enabled to cover twenties and tens of km2 over five and four years, respectively, in the form of normalized difference vegetation index (NDVI) classes, associated with microphytobenthos, low, medium and high salt marsh vegetation, including the opportunistic Elyma genus. The NDVI was then modelled at the UAV scale (a few km2) using a three-layered NN prediction, providing the final near-infrared (NIR), and the intermediate red, green and blue reflectance imageries, calibrated/validated/tested with the Dove reflectance imageries (R2NIR=0.98, R2red=0.88, R2green=0.84, and R2blue=0.90). The 100fold increase in pixel size allowed to detect the decimeter-scale objects of the tidal flats and salt marshes, to enlarge the NDVI class ranges, and hold great promise to model other spectral bands at the UAV scale for further deeply enhancing the salt marsh mapping.

Image segmentation is a new challenge prob- lem in medical application. The use of medical imaging has become an integral part of research, as it allows us to see inside the human body without surgical intervention. Many researcher have studied brain segmentation. One stage method is used to segment the brain tissues. In this paper, we proposed the multi-stage generative ad- versarial network to solve the problem of information loss in the one-stage. We utilize the coarse-to-fine to improve brain segmentation using multi-stage generative adversar- ial networks (GAN). In the first stage, our model generated a coarse outline for (i) background and (ii) brain tissues. Then, in the second stage, the model generated outline for (i) white matter (WM), (ii) gray matter (GM) and (iii) cerebrospinal fluid (CSF). A good result can be achieved by fusing the coarse outline and refine outline. We conclude that our model is more efficient and accu- rate in practice for both infant and adult brain segmenta- tion. Moreover, we observe that multi-stage model is faster than prior models. To be more specific, the main goal of multi-stage model is to see the performance of the model in a few shot learning case where a few labeled data are available. For medical image, this proposed model can work in a wide range of image segmentation where the convolution neural networks and one-stage methods have failed.

This paper presents a novel real time control strategy on four-wheel independent steering system for electric vehicle with stepper motors using neural networks combined with fuzzy logic. The control of the four wheels is based on a variation of Hopfield Neural Network (VHNN) method, in which the input is the error of each steering motor and the output is processed by a hyperbolic tangent function (HTF) feeding the fuzzy logic controller (FLC), which ultimately drives the stepper motor. The whole system is made up of the four aforementioned blocks where they work in sync and inseparable from each other with the common goal of driving all the steering stepper motors at the same time. Simulation results demonstrate that the proposed control method has strong robustness and practicability under different angles and speeds.

Vehicle routing is a critical factor in municipal solid waste (MSW) collection planning and operations. Poor routing can introduce inefficiencies and cause targeted levels of services or performance to be missed irrespective of the level of resource application. Trial and error approaches have been proven to be not the best in the planning and prediction of expected performance. This study explores various Geographic Information System (GIS) tools and analysis techniques, and how they can be applied to optimizing vehicle routes in light of challenging site conditions. Using Adentan West residential area, suburb of Accra Ghana as a case study, current performance of the trial and error method was measured and a GIS computer model was used to evaluate various optimization scenarios to determine the level of savings that can be made. Field measurements were taking with Global Positioning System (GPS) devices for waste collection activities in areas with varying characteristics and conditions, and data analysed for one selected vehicle operating four days per week. It was found that, for a scenario where only the bin collection order was optimized while route selection was restricted by the ArcGIS Network Analyst, 2.6% of travel distance and 2.21% of travel time were saved. For the second scenario where only the route selection was optimized while order of bin collection was restricted, 4.1% and 1.5% of travel distance and time respectively were saved. For a third scenario where both the order of collection and route selection were together optimized, 10.9% and 3.7% of travel distance and time respectively were saved. Lastly, by regrouping all the bins for daily collection, 4.5% and 1.2% of travel distance and time respectively were saved. The results demonstrated that there is always room for optimization of solid waste collection routing irrespective of site constraints and other challenges that the nature of bin distribution pose to drivers. In developing countries like Ghana, where there is high demand for services in the face of limited road network access, application of GIS in optimization of routes will guide providers in planning and subsequently make more savings in fuel consumption, vehicle maintenance and cost of man-hours.

Facing the charging difficulties of free-floating shared electric vehicle and the high cost of mobile charging for single demand, this article proposes a collaborative charging planning method based on the complementary advantages of fixed charging stations and mobile charging vehicles, which can charge shared electric vehicles more efficiently and reduce the charging cost at the same time. A bi-level programming model for fixed and mobile cooperative charging is constructed. The upper level of the model is the minimization model of system charging total cost, seeking the optimal charging scheme and the number of mobile charging vehicles. The lower model is a fixed and mobile cooperative charging path planning model, which calculates the optimal routes for mobile charging vehicles and the shared electric vehicles that need to be transferred to the fixed charging station. The example results show that the cost of the proposed fixed-mobile cooperative charging scheme is reduced by 12.6% compared with the pure fixed charging scheme and 14.9% compared with the pure mobile charging scheme

The work aims to improve the battery performance in hybrid electric vehicles (HEVs) by reducing the vehicle speed. We develop a specific protocol for managing battery use and optimizing the energy consumption rate to achieve this goal. The protocol automatically controls the driving operation, avoiding incompatible driving patterns with energy-saving mode and performance improvement. The protocol is submitted to a simulation process to predict the energy rate lowering and battery performance enhancement. The proposed protocol applies to any hybrid electric vehicle type and route conditions since it uses vehicle mass, drag and rolling coefficients, and road slope as variable parameters to determine the minimum energy consumption rate. We run experimental tests to validate the simulation data and the proposed protocol. Furthermore, the protocol applies to variable starting vehicle speeds, from 10 to 50 km/h, corresponding to the current driving patterns, sport, normal, and eco, set up by car manufacturers. A reduction of 10% in vehicle speed in urban and peripheral routes achieves a minimum energy rate, enhancing battery management. Current vehicle speed shows a deviation from optimum management of 18% while applying the vehicle speed reduction limits the deviation to 0.2%. Experimental results show a good agreement with simulation data with 94% accuracy. We tested the protocol for urban and peripheral routes with maximum vehicle speed limits of 60 and 90 km/h.

Recently, the number of vehicles on the road, especially in urban centers, has increased dramatically due to the increasing trend of individuals towards urbanization. As a result, manual detection and recognition of vehicles (i.e., license plates and vehicle manufacturer) become an arduous task and beyond human capabilities. In this paper, we have developed a system using transfer learning-based DL techniques for automatic identification of Jordanian vehicles. The YOLOv3 (You Only Look Once) model was re-trained using transfer learning to accomplish the license plate detection, character recognition, and vehicle logo detection. While VGG16 (Visual Geometry Group) model was retrained to accomplish the vehicle logo recognition. To train and test these models, four datasets have been collected. The first dataset consists of 7,035 Jordanian vehicle images, the second dataset consist of 7,176 Jordanian license plates, and the third dataset consists of 8,271 Jordanian vehicle images. These datasets have been used to train and test the YOLOv3 model for Jordanian license plate detection, character recognition, and vehicle logo detection, respectively. While the fourth dataset consists of 158,230 vehicle logo images used to train and test the VGG16 model for the vehicle logo recognition. Text measures were used to evaluate the performance of our developed system. Moreover, mean average precision (mAP) measure was used to evaluate the YOLOv3 model of the detection tasks (i.e., license plate detection and vehicle logo detection). For license plate detection, the precision, recall, F-measure, and mAP were 99.6%, 100%, 99.8%, and 99.9%, respectively. While for character recognition, the precision, recall, and F-measure were 100%, 99.9%, and 99.95%, respectively. The performance of license plate recognition stage was evaluated by evaluating these two sub-stages as a sequence, where the precision, recall, and F-measure were 99.8%, 99.8%, and 99.8%, respectively. Furthermore, for vehicle logo detection, the precision, recall, F-measure, and mAP were 99%, 99.6%, 99.3%, and 99.1%, respectively, while for vehicle logo recognition, the precision, recall, F-measure were 98%, 98%, and 98%, respectively. The performance of vehicle logo recognition stage was evaluated by evaluating these two sub-stages as a sequence, where the precision, recall, and F-measure were 95.3%, 99.5%, and 97.4%, respectively.

An effective battery management system (BMS) is indispensable for any lithium-ion battery (LIB) powered systems such as electric vehicles (EVs) and stationary grid-tied energy storage systems. Massive wire harness, scalability issue, physical failure of wiring, and high implementation cost and weight are some of the major issues in conventional wired-BMS. One of the promising solutions researchers have come up with is the wireless BMS (WBMS) architecture. Despite research and development on WBMS got momentum more than a decade ago, it is still in a preliminary stage. Significant further upgradation is still required towards developing an industry-ready WBMS, especially for high-power LIB packs. Therefore, an in-depth survey exclusively on WBMS architectures is presented in this article. The aim is to provide a summary of the existing developments as well as to present an informative guide to the research community for future development by highlighting the issues, emerging trends, and challenges. In-depth analysis of the existing WBMS topologies will not only help the researchers to understand the existing challenges and future research scopes clearly while at the same time enthuse them to focus their research inclination in the domain of WBMS.

This study describes the implementation of IoT sensors in drinking water systems and fog collector systems in low-income communities in Ecuador. The influence and handling of these sensors are analyzed, as well as the materials and methods used in their implementation. The importance of validating the accuracy and reliability of IoT sensors compared to professional devices, especially in mountain areas, is highlighted. In addition, it is mentioned that the cost-benefit of using IoT sensors in fog catchers and drinking water networks depends on several factors, such as the scale of the project, specific objectives, and available resources. Finally, it is highlighted that the use of IoT sensors in smart construction and water collection systems has proven to be beneficial in preventing effects on the operation.

Consistent increment of road traffic in cities is remained a threat to traffic performance and causes confliction. The researcher repeatedly emphasized the connected autonomous vehicles (CAVs) as stronger solution to deal with traffic related issues. While having autonomous system in the dedicated lanes is found to be safe by many investigators because of predictable surrounding maneuvers, in contrast, the mixed lane states remain questionable. Different methodologies are being used to study the effects of autonomous vehicles (AVs) in the mixed traffic. Microscopic simulation tools are popular in such cases as it offers scope to experiment in cheap, robust, and optimistic way. One interesting methodology to deal with CAVs is considering it as conventional human driven vehicles and predict its possible characteristics based on the simulation inputs. One big challenge is, as there is no regular real-world data to calibrate and to validate the simulated model for CAVs. This is where conventional human driven vehicles from real world, come to aid as benchmark to offer the measure of effectiveness (MoE) for the calibration and validation. For the three most common driving modules, a sensitivity analysis of the driving behaviors of autonomous vehicles (AVs) and an effect assessment of CAVs in a mixed traffic environment were done to explore the human alike autonomous technology. The findings show that, up to a point, which is directly related to the quantity of interacting vehicles, the impact of CAVs is typically favorable. This study supports earlier research by demonstrating that CAVs outperform AVs in traffic. In addition, the sensitivity analysis has shown that technical and infrastructural advances are necessary to achieve the greatest benefits.

The development of low-cost, open-source Remotely Operated Vehicle (ROV) systems has provided almost unrestricted access for researchers looking to monitor the marine environment in ever greater resolution. Sampling microbial communities from the marine environment, however, still usually relies on Niskin-bottle sampling (ROV or CTD based), a method which introduces an inaccuracy and variability that is incompatible with metatranscriptomic analysis. Here, we describe a versatile, easily-replicated platform which achieves in situ mRNA preservation, via the addition of RNAlater to filtered microbial cells, to enhance ROV or CTD functionality.

Brain-to-brain communication (B2B-C) is rapidly expanding, integrating communication technology and neuroscience to enable direct neural data transfer between people. Nonetheless, because neural data is susceptible to noise, interference, and hostile attacks, guaranteeing the security and resilience of B2B-C systems continues to be difficult. This work aims to use Adversarial Neural Network Training (ANNT) to improve the security of B2B-C systems by using Steady-State Visually Evoked Potentials (SSVEP) EEG data. We use two large SSVEP datasets for a thorough analysis: Lee2019_SSVEP and Nakanishi2015. We use the Fast Gradient Sign Method (FGSM) to create adversarial instances and ANNT to train the model on clean and adversarially perturbed data. The system's accuracy and resilience are significantly improved by ANNT, as seen by the up to 17% increase in adversarial accuracy and the average 0.03 point improvement in the Area Under the Curve (AUC). This work demonstrates how ANNT may strengthen B2B-C systems against advanced cyberattacks, opening the door for dependable and safe neural communication technologies.

Peer-to-peer (P2P) content distribution or file sharing system aims to facilitate the dissemination of large files over unreliable networks. Network coding is a new transmission technique that has captured the interest of researchers because of its ability to increase throughput and robustness of the network, and decrease the download time. In this survey paper, we extensively summarize, assess, compare, and classify the most recently used techniques to improve P2P content distribution systems performance using network coding. To the best of our knowledge, this survey is the first comprehensive survey that specifically focuses on the performance of network coding based P2P file sharing systems.

This paper focuses on the impact that urban traffic has on the environment. The study characterizes the global effect of GHG emissions, including the ecologic evaluation and the characterization, normalization, and evaluation factor. The work makes a detailed survey of the different modes of driving and their influence on engine performance as one of the principal causes of gas emissions during the combustion process. The article analyzes six types of vehicles equipped with different engine configurations: diesel and gasoline, GLP and GNC, hybrid electric, and plug-in hybrid electric. Simulation of the driving mode under various operational conditions for every type of engine results in energy consumption, thus, in GHG emissions, carbon dioxide and monoxide, nitrogen oxides, and Sulphur dioxide. The study concludes that a reduction in vehicle speed, thus in the engine revolutions, has positive effects on engine combustion and gasses emissions, which is reduced by 27.5%. The study also concludes that the limitation in driving mode, avoiding sharp and sudden acceleration, may reduce up to 45% of GHG emissions. The changes applied in the driving mode improve the air quality in the urban environment, reducing the content of GHG from 39% to 61%.

The current study investigated the use of two-dimensional spatial distribution mapping representing the chlorophyll-a level in a river generated via an unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV). A domestically developed UAV (Remo-M, Uconsystem Inc., Korea) and a USV developed by our research team were used to collect data from the Nae Seong stream in Korea. An adaptation of the “Data Cleaner” tool was developed and used for USV data processing and analysis. The operation of the autonomous USV was successful. Four previously described indices for quantifying algal blooms in rivers were utilized to create chlorophyll-a images, the normalized difference vegetation index (NDVI), the normalized green red difference index, the green normalized difference vegetation index (GNDVI), and the normalized difference red edge index. The suitability of the linear regression analysis of the correlation between the spectral indices obtained using the UAV and the in situ chlorophyll-a data obtained using the USV was evaluated with the coefficient of determination (R2) at a significance level of p &lt; 0.001. In field application and correlational analysis the NDVI was strongly correlated with chlorophyll-a (R2 = 0.88, p < 0.001), and the GNDVI was moderately correlated with chlorophyll-a (R2 = 0.74, p < 0.001). The map of chlorophyll-a was successfully quantified using the UAV and USV hybrid platforms.

This work is part of an ongoing research project to develop an unmanned flying social robot to monitor dependants at home in order to detect the person’s state and bring the necessary assistance. In this sense, this paper focuses on the description of a virtual reality (VR) simulation platform for the monitoring process of an avatar in a virtual home by a rotatory-wing autonomous unmanned aerial vehicle (UAV). This platform is based on a distributed architecture composed of three modules communicated through the Message Queue Telemetry Transport (MQTT) protocol: the UAV Simulator implemented in MATLAB/Simulink, the VR Visualiser developed in Unity, and the new emotion recognition (ER) System developed in Python. Using a face detection algorithm and a convolutional neural network (CNN), the ER System is able to detect the person’s face in the image captured by the UAV’s on-board camera and classify the emotion among seven possible ones (surprise, fear, happiness, sadness, disgust, anger or neutral expression). The experimental results demonstrate the correct integration of this new computer vision module within the VR platform, as well as the good performance of the designed CNN, with around 85% in the F1-score, a mean of the precision and recall of the model. The developed emotion detection system can be used in the future implementation of the assistance UAV that monitors dependent people in a real environment, since the methodology used is valid for images of real people.

Solar powering the increasing fleet of electrical vehicles (EV) demands more surface area than may be available for photovoltaic (PV) powered buildings. Parking lot solar canopies can provide the needed area to charge EVs, but are substantially costlier than roof- or ground-mounted PV systems. To provide a lower-cost PV parking lot canopy to supply EV charging beneath them, this study provides a full mechanical and economic analysis on three novel PV canopy systems: (1) exclusively wood, single parking spot spanning system, (2) wood and aluminum double parking spot spanning system, and (3) wood and aluminum cantilevered system for curbside parking. All systems can be scalable to any amount of EV parking spots. The complete designs and bill of materials (BOM) of the canopies are provided along with basic instructions and are released with an open source license that will enable anyone to fabricate them. The results found single-span systems have cost savings of 82%-85%, double-span systems save 43%-50%, and cantilevered systems save 31%-40%. In the first operation year, the PV canopies can provide 157% of energy needed to charge the least efficient EV currently on the market if it is driven the average driving distance in London ON, Canada.

The paper presents the measurement campaign carried out on a real-world motorway stretch of Hungary with the participation of both industrial and academic partners from Austria and Hungary. The measurement included vehicle based as well as infrastructure based sensor data. The obtained results will be extremely useful for future automotive R&amp;D activities due to the available ground truth for static and dynamic content. The aim of the measurement campaign was twofold. On the one hand, road geometry was mapped with high precision in order to build Ultra High Definition (UHD) map of the test road. On the other hand, the vehicles - equipped with differential Global Navigation Satellite Systems (GNSS) for ground truth localization - carried out special test scenarios while collecting detailed data using different sensors. All test runs were recorded by both vehicles and infrastructure. As a complementary task, the available 5G network was monitored and tested. The paper also showcases application examples based on the measurement campaign data, in which the added value of having access to the ground truth labeling and the created UHD map of the motorway section becomes apparent. In order to present our work transparently, a part of the measured data have been shared openly such that interested automotive as well as academic parties may use it for their own purposes.

This paper presents an optimized energy management strategy for Li-ion power batteries used on electric vehicles (EVs) at low temperatures. Under low-temperature environments, EVs suffer a sharp driving range loss resulted from the energy and power capability reduction of the battery. Simultaneously, because of Li plating, battery degradation becomes an increasing concern as temperature drops. All these factors could greatly increase the total vehicle operation cost. Prior to battery charging and vehicle operating, preheating battery to a battery-friendly temperature is an approach to promote energy utilization and reduce total cost. Based on the proposed LiFePO4 battery model, the total vehicle operation cost under certain driving cycles is quantified in the present paper. Then given a certain ambient temperature, a target temperature of preheating is optimized under the principle of minimizing total cost. As for the preheating method, a liquid heating system is also implemented on an electric bus. Simulation results show that the preheating process becomes increasingly necessary with a decreasing ambient temperature; however, the preheating demand declines as driving range grows. Vehicle tests verify that the preheating management strategy proposed in this paper is able to save total vehicle operation cost.

The importance of this study may be defined by using the smart techniques to earned value indicators of residential buildings projects in Republic of Iraq, only one development intelligent forecasting model was presented to predict Schedule Performance Index (SPI), Cost Performance Index (CPI), and To Complete Cost Performance Indicator (TCPI) are defined as the dependent. The approach is principally influenced by the determining numerous factors which effect on the earned value management, that involves Iraqi historical data. In addition, six independent variables (F1: BAC, Budget at Completion., F2: AC, Actual Cost., F3, A%, Actual Percentage., F4: EV, Earned Value. F5: P%, Planning Percentage., and F6: PV, Planning Value) were arbitrarily designated and satisfactorily described for per construction project. It was found that ANN has the capability to envisage the dust storm with a great accuracy. The correlation coefficient (R) has been 90.00%, and typical accuracy percentage has been 89.00%.

Large Language Models (LLMs) have proved significant proficiency when comes to code generation especially in Structured Query Language (SQL) for databases and recent successful Text-to-SQL method involves fine-tuning pre-trained LLMs for SQL generation tasks. Transforming natural language text into SQL queries, has been attempted to solve with various learning techniques including Few-shot learning[1], fine tuning. In this paper we propose Supervised fine-tuning (SFT) as a better alternative for learning technique for text-to-SQL generation task using Code-Llama that pushes state of art accuracy on spider test suite to 89.6% on dev set which represent first instance of surpassing the earlier best-in-class with 5.5% higher score and 86.8% of exact match accuracy on dev set. Furthermore, we demonstrate that properly prompted LLM along with SFT provides far fewer hallucinations and much more robust LLM that can be used as a general tool for any text-to-SQL generation use case.

Given the high number of vehicle-crash victims, it has been established as a priority to reduce this figure in the transportation sector. For this reason, many of the recent researches are focused on including control systems in existing vehicles, to improve their stability, comfort and handling. These systems need to know in every moment the behavior of the vehicle (state variables), among others, when the different maneuvers are performed, to actuate by means of the systems in the vehicle (brakes, steering, suspension) and, in this way, to achieve a good behavior. The main problem arises from the lack of ability to directly capture several required dynamic vehicle variables, such as roll angle, from low-cost sensors. Previous studies demonstrate that low-cost sensors can provide data in real-time with the required precision and reliability. Even more, other research works indicate that neural networks are efficient mechanisms to estimate roll angle. Nevertheless, it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by neural networks can fulfill the reliability and appropriateness requirements for using these technologies to improve overall safety in production vehicles. Because of the increasing of computing power, the reduction of consumption and electric devices size, along with the high variety of communication technologies and networking protocols using Internet have yield to Internet of Things (IoT) development. In order to address this issue, this study has two main goals: 1) Determine the appropriateness and performance of neural networks embedded in low-cost sensors kits to estimate roll angle required to evaluate rollover risk situations. 2) Compare the low-cost control unit devices (Intel Edison and Raspberry Pi 3 Model B), to provide the roll angle estimation with this artificial neural network-based approach. To fulfil these objectives an experimental environment has been set up composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model B, low cost Inertial Measurement Unit (BNO055 - 37€) and GPS (Mtk3339 - 53€) and the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module. This experimental environment will be tested in different maneuvers for comparison purposes. Neural networks embedded in low-cost sensor kits provide roll angle estimations very approximated to real values. Even more, Intel Edison and Raspberry Pi 3 Model B have enough computing capabilities to successfully run roll angle estimation based on neural networks to determine rollover risks situation fulfilling real-time operation restrictions stated for this problem.

The emerging working mode of coordinated trucks and drones has demonstrated significant practical potential in various fields, including logistics and delivery, intelligence surveillance reconnaissance, area monitoring, and patrol. The seamless collaboration between trucks and drones is garnering widespread attention in academia and has emerged as a key technology for achieving efficient and secure transportation. This paper provides a comprehensive and in-depth review of the research status on the routing problem for coordinated trucks and drones, covering aspects such as application background, cooperative modes, configurations, issues have been taken into consideration, and solution methodologies.

The advancement of autonomous driving technology is becoming increasingly vital in the modern technological landscape, promising notable enhancements in safety, efficiency, traffic management, and energy use. Despite these benefits, conventional deep reinforcement learning algorithms often struggle to navigate complex driving environments effectively. To tackle this challenge, we propose a novel network called DynamicNoise, designed to significantly boost algorithmic performance by introducing noise into the Deep Q-Network (DQN) and Double Deep Q-Network (DDQN). Drawing inspiration from the NoiseNet architecture, DynamicNoise uses stochastic perturbations to improve the exploration capabilities of these models, leading to more robust learning outcomes. Our experiments demonstrate a 57.25% improvement in navigation effectiveness within a 2D experimental setting. Moreover, by integrating noise into the action selection and fully connected layers of the Soft Actor-Critic (SAC) model in the more complex 3D CARLA simulation environment, our approach achieved an 18.9% performance gain, substantially surpassing traditional methods. These results confirm that the DynamicNoise network significantly enhances the performance of autonomous driving systems across various simulated environments, regardless of their dimensionality and complexity, by improving their exploration capabilities rather than just their efficiency.

The automotive industry is increasingly adopting laminated glass in side and rear windows to comply with safety standards aimed at reducing ejection injuries. However, this trend may have unintended consequences for occupant rescue and escape in submerged vehicle incidents. As more vehicles incorporate laminated glass, concerns arise that they could become "glass coffins" in water, making it more difficult for occupants to exit or be rescued. The use of laminated glass is driven by Federal Motor Vehicle Safety Standard 226 (FMVSS 226) for ejection mitigation, but it does not address the risk of vehicle submersion and occupant drowning. Drowning accounts for a substantial portion of vehicle-related fatalities. As the adoption of laminated glass grows, there is a need for drowning prevention measures, such as automatic window opening systems, to mitigate the risk of vehicles becoming "glass coffins" in water. A comprehensive approach addressing both ejection and submersion dangers is necessary to maximize occupant protection and save lives.

In this paper, we examined the temperature variations of key constituents during the operational cycle of a custom-built electric vehicle. The vehicle was made employing a motor of 10kW, a KLS7275D controller, and a lithium-ion battery. Temperature sensors for monitoring was affixed to the motor, controller, and battery, facilitating a comprehensive analysis of thermal behaviors. A controlled 20 minute driving tests encompassing both high and low speeds were executed to scrutinize temperature variations. Our findings reveal that the controller exhibited the most substantial temperature fluctuation compared to the battery and motor. Results shows the importance of thermal management strategies for components to ensure optimal driving performance and component reliability in electric vehicles.

To fulfil a need of limitation automotive emissions the reduction of vehicle weight is widely recommended and achieved in many ways, both by the construction of individual elements of the vehicle, and by the selection of light materials including Al alloys. Connecting these elements with each other and with elements made of iron alloys can be realized, inter alia, by welding or stir welding. However, the quality of the welds obtained varies widely and depends on many design, operational and environmental factors. The present study focused on a review of various welding technics used to joint both similar and dissimilar Al alloys utilized in automotive industry, the effect of various process parameters on welds quality, and phenomena appeared in such welds..

Zero-emission trucks for regional and long haul missions are an option for fossil-free freight. The viability of such powertrains and system solutions was studied conceptually in project ESCALATE for trucks with GVW of 40 tonnes and beyond through various prime mover combinations. The study covers battery and fuel cell power sources with different degrees of battery electric as well as H2 and fuel cell operation. As a design basis, two different missions with a single-charge/H2 refill were analysed. The first mission was the VECTO long haul profile repeated up to 750 km, whereas the second was a real 520 km on-road mission in Finland. Based on the simulated energy consumption on the driving cycle, on-board energy demand was estimated, and the initial single-charge operational scenarios were analysed with five different power source topologies. The traction motors of the tractor were dimensioned so that a secondary mission of GVW up to 76 tons on a shorter route can be operated. Based on the powertrain and vehicle model, various infrastructure options for charging and H2 refuelling strategy as well as various operative scenarios with indicative total cost of ownership (TCO) were analysed.

Nowadays, people like to travel to alleviate the stress and depression in their busy lives, and if they can do so at a low cost, it will be even more beneficial to them. The usual way for a traveler to book a ticket or to contact a travel agency is to book a ticket. It becomes more expensive for travelers. Due to the lack of vehicle management, travel agencies don’t get their appropriate profit. In this journal, we have provided proficient solutions for vehicle optimization to minimize travel costs. However, when travelers want to travel, they don’t find a proper tour guide, and it is sometimes expensive to arrange a proper tour guide. Moreover, travelers often don’t find a suitable package, and occasionally they do not know the place where they may want to visit. In this paper, we have discussed how we used effective methods to reduce vehicle costs for tour purposes. Further, we have developed a solution where bus seats are optimally occupied, and the package system and tour guide are included in it. We have also added a blog portal to our web-based software to introduce all tourist places and their information. In this journal, the proposed optimal vehicle usage system has been developed using Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), the Tailwind Framework, React, and Node.js.

Contract violations, uncontracted load, and the alternating nature of electricity are the key causes of the frequency irregularity and Unscheduled Interchange (UI) / Deviation Settlement pricing in a restructured organized smart grid. These variations cause the frequency to fluctuate. The topic of frequency variation is discussed in this paper under various market circumstances. To correct frequency discrepancies under various market conditions, a fleet of electrical vehicles (EV) is offered as a distributed energy storage (DES). A bidirectional charger with a PI controller is suggested for the EV bidirectional power transfer. It is suggested to use a fuzzy logic (FL) controller to modify the proportional-integral (PI) controller gains. It has been noted that utilizing such a new approach lowers the UI prices.

The development of autonomous vehicles is becoming increasingly popular and gathering real world data is considered a valuable task. Many datasets have been published recently in the autonomous vehicle sector, with synthetic datasets gaining particular interest due to availability and cost. For a real implementation and correct evaluation of vehicles at higher levels of autonomy it is also necessary to consider human interaction, which is precisely something that lacks in existing datasets. In this article the UPCT dataset is presented, a public dataset containing high quality, multimodal data obtained using state of the art sensors and equipment installed onboard the UPCT’s CICar autonomous vehicle. The dataset includes data from a variety of perception sensors including 3D LiDAR, cameras, IMU, GPS, encoders, as well as driver biometric data and driver behaviour questionnaires. In addition to the dataset, the software developed for data synchronisation and processing has been made available. The quality of the dataset was validated using an end-to-end neural network model with multiple inputs to obtain speed and steering wheel angle and obtained very promising results.

The performance of the absolute atom gravimeters used on moving platforms, such as vehicles, ships and aircrafts, is strongly affected by the vibration noise. To suppress its influence, we summarize a vibration compensation method utilizing data measured by a classical accelerometer. The measurements with the accelerometer show that the vibration noise in the vehicle can be 2 order of magnitude greater than that in the lab during daytime, and can induce an interferometric phase fluctuation with a standard deviation of 16.70π. With the compensation method, our vehicle-mounted atom gravimeter can work normally in these harsh conditions. Comparing the Allan standard deviations before and after the vibration noise correction, we find a suppression factor of 22.74 can be achieved in static condition with an interrogation time of T = 20 ms, resulting a sensitivity of 1.35 mGal/Hz1/2, and a standard deviation of 0.5 mGal with an average time of 10 s. We also demonstrate the first test of an atom gravimeter in a moving vehicle, in which a suppression factor of 50.85 and a sensitivity of 60.88 mGal/Hz1/2 were realized with T = 5 ms.

Following electrification of automotive transport, studies on the penetration of Electric Vehicles (EVs) are widespread, especially in defined contexts, e.g. cities. As major transport hubs, airports fall within contexts worth of interest. In this work, a forecast of the demand for electric mobility in an Italian international airport (Rome, Fiumicino) is presented. First, a wide review of proposed sce-narios on the penetration of EVs at international and national level and available data on local automotive transport are presented, as preliminary study for the definition of reference scenarios for the local context. Then the methodology proposed is presented and applied to the specific case study. Finally, a preliminary sizing of the required charging infrastructure is reported. The proposed approach can be considered as reference for similar studies on electrical mobility in other airport areas around the world.

The research develops the vehicular routing problem for the distribution of refrigerated products in a multinational company. The mathematical model corresponds to the vehicle routing problem with hard time windows and stochastic service (VRPTW-ST) time model applied in the city of Santiago de Chile. For optimization of the model, optimization methods tabu search, chaotic search and general algebraic modeling were used. The results allow to validate the meta-heuristic chaotic search to solve the VRPTW-ST; chaotic search method obtains superior performance than tabu search method for solving a real problem in a large city.

Since the 1990’s, studies and pilot tests have been conducted to reduce traffic, accidents, and pollution due to urban freight transport (UFT). These ended up in several policies, regulations, and restrictions for UFT, such as low emission zones, delivery time windows, and vehicle size and weight restrictions. However, issues in UFT under regulatory measures still persevere. This study introduces an optimization framework for deriving an optimal combination of various types of vehicles with different capacities for vehicle replacement in UFT. This framework allows an understanding of how an urban freight company with a limited budget efficiently satisfies its freight demand within an urban area in the presence of regulatory measures by urban administrators. The introduced formulation, which is a mixed-integer linear programming, will assist the operator in choosing the best investment strategy for introducing new vehicles of certain types and sizes, for operation in different zones, into its fleet while gaining economic benefits and having a positive impact on the liveability of the urban area. Furthermore, a sensitivity analysis is performed to consider the effects of specific uncertain parameters on the total cost. The numerical results show that the share of electric vehicles in the fleet increases, and they are more competitive than diesel vehicles.

Recently, environmental degradation has become an international issue due to the extensive economic activities around the globe. This aspect demands the policymakers' and new researchers’ emphasis. Hence, the present article examines the impact of corporate social responsibilities (CSR) to community and environment on the environmental performance (ENP) of the manufacturing industry in China. The article also investigates the mediating role of green innovation and environmental strategy among CSR to community, CSR to environment and ENP of manufacturing industry in China. The article gets the primary data from the selected employees of the manufacturing industry using questionnaires. The article also tests the data's validity and reliability, and association among variables using smart-PLS. The outcomes indicated that CSR to environment has a positive association with ENP. The outcomes also revealed that green innovation and environmental strategy significantly mediate among CSR to community, CSR to environment and ENP. The article guides the policymakers in making policies related to improve ENP using CSR to environment and green innovation.

For decades, fossil fuels have been the backbone of reliable energy systems, offering unmatched energy density and flexibility. However, as the world shifts toward renewable energy, overcoming the limitations of intermittent power sources requires a bold reimagining of energy storage and integration. Power-to-X (PtX) technologies, which convert excess renewable electricity into storable energy carriers, offer a promising solution for long-term energy storage and sector coupling. Recent advancements in machine learning (ML) have revolutionized PtX systems by enhancing efficiency, scalability, and sustainability. This review provides a detailed analysis of how ML techniques, such as deep reinforcement learning, data-driven optimization, and predictive diagnostics, are driving innovation in Power-to-Gas, Power-to-Liquid, and Power-to-Heat systems. While ML applications have shown great potential in optimizing operational decisions and managing uncertainties in renewable energy integration, challenges such as data quality, real-time processing, and scalability remain. Addressing these gaps presents important future research opportunities. These advancements are critical to decarbonizing hard-to-electrify sectors such as heavy industry, transportation, and aviation, aligning with global sustainability goals.

This paper outlines a tool developed with the purpose of creating a simple configurable emulated network environment that can be used in cyber defence exercises. Research has been conducted into the various related subject areas: cyber defence exercises, network threats, network emulation, network traffic replay, network topologies, and common network services. From this research a requirements specification was produced to encapsulate the features required to create this tool. A network, containing many of the aspects researched, was designed and implemented using Netkit-NG to act as a blueprint for the tool and to further knowledge in the construction of an emulated network. Following this the tool was developed and tested to ensure requirements were met.

An emissions baseline for a city is the foundation to building an informed clean air action plan. This includes an understanding of the sources and their intensities to device cost-effective measures with the least health impacts. In an urban environment, emissions from road transport is one of the major contributors to the air pollution problem, and accounting for this sector requires a detailed profile of in-use vehicles, covering their age-mix, usage, and fuel economy. In this paper, we present a simplified framework to survey at select fuel stations to establish the necessary inputs and use them to build a localized vehicle emissions inventory. The methodology is illustrated with an application in the city of Patna, India. Here, the FuSS was employed to profile the in-use passenger vehicle characteristics by 19 trained students, over nine days and at ten fuel stations, to collect 9,775 survey points. The methodology outlined in this paper, along with the tools and training framework, is also replicable in settings other than the fuel stations.

This paper describes a novel thruster fault-tolerant control system (FTC) for open-frame remotely operated vehicles (ROVs). The proposed FTC consists of two subsystems: a model-free thruster fault detection and isolation subsystem (FDI) and a fault accommodation subsystem (FA). The FDI subsystem employs fault detection units (FDUs), associated with each thruster, to monitor their state. The robust, reliable and adaptive FDUs use a model-free pattern recognition neural network (PRNN) to detect internal and external faulty states of the thrusters in real time. The FA subsystem combines information provided by the FDI subsystem with predefined, user-configurable actions to accommodate partial and total faults and to perform an appropriate control reallocation. Software-level actions include penalisation of faulty thrusters in solution of control allocation problem and reallocation of control energy among the operable thrusters. Hardware-level actions include power isolation of faulty thrusters (total faults only) such that the entire ROV power system is not compromised. The proposed FTC system is implemented as a LabVIEW virtual instrument (VI) and evaluated in virtual (simulated) and real-world environments. The proposed FTC module can be used for open frame ROVs with up to 12 thrusters: eight horizontal thrusters configured in two horizontal layers of four thrusters each, and four vertical thrusters configured in one vertical layer. Results from both environments show that the ROV control system, enhanced with the FDI and FA subsystems, is capable of maintaining full 6 DOF control of ROV in the presence of up to 6 simultaneous total faults in the thrusters. With the FDI and FA subsystems in place the control energy distribution of the healthy thrusters is optimised so that the ROV can still operate in difficult conditions under fault scenarios.

: Biological networks such as protein interaction networks, gene regulation networks and metabolic pathways are examples of complex networks which are large graphs with small-world and scale-free properties. Analysis of these networks has a profound effect on our understanding the origins of life, health and disease states of organisms, and diagnose diseases to aid the search for remedial processes. In this review, we describe main analysis methods of biological networks using graph theory by first defining main parameters such as clustering coefficient, modularity and centrality. We then survey fundamental graph clustering methods and algorithms followed by the network motif search algorithms with the aim of finding repeating subgraphs in a biological network graph. A frequently appearing subgraph usually conveys a basic function carried out by that small network and discovering such a function provides an insight to the overall function of the organism. Lastly, we review network alignment algorithms that achieve to find similarities between two or more graphs representing biological networks. A conserved subgraph between the biological networks of organisms may mean a common ancestor and finding such relationship may help researchers derive ancestral relationships and predict the future evolution of organisms to enable designing new drugs. We conclude by the current challenging areas of biological network analysis using graph theory and parallel processing for high performance analysis

In recent years, the unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) has become a highly sought-after topic for its wide applications in the field of target recognition, detection, and tracking. However, SAR automatic target recognition (ATR) models based on deep neural networks (DNN) are suffering from adversarial examples. Generally, non-cooperators rarely disclose any information about SAR-ATR models, making adversarial attacks challenging. In this situation, we propose Transferable Adversarial Network (TAN) to attack these models with highly transferable adversarial examples. The proposed method improves the transferability via a two-player game, in which we simultaneously train two encoder-decoder models: a generator that crafts malicious samples through a one-step forward mapping from original data, and an attenuator that weakens the effectiveness of malicious samples by capturing the most harmful deformations. In particular, compared to traditional iterative methods, our approach is able to one-step map original samples to adversarial examples, thus enabling real-time attacks. Experimental results indicate that the proposed approach achieves state-of-the-art transferability with acceptable adversarial perturbations and minimum time costs compared to existing attack methods, i.e., it excellently realizes real-time transferable adversarial attacks.

This paper proposes an optimal path type classification scheme for the three-point Dubins problem. It allows to directly extract the shortest path type from a Dubins set, evaluating only the relative initial and final configurations with the via point position using a suitable partition of the Cartesian plane. Two alternative approaches are proposed to address the problem: an analytical approach and a heuristic one. The latter was revealed to be much faster from a computational point of view. The proposed classification logic makes the path planning for the three-point Dubins problem much more effective and suitable for real-time applications. Numerical examples are provided to show the efficiency of the proposed strategy.

Introduction: Most industrialized and developed countries now working to implement more advanced transportation systems. These include autonomous vehicle(AV) systems of various forms, such as delivery vehicles, which eliminate the crashes, injuries, and fatalities associated with human-operated machinery. Self-driving vehicles have the potential to make roads safer, but some safety concerns need to be addressed. Before AV systems become commonplace in developing countries, society needs to determine how safe they truly are. Objectives: This paper provides brief descriptions and examines the realities for AV in developing countries using results from infrastructure-based assessment of countries worldwide and from a questionnaire-based survey. Methods: Relevant research papers, articles, reports, and departmental websites were used as the main data sources. Ethiopia was chosen as a representative case. Different aspects of AV infrastructure, its current status in developing countries, and perspectives on AV systems are explored through interviews with approximately 1500 randomly selected individuals. The main variables in the assessment were physical infrastructure, digital infrastructure, and societal perceptions on AV systems. Results: Surprisingly, the results indicated that 55.82%, 78.58%, 87.78%, and 86.09% of Ethiopian interviewees agreed with statements regarding high ease of use, usefulness, positive attitude, and benefits, respectively, of AV. However, it should be noted that some interviewees did not provide a response to certain questions.

Computational animation of spatiotemporal models for 3-D shape and motion is considered as core of dynamic real-time vision. This is based on sets of features evaluated from multi-focal image streams. Subjects are defined as special objects capable of sensing environmental parameters and of initiating own actions in combination with stored knowledge. Object/subject recognition and scene understanding are achieved on different levels. Multiple objects are tracked individually for perceiving their actual state (‘here and now’). Fast saccadic jumps in gaze direction allow flexible concentration on objects or parts of actual interest. By analyzing motion of relevant objects/subjects over a larger time scale on the level of state variables and documenting them in the ‘scene tree representation’ (computer graphics), the situation with respect to decision making is assessed. Various behavioral capabilities of subjects are represented on an abstract level for characterizing their potential behaviors. These are generated by stereotypical feed-forward and feedback control applications on a separate level close to the actuator hardware with corresponding methods. This dual representation on one level for decision making and one on the implementation level allows for flexibility and easy adaptation or extension. Results are shown for road vehicle guidance based on three cameras on a gaze-controlled platform.

ABSTRACT The work is focused on determining the energy transfer from EV’s to grid for different operating conditions. The study has been simulated on a scaled model and later validated through experimental tests on batteries of 20 kWh, 40 kWh, 60 kWh and 80 kWh for EV’s energy rates from 130 Wh/km to 180 Wh/km. Tests have been developed to evaluate the energy transfer from driving distance between 0 and 50 km. Results have proved there is a linear dependence between energy transfer and energy rate. Experimental and simulated values have been correlated showing a 99.6% accuracy. The ratio of energy transfer to battery energy capacity depends not only on this latter parameter, but also on the energy rate of the electric vehicle. Experimental tests have shown a variation from a minimum transfer ratio of 54.1% for the highest electric vehicle energy rate and lowest battery energy capacity and power transfer to grid rate to a maximum of 88.6% for the highest power transfer to grid rate and battery energy capacity and lowest electric vehicle energy rate.

Effective agricultural management in maize production operations starts with the early quantification of seedlings. Accurately determining plant presence allows growers to optimize planting density, allocate resources, and detect potential growth issues early on. This study presents an in-depth analysis of multiple object detection models used by drones to count maize seedlings, while concurrently investigating the influence of planting density, flight altitude, and plant growth stage. The findings of this study demonstrate that one-stage models are able to more accurately detect maize seedlings than two-stage models. In particular, YOLOv8n exhibits outstanding performance, achieving an F1-score of over 0.92 under various density conditions and maintaining a stable performance, especially under densities below 105,000 plants/ha. Additionally, planting density and growth stage were observed to significantly affect detection accuracy, with performance declining as density and growth stage increased. Image resolution and detection were impacted by flight altitude, with lower flights producing higher-quality results. Ultimately, YOLOv8n displays optimal performance under various conditions, providing crucial data to support intelligent decision-making. This research offers valuable insights into the application of object detection technology and provides a basis for the future development of precision agriculture.

The predominance of traffic lights in urban settings often induces fluctuations in traffic patterns and energy utilization among vehicles. To counteract the adverse effects of traffic lights on the energy efficiency of electric vehicles (EVs), a Multi-Intersections-Based Eco-Approach and Departure strategy (M-EAD) is proposed. This strategy aims to enhance vehicle energy efficiency, traffic flow, and battery longevity, all while upholding satisfactory driving comfort. The M-EAD strategy unfolds in two distinct stages: the optimization of an eco-friendly green signal window and the refinement of speed trajectories. The initial stage tackles the optimization of traffic light green signal windows, underpinned by the minimization of travel delays via solving the shortest path problem. In the subsequent stage, a receding horizon framework takes center stage, leveraging an iterative dynamic programming algorithm to tackle the speed optimization challenge. The objective here is to curtail energy consumption and reduce battery wear by finding an optimal speed trajectory. Furthermore, the real-world efficacy of this approach is substantiated through on-road vehicle tests, attesting to its viability in actual road scenarios.

In current electric vehicles, the traction battery is intended to store energy. When designing this battery different parameters must be taken into account in order to arrange the battery/module/cells in the mechanically and thermally safest configuration. Moreover, the battery layout must have a correct dynamic behavior in possible collisions. In the present study, different battery configurations plus added energy absorbers are analyzed. In order to do that, an internal combustion vehicle modeled with finite elements is applied as the reference model. The structural behavior of the different battery configurations in the event of a side collision is examined. Firstly, the safest arrangement is stablished, both with respect to cabin intrusion and thermal runaway propagation. Secondly, the safest arrangement that guarantees the safety of the occupants in the event of a side collision is analyzed but using the MADYMO. This software includes experimentally validated dummies which allow have an insight in the stresses experienced by occupants. On the one hand, the analysis states that the battery pack inclusion in the vehicle increases the stiffness of the car floor, resulting in fewer intrusions in the passenger compartment. Therefore, a greater safety for the occupants is reached. On the other hand, none of the configurations analyzed has shown enough safety against the phenomenon of Thermal Runaway.

This paper develops a two-layer optimization approach that provides energy-optimal control for vehicles and traffic signal controllers. The optimizer in the first layer computes the traffic signal timings to minimize the total energy consumption levels of approaching vehicles from upstream traffic. The traffic signal optimization can be easily implemented in real-time signal controllers, and it overcomes the issues in the traditional Webster’s method of overestimating the cycle length when the traffic volume-to-capacity ratio exceeds 50 percent. The second layer optimizer is the vehicle speed controller, which calculates the optimal vehicle brake and throttle levels to minimize the energy consumption of individual vehicles. The A-star dynamic programming is used to solve the formulated optimization problem in the second layer to expedite the computation speed so that the optimal vehicle trajectories can be computed in real-time and can be easily implemented in simulation software for testing. The proposed integrated controller is first tested on an isolated signalized intersection, and then an arterial network with multiple intersections to investigate the performance of the proposed controller under various traffic demand levels. The test results demonstrate that the proposed integrated controller can greatly improve energy efficiency with fuel savings up to 17.7%, at the same time enhancing traffic mobility by up to 47.18% reduction in traffic delay and up to 24.84% reduction in vehicle stops.