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It is of utmost importance for advanced driver assistance systems to evaluate the risk of the current situation and make continuous decisions about what kind of evasive maneuver can be initiated. The purpose of this paper is to establish efficient indicators to evaluate the risk of candidate driving maneuvers for a human-in-the-loop vehicle. A novel safe driving envelope generation method is proposed, which takes various constraints into consideration, including the human operation, vehicle motion limits, and collision avoidance with road boundary and obstacles. The efficiency of the proposed method is validated by simulation experiments and real vehicle tests. The results show that the feasibility of candidate driving maneuvers can be efficiently determined by computing the driving envelope, and the proposed driving envelope method can be easily implemented for real-time applications. [ABSTRACT FROM AUTHOR]

In this article, methods for the attitude control optimization of large-load plant-protection quadrotor unmanned aerial vehicles (UAVs) are presented. Large-load plant-protection quadrotors can be defined as quadrotors equipped with sprayers and a tank containing a large amount of water or pesticide, allowing the quadrotors to water plants or spray pesticide during flight. Compared to the control of common small quadrotors, two main points need to be considered in the control of large-load plant-protection quadrotors—first, the water in the tank gradually diminishes during flight and the physical parameters change during this process. Second, the size and mass of the rotors are especially large, which greatly slows the response rate of the rotors. We present an extended-state reinforcement learning (RL) algorithm to solve these problems. The moment of inertia (MOI) of the three axes and the dynamic response constant of the rotors are included in the state list of the quadrotor during the training process, so that the controller can learn these changes in the models. The controlling laws are automatically generated and optimized, which greatly simplifies the tuning process compared to those of traditional control algorithms. The controller in this article is tested on a 10 kg class large-load plant-protection quadrotor, and the flight performance verifies the effectiveness of our work. [ABSTRACT FROM AUTHOR]

Featured Application: This proofing of concept may serve as evidence for e.g., Traffic Police of the Czech Republic about the high efficiency, suitability, and usability of RPAS during the traffic accident documentation process. Traffic accident documentation worldwide is generally done by conventional methods (e.g., surveying wheel and steel tapeline) followed by sketch and plan creation. These conventional methods are time-consuming and inaccurate; therefore, they can possibly be replaced by advanced modern methods, using the remotely piloted aircraft system (RPAS) for obtaining data, as well as the structure from motion (SfM) for processing. For the proof of concept, we designed and performed an experiment using low-cost multi-rotor RPAS on an arranged traffic accident involving three cars. The result of this experiment was an accurate 3D model with the possibility of precise measurement. The differences in distances and reference objects' dimensions were in general below 1% compared to real values. The presented method is time-saving (requiring 85% less time), more precise, and more efficient compared to conventional methods. Moreover, it allows better data reusability, thus is very suitable and can be recommended for usage in routine situations in traffic accident documentation. [ABSTRACT FROM AUTHOR]

In this paper, an urban-based path planning algorithm that considered multiple obstacles and road constraints in a university campus environment with an autonomous micro electric vehicle (micro-EV) is studied. Typical path planning algorithms, such as A*, particle swarm optimization (PSO), and rapidly exploring random tree* (RRT*), take a single arrival point, resulting in a lane departure situation on the high curved roads. Further, these could not consider urban-constraints to set collision-free obstacles. These problems cause dangerous obstacle collisions. Additionally, for drive stability, real-time operation should be guaranteed. Therefore, an urban-based online path planning algorithm, which is robust in terms of a curved-path with multiple obstacles, is proposed. The algorithm is constructed using two methods, A* and an artificial potential field (APF). To validate and evaluate the performance in a campus environment, autonomous driving systems, such as vehicle localization, object recognition, vehicle control, are implemented in the micro-EV. Moreover, to confirm the algorithm stability in the complex campus environment, hazard scenarios that complex obstacles can cause are constructed. These are implemented in the form of a delivery service using an autonomous driving simulator, which mimics the Chungbuk National University (CBNU) campus. [ABSTRACT FROM AUTHOR]

The new progressive smart technologies announced in the fourth industrial revolution in aviation—Aviation 4.0—represent new possibilities and big challenges in aircraft maintenance processes. The main benefit of these technologies is the possibility to monitor, transfer, store, and analyze huge datasets. Based on analysis outputs, there is a possibility to improve current preventive maintenance processes and implement predictive maintenance processes. These solutions lower the downtime, save manpower, and extend the components' lifetime; thus, the maximum effectivity and safety is achieved. The article deals with the possible implementation of an unmanned aerial vehicle (UAV) with an infrared camera and Radio Frequency Identification (RFID) as two of the smart hangar technologies for airframe condition monitoring. The presented implementations of smart technologies follow up the specific results of a case study focused on trainer aircraft failure monitoring and its impact on maintenance strategy changes. The case study failure indexes show the critical parts of aircraft that are subjected to damage the most. The aim of the article was to justify the need for thorough monitoring of critical parts of the aircraft and then analyze and propose a more effective and the most suitable form of technical condition monitoring of aircraft critical parts. The article describes the whole process of visual inspection performed by an unmanned aerial vehicle (UAV) with an IR camera and its related processes; in addition, it covers the possible usage of RFID tags as a labeling tool supporting the visual inspection. The implementations criteria apply to the repair and overhaul small aircraft maintenance organization, and later, it can also increase operational efficiency. The final suggestions describe the possible usage of proposed solutions, their main benefits, and also the limitations of their implementations in maintenance of trainer aircraft. [ABSTRACT FROM AUTHOR]

Path planning research plays a vital role in terms of safety and comfort in autonomous driving systems. This paper focuses on safe driving and comfort riding through path planning in autonomous driving applications and proposes autonomous driving path planning through an optimal controller integrating obstacle-dependent Gaussian (ODG) and model prediction control (MPC). The ODG algorithm integrates the information from the sensors and calculates the risk factors in the driving environment. The MPC function finds vehicle control signals close to the objective function under limited conditions, such as the structural shape of the vehicle and road driving conditions. The proposed method provides safe control and minimizes vehicle shaking due to the tendency to respond to avoid obstacles quickly. We conducted an experiment using mobile robots, similar to an actual vehicle, to verify the proposed algorithm performance. The experimental results show that the average safety metric is 72.34%, a higher ISO-2631 comport score than others, while the average processing time is approximately 14.2 ms/frame. [ABSTRACT FROM AUTHOR]

Featured Application: Structural Dynamic Responses of Precast Slab Track systems. Recently, precast slab tracks have been used widely in railway applications, especially in conventional urban railway lines. These types of tracks are rapidly constructed and limit interruptions to train operation. However, the problems of dynamic stability when the trains run on the discontinuous type of tracks must be seriously considered. This paper focuses on analyzing the train-track interaction in two types of tracks under the dynamic load by using the numerical analysis program (APATSI) to evaluate the structural response as well as the running safety to precisely understand the load transfer efficiency of precast slab track systems. [ABSTRACT FROM AUTHOR]

Wi-Fi sensing technology based on deep learning has contributed many breakthroughs in gesture recognition tasks. However, most methods concentrate on single domain recognition with high computational complexity while rarely investigating cross-domain recognition with lightweight performance, which cannot meet the requirements of high recognition performance and low computational complexity in an actual gesture recognition system. Inspired by the few-shot learning methods, we propose WiGR, a Wi-Fi-based gesture recognition system. The key structure of WiGR is a lightweight few-shot learning network that introduces some lightweight blocks to achieve lower computational complexity. Moreover, the network can learn a transferable similarity evaluation ability from the training set and apply the learned knowledge to the new domain to address domain shift problems. In addition, we made a channel state information (CSI)-Domain Adaptation (CSIDA) data set that includes channel state information (CSI) traces with various domain factors (i.e., environment, users, and locations) and conducted extensive experiments on two data sets (CSIDA and SignFi). The evaluation results show that WiGR can reach 87.8–94.8% cross-domain accuracy, and the parameters and the calculations are reduced by more than 50%. Extensive experiments demonstrate that WiGR can achieve excellent recognition performance using only a few samples and is thus a lightweight and practical gesture recognition system compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Multirotor drones are widely used currently in several areas of life. Their suitable size and the tasks that they can perform are their main advantages. However, to the best of our knowledge, they must be controlled via remote control to fly from one point to another, and they can only be used for a specific mission (tracking, searching, computing, and so on). In this paper, we intend to present an autonomous UAV based on Raspberry Pi and Android. Android offers a wide range of applications for direct use by the UAV depending on the context of the assigned mission. The applications cover a large number of areas such as object identification, facial recognition, and counting objects such as panels, people, and so on. In addition, the proposed UAV calculates optimal trajectories, provides autonomous navigation without external control, detects obstacles, and ensures live streaming during the mission. Experiments are carried out to test the above-mentioned criteria. [ABSTRACT FROM AUTHOR]

Marine transportation is considered to be one of the most important aspects of global transportation services. Due to the increase in marine transportation, there are significant impacts on the marine environment. One of the possible measures for mitigation of the environmental impact could be switching to environmentally friendly fuel. However, the alternative fuel selection process is considered to be a problem due to various criteria to be considered and stakeholders that should be involved in the selection process. The aim of this paper is to demonstrate the application of multicriteria analysis as a decision-support tool for the alternative marine fuel selection problem in coastal marine traffic. The suggested methodology takes into account environmental, technological, and economic aspects, and ensures the participation of different stakeholders in the selection process. The priority ranking of the alternatives is based on a combination of the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW). The implementation of this method considers the involvement of relevant stakeholders through evaluation of the criteria weights and performance of each alternative with respect to each criterion. The method is applied for the case study of Croatia, where the results demonstrated that the best alternative for all stakeholders is electric propulsion, even though there are differences in opinions and perceptions with respect to the objectives and criteria. The findings of this analysis, likely the first of this type in this area, can serve as a solid basis for strategic planning. [ABSTRACT FROM AUTHOR]

A traffic simulation of the Jianghan Zone in Wuhan, China was carried out. In order to simulate genuine traffic flow without traditional hard-to-implement data collection methods, geographic population distribution data were gathered from the public information and traffic flow was generated by ActivityGen in SUMO (Simulation of Urban Mobility). For the sake of discovering the accuracy of the simulated traffic, real-time road condition and traffic prediction based on previous data on same time of each road in this area was compared. The results show that traffic flow generated from geographic population distribution data has referential meanings and with more detailed model classification, simulated traffic data can be closer to real conditions. This may offer a new way to generate traffic flow for researchers working in traffic simulation area. Further improvement of the accuracy in traffic flow generation by geographic population needs to pay more attention on special places like hospital and train stations. [ABSTRACT FROM AUTHOR]

The article presents the standard method of the evaluation of car drivers' psychomotor reaction times. A characteristic feature of this method is the ability to conduct tests using real vehicles equipped with mobile measuring apparatus. Measurements are carried out on people whose task is to initiate specific maneuvers (such as: a braking or turning maneuver, a combined braking and turning maneuver, as well as e.g., the use of an audio signal) in response to external stimuli coming from different directions. The sources of the stimuli are light signals emitted by variable message signs placed around the vehicle. The developed control and measurement equipment allows us to realize various and complex test scenarios. It generates stimulus sequences, assesses the correctness of the driver's response and measures its time. This method allows us to obtain sets of results from tests carried out under different conditions and variable test durations. The possibility of testing a large number of participants in a relatively short time and in repetitive conditions allows for reliable statistical inference. The paper presents examples of research results obtained on the basis of few thousand tests carried out on a large group of respondents. The registered data was statistically processed and referred to the literature. A high degree of correlation between the analyzed results and the literature reports proves that the presented method may be a source of data for the analysis of phenomena related to the time of the driver's response, especially the influence of various factors affecting its values. [ABSTRACT FROM AUTHOR]

This research aims to develop an automatic unmanned aerial vehicle (UAV)-based indoor environmental monitoring system for the acquisition of data at a very fine scale to detect rapid changes in environmental features of plants growing in greenhouses. Due to the complexity of the proposed research, in this paper we proposed an off-board distributed control system based on visual input for a micro aerial vehicle (MAV) able to hover, navigate, and fly to a desired target location without considerably affecting the effective flight time. Based on the experimental results, the MAV was able to land on the desired location within a radius of about 10 cm from the center point of the landing pad, with a reduction in the effective flight time of about 28%. [ABSTRACT FROM AUTHOR]

An approach based on Artificial Neural Networks is proposed in this paper to improve the localisation accuracy of Inertial Navigation Systems (INS)/Global Navigation Satellite System (GNSS) based aided navigation during the absence of GNSS signals. The INS can be used to continuously position autonomous vehicles during GNSS signal losses around urban canyons, bridges, tunnels and trees, however, it suffers from unbounded exponential error drifts cascaded over time during the multiple integrations of the accelerometer and gyroscope measurements to position. More so, the error drift is characterised by a pattern dependent on time. This paper proposes several efficient neural network-based solutions to estimate the error drifts using Recurrent Neural Networks, such as the Input Delay Neural Network (IDNN), Long Short-Term Memory (LSTM), Vanilla Recurrent Neural Network (vRNN), and Gated Recurrent Unit (GRU). In contrast to previous papers published in literature, which focused on travel routes that do not take complex driving scenarios into consideration, this paper investigates the performance of the proposed methods on challenging scenarios, such as hard brake, roundabouts, sharp cornering, successive left and right turns and quick changes in vehicular acceleration across numerous test sequences. The results obtained show that the Neural Network-based approaches are able to provide up to 89.55% improvement on the INS displacement estimation and 93.35% on the INS orientation rate estimation. [ABSTRACT FROM AUTHOR]

For handling the broken-down communication infrastructure when a disaster event happens, this paper proposes to dispatch the unmanned aerial vehicle (UAV) to the disaster area as the relay node, which further forms a Flying Ad hoc Network (FANET). Since the UAV only owns limited energy and a disaster event may need multiple UAVs to cover its area, an efficient multi-UAV dispatch algorithm is critical to recover the communication link of the disaster area. In this paper, we adopt the mobile ground control station (GCS) to transport UAVs to the boundary of the disaster area first. According to the UAV energy consumption rate during flight and two communication modes, the UAV charging progress, and the number of required UAVs of the event, the mobile GCS then executes the proposed energy-aware multi-UAV dispatch algorithm (EAMUD) to dispatch multiple UAVs to this disaster area for building the FANET. Hence, the broken-down link in the disaster area is recovered after the FANET connects to nearby network infrastructure. Further, we propose the multi-UAV handoff scheme and exception handling processes to replace energy-exhausted UAVs for maximizing the event communication time of the disaster event. Finally, we execute simulations for related work and four EAMUD variants under different parameter values in the real scenario. These results exhibit that EAMUD with the Postpone method (EAMUD-P) achieves the highest event communication time among all these schemes. [ABSTRACT FROM AUTHOR]

One of the strategies to control the spread of infectious diseases is based on the use of specialized applications for smartphones. These apps offer the possibility, once individuals are detected to be infected, to trace their previous contacts in order to test and detect new possibly-infected individuals. This paper evaluates the effectiveness of recently developed contact tracing smartphone applications for COVID-19 that rely on Bluetooth to detect contacts. We study how these applications work in order to model the main aspects that can affect their performance: precision, utilization, tracing speed and implementation model (centralized vs. decentralized). Then, we propose an epidemic model to evaluate their efficiency in terms of controlling future outbreaks and the effort required (e.g., individuals quarantined). Our results show that smartphone contact tracing can only be effective when combined with other mild measures that can slightly reduce the reproductive number R 0 (for example, social distancing). Furthermore, we have found that a centralized model is much more effective, requiring an application utilization percentage of about 50% to control an outbreak. On the contrary, a decentralized model would require a higher utilization to be effective. [ABSTRACT FROM AUTHOR]