Journal articles on the topic 'Autonomous vehicles (except systems infrastructures)'

Transport engineering has recently undergone several significant changes and innovations, one of which is the appearance and spread of autonomous vehicles; with this technology becoming more common and ordinary by the day, it is now necessary to implement some systems and contexts to facilitate autonomous vehicle operations. Consequently, a different perspective is now arising when dealing with road infrastructures, aiming to simplify and improve efficiency and maintenance of the existing roads, increase the life cycle of newly built ones, and minimize the economic and financial impact at the same time. Roadway pavements are one of the primary factors affecting vehicle operations; over time, this distinctive aspect has gone through various mechanical and physical changes due to the adoption of new materials or design methods. Consequently, to the spread of autonomous vehicles, scientific research has begun to study and develop systems to make road pavements and platforms not exclusively aimed at bearing loads, but rather at considering them as a means of communication and information exchange, if not even as a source of energy. This new approach introduces the so-called “Smart Roads,” i.e., road infrastructures capable of communicating with vehicles and self-monitoring fundamental perspectives concerning driverless vehicles and the roadway platform life cycle. This paper examines the characteristics of Smart Roads, considering their broad field of application and their potential advantages and drawbacks. This paper also pursues the objective of describing the global vision, the possible future direction of these innovations concerning the automotive and transport industries, and a particular focus on infrastructures and roadways.

The use of robotic systems, especially multi-rotor aerial vehicles, in the documentation of historical buildings and cultural heritage monuments has become common in recent years. However, the teleoperated robotic systems have significant limitations encouraging the ongoing development of autonomous unmanned aerial vehicles (UAVs). The autonomous robotic platforms provide a more accurate and safe measurement in distant and difficult to access areas than their teleoperated counterpart. Through the use of autonomous aerial robotic systems, access to such places by humans and building of external infrastructures like scaffolding for documentation purposes is no longer necessary. In this work, we aim to present a novel autonomous unmanned aerial vehicle designed for the documentation of hardly attainable areas of historical buildings. The prototype of this robot was tested in several historical monuments comprising scanned objects located in dark and hardly accessible areas in the upper parts of tall naves. This manuscript presents the results from two specific places: the Church of St. Anne and St. Jacob the Great in Stará Voda, and St. Maurice Church in Olomouc, both in the Czech Republic. Finally, we also compare the three-dimensional map obtained with the measurements made by the 3D laser scanner carried onboard UAV against the ones performed by a 3D terrestrial laser scanner.

Autonomous vehicles (AVs) can generate major changes in urban systems due to their ability to use road infrastructures more efficiently and shorten trip times. However, there is great uncertainty about these effects and about whether the use of these vehicles will continue to be private, in continuity with the current paradigm, or whether they will become shared (carsharing/ridesharing). In order to try to shed light on these matters, the use of a scenario-based methodology and the evaluation of the scenarios using a land use–transport interaction model (LUTI model TRANSPACE) is proposed. This model allows simulating the impacts that changes in the transport system can generate on the location of households and companies oriented to local demand and accessibility conditions. The obtained results allow us to state that, if AVs would generate a significant increase in the capacity of urban and interurban road infrastructures, the impacts on mobility and on the location of activities could be positive, with a decrease in the distances traveled, trip times, and no evidence of significant urban sprawl processes. However, if these increases in capacity are accompanied by a large augment in the demand for shared journeys by new users (young, elderly) or empty journeys, the positive effects could disappear. Thus, this scenario would imply an increase in trip times, reduced accessibilities, and longer average distances traveled, all of which could cause the unwanted effect of expelling activities from the consolidated urban center.

Connected Autonomous Vehicles (AVs) promise innovative solutions for traffic flow management, especially for congestion mitigation. Vehicle-to-Vehicle (V2V) communication depends on wireless technology where vehicles can communicate with each other about obstacles and make cooperative strategies to avoid these obstacles. Vehicle-to-Infrastructure (V2I) also helps vehicles to make use of infrastructural components to navigate through different paths. This paper proposes an approach based on swarm intelligence for the formation and evolution of platoons to maintain traffic flow during congestion and collision avoidance practices using V2V and V2I communications. In this paper, we present a two level approach to improve traffic flow of AVs. At the first level, we reduce the congestion by forming platoons and study how platooning helps vehicles deal with congestion or obstacles in uncertain situations. We performed experiments based on different challenging scenarios during the platoon’s formation and evolution. At the second level, we incorporate a collision avoidance mechanism using V2V and V2I infrastructures. We used SUMO, Omnet++ with veins for simulations. The results show significant improvement in performance in maintaining traffic flow.

Abstract In a production environment, there are several challenges in meeting the Industry 4.0 (I4.0) standard requirements. Energy efficiency is an essential area of focus. In the production setup, the critical and real-time control systems need to be very efficient while implementing functions, namely, accurate sensing, fast processing and precise actuation. Automated Guided vehicles (AGVs) and Automated Guided Vehicles are an integral part of modern and intelligent manufacturing systems. Power consumption in such systems is directly proportional to the performance level achieved. However, there is a need to evolve strategies to reduce power consumption and attain optimal performance. Field Programmable Gate Array(FPGA) based controller solutions can provide competent performance at optimized power consumption. The proposed work discusses the requirements of I4.0 concerning energy efficiency infrastructures for the intelligent manufacturing setup. The need to develop efficient subsystems for sensing, decision-making and actuation based on FPGA is stressed. Thus the focus is on the FPGA based power-efficient models used for sensor fusion technique in Autonomous Mobile Robots. The fundamentals of sensor fusion technique and the need to fuse sensor data for improved decision making and actuation are emphasized.

Autonomous vehicles (AVs) are already operating on the streets of many countries around the globe. Contemporary concerns about AVs do not relate to the implementation of fundamental technologies, as they are already in use, but are rather increasingly centered on the way that such technologies will affect emerging transportation systems, our social environment, and the people living inside it. Many concerns also focus on whether such systems should be fully automated or still be partially controlled by humans. This work aims to address the new reality that is formed in autonomous shuttles mobility infrastructures as a result of the absence of the bus driver and the increased threat from terrorism in European cities. Typically, drivers are trained to handle incidents of passengers’ abnormal behavior, incidents of petty crimes, and other abnormal events, according to standard procedures adopted by the transport operator. Surveillance using camera sensors as well as smart software in the bus will maximize the feeling and the actual level of security. In this paper, an online, end-to-end solution is introduced based on deep learning techniques for the timely, accurate, robust, and automatic detection of various petty crime types. The proposed system can identify abnormal passenger behavior such as vandalism and accidents but can also enhance passenger security via petty crimes detection such as aggression, bag-snatching, and vandalism. The solution achieves excellent results across different use cases and environmental conditions.

The industrial development of the last few decades has prompted an increase in the number of vehicles by multiple folds. With the increased number of vehicles on the road, safety has become one of the primary concerns. Inter vehicular communication, specially Vehicle to Everything (V2X) communication can address these pressing issues including autonomous traffic systems and autonomous driving. The reliability and effectiveness of V2X communication greatly depends on communication architecture and the associated wireless technology. Addressing this challenge, a device-to-device (D2D)-based reliable, robust, and energy-efficient V2X communication architecture is proposed with LoRa wireless technology. The proposed system takes a D2D communication approach to reduce the latency by offering direct vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, rather than routing the data via the LoRa WAN server. Additionally, the proposed architecture offers modularity and compact design, making it ideal for legacy systems without requiring any additional hardware. Testing and analysis suggest the proposed system can communicate reliably with roadside infrastructures and other vehicles at speeds ranging from 15–50 km per hour (kmph). The data packet consists of 12 bytes of metadata and 28 bytes of payload. At 15 kmph, a vehicle sends one data packet every 25.9 m, and at 50 kmph, it sends the same data packet every 53.34 m with reliable transitions.

Abstract The complexity and dynamics in groupage traffic require flexible, efficient, and adaptive planning and control processes. The general problem of allocating orders to vehicles can be mapped into the Vehicle Routing Problem (VRP). However, in practical applications additional requirements complicate the dispatching processes and require a proactive and reactive system behavior. To enable automated dispatching processes, this article presents a multiagent system where the decision making is shifted to autonomous, interacting, intelligent agents. Beside the communication protocols and the agent architecture, the focus is on the individual decision making of the agents which meets the specific requirements in groupage traffic. To evaluate the approach we apply multiagent-based simulation and model several scenarios of real world infrastructures with orders provided by our industrial partner. Moreover, a case study is conducted which covers the autonomous groupage traffic in the current processes of our industrial parter. The results reveal that agent-based dispatching meets the sophisticated requirements of groupage traffic. Furthermore, the decision making supports the combination of pickup and delivery tours efficiently while satisfying logistic request priorities, time windows, and capacity constraints.

AbstractRecently, cooperative autonomous underwater vehicles (AUVs) have been deployed in application areas such as surveillance and protection of maritime infrastructures for inspection and monitoring purposes. These cooperative methodologies require wireless transmission of data between the different AUVs operating in the underwater environment. Communication over ranges exceeding 100 m exclusively relies on underwater acoustic communication. However, the propagating acoustic waves suffer from several challenges due to the presence of path loss, multi-path propagation, the slow and variant propagation speed, background noise, and Doppler distortion. Since the power supply of the AUVs is limited, communication must be very energy efficient and energy constraints have to be known to be able to plan the mission of AUVs. Due to the difficulties of real experiments, the modeling and simulation of the energy consumption and underwater acoustic communication play an essential role in studying and developing these systems. We provide a modular simulation model for the energy consumption and acoustic underwater communication of AUVs implemented in the network simulator OMNeT++ using the INET framework. More specifically, we extend several INET modules in such a way as to reflect the characteristics of AUVs and underwater communication. We study and analyze the AUVs’ energy consumption and dependence of the message quality on different properties such as those mentioned above.

In recent years, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication brings more and more attention from industry (e.g., Google and Uber) and government (e.g., United States Department of Transportation). These Vehicle-to-Everything (V2X) technologies are widely adopted in future autonomous vehicles. However, security issues have not been fully addressed in V2V and V2I systems, especially in key distribution and key management. The physical layer key generation, which exploits wireless channel reciprocity and randomness to generate secure keys, provides a feasible solution for secure V2V/V2I communication. It is lightweight, flexible, and dynamic. In this paper, the physical layer key generation is brought to the V2I and V2V scenarios. A LoRa-based physical key generation scheme is designed for securing V2V/V2I communications. The communication is based on Long Range (LoRa) protocol, which is able to measure Received Signal Strength Indicator (RSSI) in long-distance as consensus information to generate secure keys. The multi-bit quantization algorithm, with an improved Cascade key agreement protocol, generates secure binary bit keys. The proposed schemes improved the key generation rate, as well as to avoid information leakage during transmission. The proposed physical layer key generation scheme was implemented in a V2V/V2I network system prototype. The extensive experiments in V2I and V2V environments evaluate the efficiency of the proposed key generation scheme. The experiments in real outdoor environments have been conducted. Its key generation rate could exceed 10 bit/s on our V2V/V2I network system prototype and achieve 20 bit/s in some of our experiments. For binary key sequences, all of them pass the suite of statistical tests from National Institute of Standards and Technology (NIST).

Abstract According to the analysis by the National Police Headquarters, roughly 40% of all road accident victims in Poland are vulnerable road users (VRU), i.e. pedestrians and cyclists. Their protection has become one of the priorities for action regarding road safety. For this purpose, various activities are carried out aimed not only at human behaviour or the development of modern and safe road infrastructures but also at the development of modern vehicles, including advanced driver assistance systems (ADAS). In order to identify the limitations of the currently available driver assistance systems, designed to respond to VRU, research was carried out under the project name, “PEDICRASH: Safety aspects of VRU in CAD automated vehicles.” The project was aimed at increasing users’ awareness (both pedestrians and drivers) of the limitations of ADAS by analysing barriers and indicating recommendations allowing for more effective protection of pedestrians and cyclists due to the identified operating limitations of these systems. The research focused on the autonomous emergency braking (AEB) system and its potential impact on the level of road safety, with particular emphasis on VRU.

Abstract Several European road operators and authorities joined the C-Roads Platform with the aim of harmonising the deployment activities of cooperative intelligent transport systems (C-ITS). C-ITS research is preliminary to future automated-driving vehicles. The current conventional highways were designed on traditional criteria and models specifically developed for traffic flows of manually guided vehicles. Thus, this article describes some new criteria for designing and monitoring road infrastructures on the basis of performance features of autonomous (or self-driving) vehicles. The new criteria have been adopted to perform an accurate conformity control of the A22 Brenner motorway, included in the C-Roads Platform, and also to ascertain whether in future it may be travelled by automated vehicles in safety conditions. Always in accordance with the technical and scientific insights required by the C-Roads Platform, a traffic model has been implemented to estimate how the A22 capacity increases compared to current values, by taking various percentages of automated or manual vehicles into consideration. The results given by theoretical models indicate that the highway will be able to be travelled by automated vehicles in safety conditions. On the other hand, the lane capacity is due to increase up to 2.5 times more than the current capacities, experimentally determined through traffic data collected from 4 highway sections by means of Drake’s flow model.

Given the enormous interest shown by customers as well as industry in autonomous vehicles, the concept of Internet of Vehicles (IoV) has evolved from Vehicular Ad hoc NETworks (VANETs). VANETs are likely to play an important role in Intelligent Transportation Systems (ITS). VANETs based on fixed infrastructures, called Road Side Units (RSUs), have been extensively studied. Efficient, authenticated message dissemination in VANETs is important for the timely delivery of authentic messages to vehicles in appropriate regions in the VANET. Many of the approaches proposed in the literature use RSUs to collect events (such as accidents, weather conditions, etc.) observed by vehicles in its region, authenticate them, and disseminate them to vehicles in appropriate regions. However, as the number of messages received by RSUs increases in the network, the computation and communication overhead for RSUs related to message authentication and dissemination also increases. We address this issue and propose a low-overhead message authentication and dissemination scheme in this paper. We compare the overhead, related to authentication and message dissemination, of our approach with an existing approach and also present an analysis of privacy and security implications of our approach.

In direct line with the evolution of technology, but also with the density of vehicles that create congestion and often road accidents, traffic monitoring systems are parts that integrate intelligent transport systems (ITS). This is one of the most critical elements within transport infrastructures, an aspect that involves extremely important financial investments in order to collect and analyze traffic data with the aim of designing systems capable of properly managing traffic. Technological progress in the field of wireless communications is advancing, highlighting new traffic monitoring solutions, and the need for major classification, but proposing a real-time analysis model to guide the new systems is a challenge addressed in this manuscript. The involvement of classifiers and computerized detection applied to traffic monitoring cameras can outline extremely vital systems for the future of logistic transport. Analyzing and debating vehicle classification systems, examining problems and challenges, as well as designing a software project capable of being the basis of new developments in the field of ITS systems are the aim of this study. The outline of a method based on intelligent algorithms and improved YOLOv3 can have a major impact on the effort to reduce the negative impact created by chaotic traffic and the outline of safety protocols in the field of transport. The reduction of waiting times and decongestion by up to 80% is a valid aspect, which we can deduce from the study carried out.

The goal of civil engineering has always been the research and implementation of methods, technologies, and infrastructures to improve the community’s quality of life. One of the branches of civil engineering that has the strongest effect on progress is transport. The quality of transport has a profound economic and social impact on our communities regarding trade (freight transport) and city livability (public transport systems). However, innovation is not the only way to improve the features above-mentioned, especially public transport, considering that it is usually beneficial to enhance and repurpose vehicles with appropriate adjustments to offer more efficient services. Other perspectives that influence public transport systems are the costs and times of design and construction, maintenance, operating costs, and environmental impact, especially concerning CO2 emissions. Considering these issues, among the various types of existing public transport systems, those of the so-called Bus Rapid Transit (BRT) offer worthwhile results. The BRT system is a type of public road transport operated by bus on reserved lanes, and it is significantly profitable, especially from an economic point of view, in areas where there are existing bus routes. Nonetheless, for the construction of works minimization, it is closely linked to other features that improve its usefulness, depending on the vehicles’ quality such as capacity, but above all, the propulsion or driving autonomy that would guarantee high efficiency. This paper introduces an analysis of some BRT systems operating worldwide, presenting the background, general technical features, and the correlation with autonomous vehicles.

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.

Abstract. UAV technology has become a useful tool for the inspection of infrastructures. Structural Health Monitoring methods are already implementing these vehicles to obtain information about the condition of the structure. Several systems based on close range remote sensing and contact sensors have been developed. In both cases, in order to perform autonomous missions in hard accessible areas or with obstacles, a path planning algorithm that calculates the trajectory to be followed by the UAV to navigate these areas is mandatory. This works presents a UAV path planning algorithm developed to navigate indoors and outdoors. This algorithm does not only calculate the waypoints of the path, but the orientation of the vehicle for each location. This algorithm will support a specific UAV-based contact inspection of vertical structures. The required input data consist of a point cloud of the environment, the initial position of the UAV and the target point of the structure where the contact inspection will be performed.

Small-scale unmanned aerial vehicles are being deployed in urban areas for missions such as ground target tracking, crime scene monitoring, and traffic management. Aerial vehicles deployed in such cluttered environments are required to have robust autonomous navigation with both target tracking and obstacle avoidance capabilities. To this end, this work presents a simple-to-design but effective steerable sensor platform and its implementation techniques for both obstacle avoidance and target tracking. The proposed platform is a 2-axis gimbal system capable of roll and pitch/yaw. The mathematical model that governs the dynamics of this platform is developed. The performance of the platform is validated through a software-in-the-loop simulation. The simulation results show that the platform can be effectively steered to all regions of interest except backward. With its design layout and mount location, the platform can engage sensors for obstacle avoidance and target tracking as per requirements. Moreover, steering the platform in any direction does not induce aerodynamic instability on the unmanned aerial vehicle in mission.

Because photovoltaic (PV) plants require periodic maintenance, using unmanned aerial vehicles (UAV) for inspections can help reduce costs. Usually, the thermal and visual inspection of PV installations works as follows. A UAV equipped with a global positioning system (GPS) receiver is assigned a flight zone, which the UAV will cover back and forth to collect images to be subsequently composed in an orthomosaic. When doing this, the UAV typically flies at a height above the ground that is appropriate to ensure that images overlap even in the presence of GPS positioning errors. However, this approach has two limitations. First, it requires covering the whole flight zone, including “empty” areas between PV module rows. Second, flying high above the ground limits the resolution of the images to be subsequently inspected. The article proposes a novel approach using an autonomous UAV with an RGB and a thermal camera for PV module tracking through segmentation and visual servoing, which does not require a GPS except for measuring the “small” relative displacement between a PV module row and the next one. With this solution, the UAV moves along PV module rows at a lower height than usual and inspects them back and forth in a boustrophedon way by ignoring “empty” areas with no PV modules. Experimental tests performed in simulation and at an actual PV plant are reported, showing a tracking error lower than 0.2 m in most situations when moving at 1.2 m/s.

The transportation industries forecast that by 2050 more than 50% of vehicles on the road will be autonomous vehicles, and automotive services will dynamically support all vehicles. All of them will be serviced using the latest technology, which includes the Software Defined Network (SDN) and available new generations (5G+ or 6G) at the time. Although many transportation services and rapid facilities are achievable dynamically, transportation services with automation and intelligent actions are still not mature because the legacy of transport services cannot be corporate with the 5G+. These expected problems can be improved through the following possible and manageable approaches: flexible framework of 5G automotive services from the legacy systems, designing energy-efficient and intelligent infrastructures with SDN, and managing security solutions that evolve with the emerging technology. An efficient model (flexible framework) is proposed to secure smart transportation services with a secure and intelligent connected system and security solutions based on the 5G concept. Although 5G is considered in this framework, the method and steps of design and solution phases will be adaptable to the 5G+ framework. Furthermore, the basic properties of SDN allowed us to design a novel approach for measuring data traffic related to transport services and transport management, such as the priority of the transportation services. With the emergence of 5G+ capabilities, transportation services expect more challenges through future user requirements, including dynamic security solutions, minimum latency, maximum energy efficiency (EE), etc. Future automotive services depend on many sensors and their messages received through secure communication systems with 5G+ capabilities. As a result, this theoretical model will prove that 5G capabilities provide security facilities, better latency, and EE within the transportation system. Moreover, this model can be extendable to improve the 5G+ transportation services.

The transportation industries forecast that by 2050 more than 50% of vehicles on the road will be autonomous vehicles, and automotive services will dynamically support all vehicles. All of them will be serviced using the latest technology, which includes the Software Defined Network (SDN) and available new generations (5G+ or 6G) at the time. Although many transportation services and rapid facilities are achievable dynamically, transportation services with automation and intelligent actions are still not mature because the legacy of transport services cannot be corporate with the 5G+. These expected problems can be improved through the following possible and manageable approaches: flexible framework of 5G automotive services from the legacy systems, designing energy-efficient and intelligent infrastructures with SDN, and managing security solutions that evolve with the emerging technology. An efficient model (flexible framework) is proposed to secure smart transportation services with a secure and intelligent connected system and security solutions based on the 5G concept. Although 5G is considered in this framework, the method and steps of design and solution phases will be adaptable to the 5G+ framework. Furthermore, the basic properties of SDN allowed us to design a novel approach for measuring data traffic related to transport services and transport management, such as the priority of the transportation services. With the emergence of 5G+ capabilities, transportation services expect more challenges through future user requirements, including dynamic security solutions, minimum latency, maximum energy efficiency (EE), etc. Future automotive services depend on many sensors and their messages received through secure communication systems with 5G+ capabilities. As a result, this theoretical model will prove that 5G capabilities provide security facilities, better latency, and EE within the transportation system. Moreover, this model can be extendable to improve the 5G+ transportation services.

Traffic congestion in urban road networks is a costly problem that affects all major cities in developed countries. To tackle this problem, it is possible (i) to act on the supply side, increasing the number of roads or lanes in a network, (ii) to reduce the demand, restricting the access to urban areas at specific hours or to specific vehicles, or (iii) to improve the efficiency of the existing network, by means of a widespread use of so-called Intelligent Transportation Systems (ITS). In line with the recent advances in smart transportation management infrastructures, ITS has turned out to be a promising field of application for artificial intelligence techniques. In particular, multiagent systems seem to be the ideal candidates for the design and implementation of ITS. In fact, drivers can be naturally modelled as autonomous agents that interact with the transportation management infrastructure, thereby generating a large-scale, open, agent-based system. To regulate such a system and maintain a smooth and efficient flow of traffic, decentralised mechanisms for the management of the transportation infrastructure are needed. In this article we propose a distributed, market-inspired, mechanism for the management of a future urban road network, where intelligent autonomous vehicles, operated by software agents on behalf of their human owners, interact with the infrastructure in order to travel safely and efficiently through the road network. Building on the reservation-based intersection control model proposed by Dresner and Stone, we consider two different scenarios: one with a single intersection and one with a network of intersections. In the former, we analyse the performance of a novel policy based on combinatorial auctions for the allocation of reservations. In the latter, we analyse the impact that a traffic assignment strategy inspired by competitive markets has on the drivers' route choices. Finally we propose an adaptive management mechanism that integrates the auction-based traffic control policy with the competitive traffic assignment strategy.

The navigation of autonomous underwater vehicles is a major scientific and technological challenge. The principal difficulty is the opacity of the water media for usual types of radiation except for the acoustic waves. Thus, an acoustic transducer (array) composed of an acoustic sonar is the only tool for external measurements of the AUV attitude and position. Another difficulty is the inconstancy of the speed of propagation of acoustic waves, which depends on the temperature, salinity, and pressure. For this reason, only the data fusion of the acoustic measurements with data from other onboard inertial navigation system sensors can provide the necessary estimation quality and robustness. This review presents common approaches to underwater navigation and also one novel method of velocity measurement. The latter is an analog of the well-known Optical Flow method but based on a sequence of sonar array measurements.

Global Navigation Satellite Systems (GNSSs) have become a ubiquitous tool for our modern society to carry out vital tasks such as transportation, civil engineering or precision agriculture. This breath has reached the realm of safety-critical applications such as time management of critical infrastructures or autonomous vehicles, in which GNSS is an essential tool nowadays. Unfortunately, current GNSS performance is not enough to fulfill the requirements of these professional and critical applications. For this reason, the FANTASTIC project was launched to boost the adoption of these applications. The project was funded by the European GNSS agency (GSA) in order to enhance the robustness and accuracy of GNSS in harsh environments. This paper presents the part related to the development of a weighting and exclusion function with a dual circularly polarized antenna. The idea is to reduce the effects of multipath by weighting and/or excluding those measurements affected by multipath. The observables and other metrics obtained from a dual polarized antenna will be exploited to define an exclusion threshold and to provide the weights. Real-world experiments will show the improvement in the positioning solution, using all available constellations, obtained with the developed technique.

In large campuses such as UMS and NTU, students need to move at long distances. The transportation of choice has been public transportation. The most common is in the form of buses. Buses need bus stops at strategic locations and only travel at intervals. Although buses are the most eco-friendly among all the transportation systems within a campus, alternative technologies had been studied to improve it further. Self-driving technologies are the most promising. Combined with sharing technologies such as Uber share, self-driving cars offer the most promising solution. Self-driving technologies are already under active development. Campus application should be the first choice for deployment. Campus environment is a private environment so is well controlled. The maps and network infrastructures are well established so will allow reliable self-driving technologies to be used within the campus only. It is therefore surprising that there are few trials involving self-driving transportation systems in a campus environment. There are various possibilities but all these should be overcome in order to have a truly eco-friendly environment within the campus. Electrical shared self-driving cars allow eco-friendly mass transportation of people because electricity is a clean energy. Sharing allows full utilisation of the vehicles unlike other modes of transportation. Mobile apps and GPS allow pickup of passengers at any safe place instead of just at designated places. Because self-driving cars have no drivers, small vehicles may be used economically without the added costs of an extra non-paying passenger and salary of the driver for each vehicle. The lack of any driver also makes it safer for the students especially female students. Although there are still issues of safety among current self-driving technologies as had been shown by the accidents suffered by Tesla cars running on even semi-autonomous modes, safety within the campus should be much better and there is no need for full certification from the transportation authorities. Operating within the campus environment allow operators to operate without the strict licencing requirements of the public transportation environment. Safety can still be ensured by restricting the operation of the self-driving vehicles within clearly marked roads in the campus, enforcing safe speed limits such as the 50 km/hr imposed by Google and restricting operations in clear weather and daytime only. With remote operators and emergency buttons, even the semi-autonomous modes that are within the capabilities of current hardware, self-driving ride-sharing cars should be possible.

Using aerial platforms for Non-Destructive Inspection (NDI) of large and complex structures is a growing field of interest in various industries. Infrastructures such as: buildings, bridges, oil and gas, etc. refineries require regular and extensive inspections. The inspection reports are used to plan and perform required maintenance, ensuring their structural health and the safety of the workers. However, performing these inspections can be challenging due to the size of the facility, the lack of easy access, the health risks for the inspectors, or several other reasons, which has convinced companies to invest more in drones as an alternative solution to overcome these challenges. The autonomous nature of drones can assist companies in reducing inspection time and cost. Moreover, the employment of drones can lower the number of required personnel for inspection and can increase personnel safety. Finally, drones can provide a safe and reliable solution for inspecting hard-to-reach or hazardous areas. Despite the recent developments in drone-based NDI to reliably detect defects, several limitations and challenges still need to be addressed. In this paper, a brief review of the history of unmanned aerial vehicles, along with a comprehensive review of studies focused on UAV-based NDI of industrial and commercial facilities, are provided. Moreover, the benefits of using drones in inspections as an alternative to conventional methods are discussed, along with the challenges and open problems of employing drones in industrial inspections, are explored. Finally, some of our case studies conducted in different industrial fields in the field of Non-Destructive Inspection are presented.

This paper proposes “An Integrated Self-diagnosis System (ISS) for an Autonomous Vehicle based on an Internet of Things (IoT) Gateway and Deep Learning” that collects information from the sensors of an autonomous vehicle, diagnoses itself, and the influence between its parts by using Deep Learning and informs the driver of the result. The ISS consists of three modules. The first In-Vehicle Gateway Module (In-VGM) collects the data from the in-vehicle sensors, consisting of media data like a black box, driving radar, and the control messages of the vehicle, and transfers each of the data collected through each Controller Area Network (CAN), FlexRay, and Media Oriented Systems Transport (MOST) protocols to the on-board diagnostics (OBD) or the actuators. The data collected from the in-vehicle sensors is transferred to the CAN or FlexRay protocol and the media data collected while driving is transferred to the MOST protocol. Various types of messages transferred are transformed into a destination protocol message type. The second Optimized Deep Learning Module (ODLM) creates the Training Dataset on the basis of the data collected from the in-vehicle sensors and reasons the risk of the vehicle parts and consumables and the risk of the other parts influenced by a defective part. It diagnoses the vehicle’s total condition risk. The third Data Processing Module (DPM) is based on Edge Computing and has an Edge Computing based Self-diagnosis Service (ECSS) to improve the self-diagnosis speed and reduce the system overhead, while a V2X based Accident Notification Service (VANS) informs the adjacent vehicles and infrastructures of the self-diagnosis result analyzed by the OBD. This paper improves upon the simultaneous message transmission efficiency through the In-VGM by 15.25% and diminishes the learning error rate of a Neural Network algorithm through the ODLM by about 5.5%. Therefore, in addition, by transferring the self-diagnosis information and by managing the time to replace the car parts of an autonomous driving vehicle safely, this reduces loss of life and overall cost.

Usage of Unmanned Aerial Vehicles (UAVs) is growing rapidly in a wide range of consumer applications, as they prove to be both autonomous and flexible in a variety of environments and tasks. However, this versatility and ease of use also brings a rapid evolution of threats by malicious actors that can use UAVs for criminal activities, converting them to passive or active threats. The need to protect critical infrastructures and important events from such threats has brought advances in counter UAV (c-UAV) applications. Nowadays, c-UAV applications offer systems that comprise a multi-sensory arsenal often including electro-optical, thermal, acoustic, radar and radio frequency sensors, whose information can be fused to increase the confidence of threat’s identification. Nevertheless, real-time surveillance is a cumbersome process, but it is absolutely essential to detect promptly the occurrence of adverse events or conditions. To that end, many challenging tasks arise such as object detection, classification, multi-object tracking and multi-sensor information fusion. In recent years, researchers have utilized deep learning based methodologies to tackle these tasks for generic objects and made noteworthy progress, yet applying deep learning for UAV detection and classification is considered a novel concept. Therefore, the need to present a complete overview of deep learning technologies applied to c-UAV related tasks on multi-sensor data has emerged. The aim of this paper is to describe deep learning advances on c-UAV related tasks when applied to data originating from many different sensors as well as multi-sensor information fusion. This survey may help in making recommendations and improvements of c-UAV applications for the future.

Статья посвящена исследованию инновационной деятельности, в том числе инновационной инфраструктуры и патентной деятельности в регионах Северо-Востока России (СВР). К северо-восточным регионам России относятся Республика Саха (Якутия), Магаданская область, Чукотский автономный округ, Камчатский край и Сахалинская область, которые кроме Камчатского края относятся к северным регионам ресурсного типа. Методологической основой оценки инновационного развития регионов послужили работы зарубежных и отечественных авторов, а также разработки авторов по данной тематике. Выполнен обзорный анализ инновационной деятельности пяти регионов, расположенных на территории Северо-Востока России. Проанализирована инновационная инфраструктура Республики Саха (Якутия), Магаданской области, Чукотского автономного округа, Камчатского края и Сахалинской области. Представлены показатели «динамика изменения показателей патентной деятельности», характеризующие изобретательскую активность регионов. Анализ данных показывает, что лидирующую позицию среди регионов СВР по изобретательской активности занимает Республика Саха (Якутия) с показателем 0,79, что существенно выше, чем в Магаданской области (0,36), Камчатском крае (0,32) и Сахалинской области (0,10). Такая же картина расположения наблюдается и в динамике выданных патентов на изобретения, полезные модели и промышленные образцы на десять тысяч человек рабочей силы. Анализ и оценка инновационного потенциала СВР показывают, что в большинстве субъектах созданы необходимая правовая и инновационная инфраструктурная база, приняты региональные концепции и программы развития инновационных систем, в основном имеются необходимые инфраструктурные элементы для развития региональной инновационной системы. В перспективе необходимы будут дальнейшие исследования влияния инновационных процессов на социально-экономическое развитие северных (арктических) территорий, в том числе северо-восточных регионов России. The article is devoted to the study of innovation activity, including innovation infrastructure and patent activity in the regions of the Russian northeast (RNE). The north-eastern regions of Russia include the Sakha Republic (Yakutia), Magadan Oblast, Chukotka Autonomous Okrug, Kamchatka Krai and Sakhalin Oblast, which, except Kamchatka Krai, belong to the northern regions of the resource type. The methodological basis for assessing the innovative development of the regions was found in works by foreign and Russian authors, as well as revealed by the authors when studying the topic. The review analysis of innovative activity of the five regions located in the Russian northeast was done. The innovative infrastructures of the Sakha Republic (Yakutia), Magadan Oblast, Chukotka Autonomous Okrug, Kamchatka Krai and Sakhalin Oblast were analyzed. The indicators “dynamics of changes in the indicators of patent activity”, characterizing the inventive activity of the regions, were presented. The data analysis showed that the leading position among the regions of the RNE in terms of inventive activity is occupied by the Sakha Republic (Yakutia) with an indicator of 0.79, which is significantly higher than those of Magadan Oblast (0.36), Kamchatka Krai (0.32), and Sakhalin Oblast (0.10) (Fig. 5a). The same pattern of location was observed in the dynamics of issued patents for inventions, utility models and industrial designs per ten thousand people of the labor force (Fig. 5b). The analysis and assessment of the innovation potential of the RNE showed that the necessary legal and innovative infrastructure base has been created in most subjects, regional concepts and programs for the development of innovative systems have been adopted, and there are basically the necessary infrastructure elements for the development of regional innovation systems. In the future, further studies of the impact of innovative processes on the socio-economic development of the northern (Arctic) territories, including the Russian northeastern regions, will be necessary.

Abstract 3D object detection using point cloud is an essential task for autonomous driving. With the development of infrastructures, roadside perception can extend the view range of the autonomous vehicles through communication technology. Computation time and power consumption are two main concerns when deploying object detection tasks, and a light-weighted detection model applied in an embedded system is a convenient solution for both roadside and vehicleside. In this study, a 3D Point cLoud Object deTection (PLOT) network is proposed to reduce heavy computing and ensure real-time object detection performance in an embedded system. First, a bird’s eye view representation of the point cloud is calculated using pillar-based encoding method. Then a cross-stage partial network-based backbone and a feature pyramid network-based neck are implemented to generate the high-dimensional feature map. Finally, a multioutput head using a shared convolutional layer is attached to predict classes, bounding boxes, and the orientations of the objects at the same time. Extensive experiments using the Waymo Open Dataset and our own dataset are conducted to demonstrate the accuracy and efficiency of the proposed method.

The debate about how Mobility as a Service (MaaS) will revolutionize individual and collective mobility is gaining increasing attention from researchers, industries, and public sectors. MaaS is expected to create a techno-utopia with a new organization and operation of transport systems where residents have equal access to instant and ubiquitous mobility services. However, transport service is an artifact that is highly dependent on the construction of urban infrastructure. The success of MaaS requires the support of urban infrastructure that we categorize as (1) transport-flow infrastructure, (2) information-flow infrastructure, and (3) computing-flow infrastructure. The connotation of urban infrastructure here includes not only conventional concepts, such as transportation infrastructure, but also intelligent transportation concepts, such as high-speed communication networks and autonomous fleets. Moreover, travel behavior data collected by city sensors, communication networks, and intelligent vehicles require appropriate infrastructures to dynamically compute for fleet dispatch and demand-supply match. Based on the degree of integration of these infrastructures, MaaS projects will have different results in varying cities. From concept to practice, given that a vast disparity in infrastructure exists between cities, we need an inclusive, mobile, and global understanding of the MaaS concept to make it successful in different parts of the world.

AbstractCurrent research on autonomous vehicles tends to focus on making them safer through policies to manage innovation, and integration into existing urban and mobility systems. This article takes social, cultural and philosophical approaches instead, critically appraising how human subjectivity, and human-machine relations, are shifting and changing through the application of big data and algorithmic techniques to the automation of driving. 20th century approaches to safety engineering and automation—be it in an airplane or automobile-have sought to either erase the human because she is error-prone and inefficient; have design compensate for the limits of the human; or at least mould human into machine through an assessment of the complementary competencies of each. The ‘irony of automation’ is an observation of the tensions emerging therein; for example, that the computationally superior and efficient machine actually needs human operators to ensure that it is working effectively; and that the human is inevitably held accountable for errors, even if the machine is more efficient or accurate. With the emergence of the autonomous vehicle (AV) as simultaneously AI/ ‘robot’, and automobile, and distributed, big data infrastructural platform, these beliefs about human and machine are dissolving into what I refer to as the ironies of autonomy. For example, recent AV crashes suggest that human operators cannot intervene in the statistical operations underlying automated decision-making in machine learning, but are expected to. And that while AVs promise ‘freedom’, human time, work, and bodies are threaded into, and surveilled by, data infrastructures, and re-shaped by its information flows. The shift that occurs is that human subjectivity has socio-economic and legal implications and is not about fixed attributes of human and machine fitting into each other. Drawing on Postphenomenological concepts of embodiment and instrumentation, and excerpts from fieldwork, this article argues that the emergence of AVs in society prompts a rethinking of the multiple relationalities that constitute humanity through machines.

During the War on Terror, the United States military has been conducting an increasing number of foreign campaigns by remote control using drones—also called unmanned aerial vehicles (UAVs) or remotely piloted vehicles (RPVs)—to extend the reach of military power and augment the technical precision of targeted strikes while minimizing bodily risk to American combatants. Stationed on bases throughout the southwest, operators fly weaponized drones over the Middle East. Viewing the battle zone through a computer screen that presents them with imagery captured from a drone-mounted camera, these combatants participate in war from a safe distance via an interface that resembles a video game. Increasingly, this participation takes the form of targeted killing. Despite their relative physical safety, in 2008 reports began mounting that like boots-on-the-ground combatants, many drone operators seek the services of chaplains or other mental health professionals to deal with the emotional toll of their work (Associated Press; Schachtman). Questions about the nature of the stress or trauma that drone operators experience have become a trope in news coverage of drone warfare (see Bumiller; Bowden; Saleton; Axe). This was exemplified in May 2013, when former Air Force drone pilot Brandon Bryant became a public figure after speaking to National Public Radio about his remorse for participating in targeted killing strikes and his subsequent struggle with post-traumatic stress (PTS) (Greene and McEvers). Stories like Bryant’s express American culture’s struggle to understand the role screen-mediated, remotely controlled killing plays in shifting the location of combatants’s sense of moral agency. That is, their sense of their ability to act based on their own understanding of right and wrong. Historically, one of the primary ways that psychiatry has conceptualized combat trauma has been as combatants’s psychological response losing their sense of moral agency on the battlefield (Lifton).This articleuses the popular science fiction novel Ender's Game as an analytic lens through which to examine the ways that screen-mediated warfare may result in combat trauma by investigating the ways in which it may compromise moral agency. The goal of this analysis is not to describe the present state of drone operators’s experience (see Asaro), but rather to compare and contrast contemporary public discourses on the psychological impact of screen-mediated war with the way it is represented in one of the most influential science fiction novels of all times (The book won the Nebula Award in 1985, the Hugo Award in 1986, and appears on both the Modern Library 100 Best Novels and American Library Association’s “100 Best Books for Teens” lists). In so doing, the paper aims to counter prevalent modes of critical analysis of screen-mediated war that cannot account for drone operators’s trauma. For decades, critics of postmodern warfare have denounced how fighting from inside tanks, the cockpits of planes, or at office desks has removed combatants from the experiences of risk and endangerment that historically characterized war (see Gray; Levidow & Robins). They suggest that screen-mediation enables not only physical but also cognitive and emotional distance from the violence of war-fighting by circumscribing it in a “magic circle.” Virtual worlds scholars adopted the term “magic circle” from cultural historian Johan Huizinga, who described it as the membrane that separates the time and space of game-play from those of real life (Salen and Zimmerman). While military scholars have long recognized that only 2% of soldiers can kill without hesitation (Grossman), critics of “video game wars” suggest that screen-mediation puts war in a magic circle, thereby creating cyborg human-machine assemblages capable of killing in cold blood. In other words, these critics argue that screen-mediated war distributes agency between humans and machines in such a way that human combatants do not feel morally responsible for killing. In contrast, Ender’s Game suggests that even when militaries utilize video game aesthetics to create weapons control interfaces, screen-mediation alone ultimately cannot blur the line between war and play and thereby psychically shield cyborg soldiers from combat trauma.Orson Scott Card’s 1985 novel Ender’s Game—and the 2013 film adaptation—tells the story of a young boy at an elite military academy. Set several decades after a terrible war between humans and an alien race called the buggers, the novel follows the life of a boy named Ender. At age 6, recruiters take Andrew “Ender” Wiggin from his family to begin military training. He excels in all areas and eventually enters officer training. There he encounters a new video game-like simulator in which he commands space ship battalions against increasingly complex configurations of bugger ships. At the novel’s climax, Ender's mentor, war hero Mazer Rackham, brings him to a room crowded with high-ranking military personnel in order to take his final test on the simulator. In order to win Ender opts to launch a massive bomb, nicknamed “Little Doctor”, at the bugger home world. The image on his screen of a ball of space dust where once sat the enemy planet is met by victory cheers. Mazer then informs Ender that since he began officer training, he has been remotely controlling real ships. The video game war was, "Real. Not a game" (Card 297); Ender has exterminated the bugger species. But rather than join the celebration, Ender is devastated to learn he has committed "xenocide." Screen-mediation, the novel shows, can enable people to commit acts that they would otherwise find heinous.US military advisors have used the story to set an agenda for research and development in augmented media. For example, Dr. Michael Macedonia, Chief Technology Officer of the Army Office for Simulation, Training, and Instrumentation told a reporter for the New York Times that Ender's Game "has had a lot of influence on our thinking" about how to use video game-like technologies in military operations (Harmon; Silberman; Mead). Many recent programs to develop and study video game-like military training simulators have been directly inspired by the book and its promise of being able to turn even a six-year-old into a competent combatant through well-structured human-computer interaction (Mead). However, it would appear that the novel’s moral regarding the psychological impact of actual screen-mediated combat did not dissuade military investment in drone warfare. The Air Force began using drones for surveillance during the Gulf War, but during the Global War on Terror they began to be equipped with weapons. By 2010, the US military operated over 7,000 drones, including over 200 weapons-ready Predator and Reaper drones. It now invests upwards of three-billion dollars a year into the drone program (Zucchino). While there are significant differences between contemporary drone warfare and the plot of Ender's Game—including the fact that Ender is a child, that he alone commands a fleet, that he thinks he is playing a game, and that, except for a single weapon of mass destruction, he and his enemies are equally well equipped—for this analysis, I will focus on their most important similarities: both Ender and actual drone operators work on teams for long shifts using video game-like technology to remotely control vehicles in aerial combat against an enemy. After he uses the Little Doctor, Mazer and Graff, Ender's long-time training supervisors, first work to circumvent his guilt by reframing his actions as heroic. “You're a hero, Ender. They've seen what you did, you and the others. I don't think there's a government on Earth that hasn't voted you their highest metal.” “I killed them all, didn't I?” Ender asked. “All who?” asked Graff. “The buggers? That was the idea.” Mazer leaned in close. “That's what the war was for.” “All their queens. So I killed all their children, all of everything.” “They decided that when they attacked us. It wasn't your fault. It's what had to happen.” Ender grabbed Mazer's uniform and hung onto it, pulling him down so they were face to face. “I didn't want to kill them all. I didn't want to kill anybody! I'm not a killer! […] but you made me do it, you tricked me into it!” He was crying. He was out of control. (Card 297–8)The novel up to this point has led us to believe that Ender at the very least understands that what he does in the game will be asked of him in real life. But his traumatic response to learning the truth reveals that he was in the magic circle. When he thinks he is playing a game, succeeding is a matter of ego: he wants to be the best, to live up to the expectations of his trainers that he is humanity’s last hope. When the magic circle is broken, Ender reconsiders his decision to use the Little Doctor. Tactics he could justify to win the game, reframed as real military tactics, threaten his sense of himself as a moral agent. Being told he is a hero provides no solace.Card wrote the novel during the Cold War, when computers were coming to play an increasingly large role in military operations. Historians of military technology have shown that during this time human behavior began to be defined in machine-like, functionalist terms by scientists working on cybernetic systems (see Edwards; Galison; Orr). Human skills were defined as components of large technological systems, such as tanks and anti-aircraft weaponry: a human skill was treated as functionally the same as a machine one. The only issue of importance was how all the components could work together in order to meet strategic goals—a cybernetic problem. The reasons that Mazer and Graff have for lying to Ender suggest that the author believed that as a form of technical augmentation, screen-mediation can be used to evacuate individual moral agency and submit human will to the command of the larger cybernetic system. Issues of displaced agency in the military cyborg assemblage are apparent in the following quote, in which Mazer compares Ender himself to the bomb he used to destroy the bugger home world: “You had to be a weapon, Ender. Like a gun, like the Little Doctor, functioning perfectly but not knowing what you were aimed at. We aimed you. We're responsible. If there was something wrong, we did it” (298). Questions of distributed agency have also surfaced in the drone debates. Government and military leaders have attempted to depersonalize drone warfare by assuring the American public that the list of targets is meticulously researched: drones kill those who we need killed. Drone warfare, media theorist Peter Asaro argues, has “created new and complex forms of human-machine subjectivity” that cannot be understood by considering the agency of the technology alone because it is distributed between humans and machines (25). While our leaders’s decisions about who to kill are central to this new cyborg subjectivity, the operators who fire the weapons nevertheless experience at least a retrospective sense of agency. As phenomenologist John Protevi notes, in the wake of wars fought by modern military networks, many veterans diagnosed with PTS still express guilt and personal responsibility for the outcomes of their participation in killing (Protevi). Mazer and Graff explain that the two qualities that make Ender such a good weapon also create an imperative to lie to him: his compassion and his innocence. For his trainers, compassion means a capacity to truly think like others, friend or foe, and understand their motivations. Graff explains that while his trainers recognized Ender's compassion as an invaluable tool, they also recognized that it would preclude his willingness to kill.It had to be a trick or you couldn't have done it. It's the bind we were in. We had to have a commander with so much empathy that he would think like the buggers, understand them and anticipate them. So much compassion that he could win the love of his underlings and work with them like a perfect machine, as perfect as the buggers. But somebody with that much compassion could never be the killer we needed. Could never go into battle willing to win at all costs. If you knew, you couldn't do it. If you were the kind of person who would do it even if you knew, you could never have understood the buggers well enough. (298)In learning that the game was real, Ender learns that he was not merely coming to understand a programmed simulation of bugger behavior, but their actual psychology. Therefore, his compassion has not only helped him understand the buggers’ military strategy, but also to identify with them.Like Ender, drone operators spend weeks or months following their targets, getting to know them and their routines from a God’s eye perspective. They both also watch the repercussions of their missions on screen. Unlike fighter pilots who drop bombs and fly away, drone operators use high-resolution cameras and fly much closer to the ground both when flying and assessing the results of their strikes. As one drone operator interviewed by the Los Angeles Times explained, "When I flew the B-52, it was at 30,000 to 40,000 feet, and you don't even see the bombs falling … Here, you're a lot closer to the actual fight, or that's the way it seems" (Zucchino). Brookings Institute scholar Peter Singer has argued that in this way screen mediation actually enables a more intimate experience of violence for drone operators than airplane pilots (Singer).The second reason Ender’s trainers give for lying is that they need someone not only compassionate, but also innocent of the horrors of war. The war veteran Mazer explains: “And it had to be a child, Ender,” said Mazer. “You were faster than me. Better than me. I was too old and cautious. Any decent person who knows what warfare is can never go into battle with a whole heart. But you didn't know. We made sure you didn't know" (298). When Ender discovers what he has done, he loses not only his innocence but his sense of himself as a moral agent. After such a trauma, his heart is no longer whole.Actual drone operators are, of course, not kept in a magic circle, innocent of the repercussions of their actions. Nor do they otherwise feel as though they are playing, as several have publicly stated. Instead, they report finding drone work tedious, and some even play video games for fun (Asaro). However, Air Force recruitment advertising makes clear analogies between the skills they desire and those of video game play (Brown). Though the first generations of drone operators were pulled from the ranks of flight pilots, in 2009 the Air Force began training them from the ground. Many drone operators, then, enter the role having no other military service and may come into it believing, on some level, that their work will be play.Recent military studies of drone operators have raised doubts about whether drone operators really experience high rates of trauma, suggesting that the stresses they experience are seated instead in occupational issues like long shifts (Ouma, Chappelle, and Salinas; Chappelle, Psy, and Salinas). But several critics of these studies have pointed out that there is a taboo against speaking about feelings of regret and trauma in the military in general and among drone operators in particular. A PTS diagnosis can end a military career; given the Air Force’s career-focused recruiting emphasis, it makes sense that few would come forward (Dao). Therefore, it is still important to take drone operator PTS seriously and try to understand how screen-mediation augments their experience of killing.While critics worry that warfare mediated by a screen and joystick leads to a “‘Playstation’ mentality towards killing” (Alston 25), Ender's Game presents a theory of remote-control war wherein this technological redistribution of the act of killing does not, in itself, create emotional distance or evacuate the killer’s sense of moral agency. In order to kill, Ender must be distanced from reality as well. While drone operators do not work shielded by the magic circle—and therefore do not experience the trauma of its dissolution—every day when they leave the cyborg assemblage of their work stations and rejoin their families they still have to confront themselves as individual moral agents and bear their responsibility for ending lives. In both these scenarios, a human agent’s combat trauma serves to remind us that even when their bodies are physically safe, war is hell for those who fight. This paper has illustrated how a science fiction story can be used as an analytic lens for thinking through contemporary discourses about human-technology relationships. However, the US military is currently investing in drones that are increasingly autonomous from human operators. This redistribution of agency may reduce incidence of PTS among operators by decreasing their role in, and therefore sense of moral responsibility for, killing (Axe). Reducing mental illness may seem to be a worthwhile goal, but in a world wherein militaries distribute the agency for killing to machines in order to reduce the burden on humans, societies will have to confront the fact that combatants’s trauma cannot be a compass by which to measure the morality of wars. Too often in the US media, the primary stories that Americans are told about the violence of their country’s wars are those of their own combatants—not only about their deaths and physical injuries, but their suicide and PTS. To understand war in such a world, we will need new, post-humanist stories where the cyborg assemblage and not the individual is held accountable for killing and morality is measured in lives taken, not rates of mental illness. ReferencesAlston, Phillip. “Report of the Special Rapporteur on Extrajudicial, Summary, or Arbitrary Executions, Addendum: Study on Targeted Killings.” United Nations Human Rights Council (2010). Asaro, Peter M. “The Labor of Surveillance and Bureaucratized Killing: New Subjectivities of Military Drone Operators”. Social Semiotics 23.2 (2013): 196-22. Associated Press. “Predator Pilots Suffering War Stress.” Military.com 2008. 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