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Increased safety is one of the main motivations for traffic research and planning. The arduous task has two components: (i) improving the existing traffic policies based on a good understanding of risk factors related to trends in traffic accidents, and (ii) underpinning the emerging technologies that will advance the safety of vehicles. For the latter route, the introduction of connected and automated vehicles (CAVs) is a promising option as CAVs can potentially reduce the number of accidents. However, to reap their benefits, they need to be introduced in a safe manner and tested for their ability to safely deal with risky scenarios. Unfortunately, the identification of such test scenarios remains a key challenge for the industry. This study contributes to increased safety by (i) analyzing UK’s STATS19 accident data to identify patterns in past traffic accidents, and (ii) utilizing this information to systematically generate scenarios for CAV testing. For task (i), the patterns in the accidents were identified in terms of static and time-dependent internal and external factors. For this purpose, the study employed a clustering algorithm, COOLCAT, which is particularly suitable for dealing with high-dimensional categorical data. Six different clusters emerged naturally as a result of the algorithm. To interpret the clusters, we applied a frequency analysis to each cluster. The frequency tests showed that in each cluster, certain distinct real-world situations were represented more significantly compared to the non-clustered reference case, which are the markers of each cluster. The second task (ii) complemented the first task by synthesizing the relationships between attributes. This was done by association rule mining using the market basket analysis approach. The method enabled us to develop, drawing from the characteristics of the clusters, non-trivial test scenarios that can be used in the testing of CAVs, especially in virtual testing.

The driving domain is inherently dangerous. To develop connected and automated vehicles that can detect potential sources of harm, we must clearly define these hazardous events and metrics to detect them. The majority of driving scenarios we face do not materialise harm, but we often face potentially hazardous near-miss scenarios. Potential harm is difficult to quantify when harm is not materialised; thus, few metrics detect these scenarios in the absence of collision and even fewer datasets label non-collision-based hazardous events. This study focuses on detecting near-miss scenarios due to other actors since human error is the primary source of harm. We first provide a concise overview of current event-specific metrics. We then propose an event-agnostic detection framework that exploits vehicle kinematics to detect evasive manoeuvres early and dynamically calculate minimum safe distances. Given inconsistent dataset labelling methods and collision-focused events, we provide a preliminary study to demonstrate an eventagnostic and configurable dataset annotation technique to label hazardous events, even when harm is not materialised. We show promising results detecting hazardous scenes on a labelled simulation benchmark, GTACrash.

This paper provides a systematic approach to study the influence of electrical steel grade on electric vehicle traction motor performance. A permanent magnet assisted synchronous reluctance motor, an interior permanent magnet motor and an induction motor are considered to systematically quantify the influence of lamination thicknesses from 0.1 to 0.35mm, and materials with 3% and 6.5% silicon contents. Laminations with low silicon steel are better candidates in terms of peak and continuous torque production for low-speed operations. 0.1mm 6.5% silicon steel - 10JNEX900 can achieve higher efficiency and better flux-weakening capability above 400 Hz. Its higher mechanical strength is beneficial for complex rotor structure with bridges under stress from centrifugal forces. Therefore, grade choice relies on a compromise between low and high speed performance, propulsion system cost estimation and manufacturing challenges. Application related priorities define which grade is best suited.

Autonomous Optical Navigation is essential for the proximity operations of space missions to asteroids that usually have irregular gravity fields. One core component of this navigation strategy is the Image Processing (IP) algorithm that extracts optical observables from images captured by the spacecraft’s onboard camera. Among these observables, the centroid of the asteroid is important to determine the position between the spacecraft and the body, which is the focus of this research. However, the performance of standard IP algorithms is affected and constrained by the features of the images, such as the shape of the asteroid, the illumination conditions and the presence of additional bodies, therefore, the quality of the extracted optical observables is influenced. To address the latter two challenges, this paper develops a Convolutional Neural Networks (CNN)-based IP algorithm and applies it to the Early Characterization Phase (ECP) of the European Space Agency’s HERA mission with the target body of binary asteroid Didymos. This algorithm is capable of estimating the centroid of the primary body successfully with high accuracy and without being affected by the presence of the secondary body or the illumination in the input images. In addition, it can also estimate the centroid of the secondary body when the two bodies are in the same image, which increases the robustness of the overall navigation strategy.

This paper presents a novel approach to the gener-ative design optimisation of a resilient Drone Logistic Network (DLN) for the delivery of medical equipment in Scotland. A DLN is a complex system composed of a high number of different classes of drones and ground infrastructures. The corresponding DLN model is composed of a number of interconnected digital twins of each one of these infrastructures and vehicles, forming a single digital twin of the whole logistic network. The paper proposes a multi-agent bio-inspired optimisation approach based on the analogy with the Physarum Policefalum slime mould that incrementally generates and optimise the DLN. A graph theory methodology is also employed to evaluate the network resilience where random failures, and their cascade effect, are simulated. The different conflicting objectives are aggregated into a single global performance index by using Pascoletti-Serafini scalarisation.

One of the main challenges for the introduction of Automated Driving Systems (ADSs) is their verification and validation (V&V). Simulation based testing has been widely accepted as an essential aspect of the ADS V&V processes. Simulations are especially useful when exposing the ADS to challenging driving scenarios, as they offer a safe and efficient alternative to real world testing. It is thus suggested that evidence for the safety case for an ADS will include results from both simulation and real-world testing. However, for simulation results to be trusted as part of the safety case of an ADS for its safety assurance, it is essential to prove that the simulation results are representative of the real world, thus validating the simulation platform itself.\ud In this paper, we propose a novel methodology for validating the simulation environments focusing on comparing point cloud data from real lidar sensor and a simulated lidar sensor model. A 3D object dissimilarity metric is proposed to compare between the two maps (real and simulated), to quantify how accurate the simulation is. This metric is tested on collected lidar point cloud data and the simulated point cloud generated in the simulated environment.

Unmanned aerial vehicles (UAVs) have attracted great research attention due to their flexibility. In this paper, the use of UAVs in wireless sensor networks as an energy transmitter and a data collector is investigated. The UAV is first charged from a charging station, such as a base station (BS), before it flies to the sensors for data collection. Upon arrival, the UAV first charges the sensors via wireless power transfer (WPT) in the downlink, followed by data transmission from the sensors in the uplink. After that, the UAV flies back to the BS to offload data to the BS. We aim to maximize the amount of data offloaded to the BS by allocating optimal time slots to different tasks in this process, given a fixed total time. The maximization is solved in two steps as two convex optimization problems. In the first step, the time allocation between WPT to sensors and data collection from sensors is optimized. In the second step, the time allocation of BS charging, the total time in the first step, and BS data offloading is optimized. Unlike the previous works, our study takes into account the charging process from the BS to the UAV, the propulsion consumption at the UAV and the data offloading process to the BS. Both distance-dependent path loss and small-scale fading are considered. Numerical results show that the optimal time allocation can maximize the amount of data at the BS without wasting any time and energy.

The transformers architecture and transfer learning have radically modified the Natural Language Processing (NLP) landscape, enabling new applications in fields where open source labelled datasets are scarce. Space systems engineering is a field with limited access to large labelled corpora and a need for enhanced knowledge reuse of accumulated design data. Transformers models such as the Bidirectional Encoder Representations from Transformers (BERT) and the Robustly Optimised BERT Pretraining Approach (RoBERTa) are however trained on general corpora. To answer the need for domain specific contextualised word embedding in the space field, we propose Space Transformers, a novel family of three models, SpaceBERT, SpaceRoBERTa and SpaceSciBERT, respectively further pre-trained from BERT, RoBERTa and SciBERT on our domain-specific corpus. We collect and label a new dataset of space systems concepts based on space standards. We fine-tune and compare our domain-specific models to their general counterparts on a domain-specific Concept Recognition (CR) task. Our study rightly demonstrates that the models further pre-trained on a space corpus outperform their respective baseline models in the Concept Recognition task, with SpaceRoBERTa achieving significant higher ranking overall.

Transport electrification is a key enabler to reduce fossil fuel depletion and related carbon dioxide emissions. However, critical barriers exist in terms of battery costs and their expected life. Vehicle-to-grid technology can bring benefits to both the electrical power grid and electric vehicle owners, while its practical implementation faces challenges due to the concerns over accelerated battery degradation. This paper presents a comprehensive study on reduced Lithium-ion battery degradation through state-of-charge pre-conditioning strategies that allow an electric vehicle to participate in vehicle-to-grid operations during periods in which the vehicle is parked. Energy capacity reduction of the electric vehicle battery are predicted using semi-empirical ageing models, which have been built and validated to capture the degradation behaviours of the battery with respect to both calendar and cycling ageing. Five charging strategies for battery state-of-charge pre-conditioning have been developed to evaluate the ability to mitigate battery ageing before commencing vehicle-to-grid operations. Simulation studies on battery degradation utilizing such charging mechanisms under two different operational profiles have been undertaken. The analytical results show that the proposed charging strategies do not accelerate battery degradation and are capable of mitigating the total ageing process from 7.3 – 26.7% for the first 100 days of operational life and gradually vary to 8.6 – 12.3% for one-year continual operation compared to the reference standard charging approach.

3D object detection is a common function within the perception system of an autonomous vehicle and outputs a list of 3D bounding boxes around objects of interest. Various 3D object detection methods have relied on fusion of different sensor modalities to overcome limitations of individual sensors. However, occlusion, limited field-of-view and low-point density of the sensor data cannot be reliably and cost-effectively addressed by multi-modal sensing from a single point of view. Alternatively, cooperative perception incorporates information from spatially diverse sensors distributed around the environment as a way to mitigate these limitations. This article proposes two schemes for cooperative 3D object detection using single modality sensors. The early fusion scheme combines point clouds from multiple spatially diverse sensing points of view before detection. In contrast, the late fusion scheme fuses the independently detected bounding boxes from multiple spatially diverse sensors. We evaluate the performance of both schemes, and their hybrid combination, using a synthetic cooperative dataset created in two complex driving scenarios, a T-junction and a roundabout. The evaluation shows that the early fusion approach outperforms late fusion by a significant margin at the cost of higher communication bandwidth. The results demonstrate that cooperative perception can recall more than 95% of the objects as opposed to 30% for single-point sensing in the most challenging scenario. To provide practical insights into the deployment of such system, we report how the number of sensors and their configuration impact the detection performance of the system., 13 pages, 4 tables, 7 figures. Published in IEEE Transactions on Intelligent Transportation Systems

In electric two-wheelers, high riding performance in terms of energy consumption, ride comfort and rapid acceleration is known as a key enable for the sustainable development. The key performance indicators are dependent upon factors like length of the trip, state of charge of the battery, current traffic conditions and rider behaviors. This leads to the need to develop a highly efficient and convenient motorcycle with multiple drive modes, to enhance customer satisfaction. It is therefore critical to design a brake blending control strategy capable of working effectively with different drive modes to maximize the braking energy recoverability without impacting ride comfort. In order to address this challenge, a simple but efficient brake blending strategy based on serial braking concept, has been developed in this paper, for a two-wheeler electric vehicle with multiple drive modes.

Identification of riding patterns is one of the key enablers to update energy consumption strategy, optimise the energy management system and increase the range of electric motorcycles despite their weight and space limits. Considering the varying driving conditions in real applications, improving accuracy of the riding pattern recognition without significant complexity is the main challenge. In this paper a simple and efficient online classification method is introduced based on features extracted only from the motorcycle speed. The recognition mechanism is firstly developed using support vector machine technique. The effect of validation method for removing the optimism in classification and the contribution of features to the accuracy of model is then investigated. Evaluation of the method on the real riding conditions in simulation environment shows the effectiveness of the approach.\ud

Electrification of transportation means brings positive impacts to the environment because of reduced fossil fuel depletion and related carbon emissions. Critical obstacles remain in terms of battery costs and their expected life. Vehicle-to-grid technologies can deliver benefits to support electrical power grid and vehicle owner, while their practical implementation faces challenges due to the concerns over accelerated battery degradation. This study presents the evaluation of battery degradation through different smart charge strategies and vehicle-to-grid scenarios. The simulation results show that the developed smart charge schemes can mitigate the battery ageing up to 5% while lowering the charge cost from 30 - 60% as comparing to the conventional charge method within the first five days operation of the battery. In addition, the calendar ageing can be diminished upto 80% by participating in suitable V2G scenario.

Internet-of-Vehicle (IoV) is empowering smart vehicles with data collection and sharing capabilities, and blockchains have been introduced to manage the IoV data due to many advantages, including decentralization, security, reliability, and scalability. Nevertheless, existing IoV blockchain models suffer from poor security against collusion attacks instigated by malicious blockchain miners typically represented by roadside units (RSUs). To address this problem, additional block verifiers, e.g., vehicles, can be recruited during block verification, which enhances security but also can lead to the reduced throughput. Therefore, in this paper, we propose a resource management scheme for IoV blockchains to enhance the system security while maximizing the throughput by optimizing contributed computing resources from RSUs and recruited vehicles. We show that the optimal strategies of RSUs and vehicles can be found through the Karush-Kuhn-Tucker (KKT) conditions and verify (using simulations) that our scheme achieves the higher throughput with enhanced security compared to the existing IoV blockchains.

With the development of automated vehicles and advanced driver assistance systems, the compression of the large amount of data generated by the vehicle camera sensors becomes a necessary processing step to improve the automated driving system efficiency. H.264 is a widely adopted video compression scheme, and it has been designed for human vision. Rate control in H.264 uses fixed quantisation parameter, however, this process can lead to fluctuation in different regions of the image quality of each frame. In this paper, we propose a two-stageH.264 based video compression framework, named “Two Stage Compression (TSC)”, to compress the automotive camera videos with different values of compression rate in different regions of each frame. In the first stage, each frame will be divided into the region-of-interest and the region-out-of-interest. In the second- stage, different compression ratios will be applied based on the importance of the region. The experimental results show that under the same overall compression ratio, our proposed TSC increments the semantic-aware PSNR by 3.213 dB compared to uniform H.264 compression. Our method is also compared to uniform H.264 compression using a segmentation algorithm, with an improvement of 1.77% in mIOU, the average Intersection over- Union

This paper presents a case study of the full development process from modelling and design to build, vehicle integration and testing of a 20 kW rated wireless charging system. The combined charging system (CCS) DC charging interface on the vehicle was used for vehicle charging and communication. The work motivation along with details of the system design, construction and vehicle integration are included in this paper. The main learning points are discussed and test results showing the final system in operation are presented.

While partially automated vehicles can provide a range of benefits, they also bring about new Human Machine Interface (HMI) challenges around ensuring the driver remains alert and is able to take control of the vehicle when required. While humans are poor monitors of automated processes, specifically during `steady state' operation, presenting the appropriate information to the driver can help. But to date, interfaces of partially automated vehicles have shown evidence of causing cognitive overload. Adaptive HMIs that automatically change the information presented (for example, based on workload, time or physiologically), have been previously proposed as a solution, but little is known about how information should adapt during steady-state driving. This study aimed to classify information usage based on driver experience to inform the design of a future adaptive HMI in partially automated vehicles. The unique feature of this study over existing literature is that each participant attended for five consecutive days; enabling a first look at how information usage changes with increasing familiarity and providing a methodological contribution to future HMI user trial study design. Seventeen participants experienced a steady-state automated driving simulation for twenty-six minutes per day in a driving simulator, replicating a regularly driven route, such as a work commute. Nine information icons, representative of future partially automated vehicle HMIs, were displayed on a tablet and eye tracking was used to record the information that the participants fixated on. The results found that information usage did change with increased exposure, with significant differences in what information participants looked at between the first and last trial days. With increasing experience, participants tended to view information as confirming technical competence rather than the future state of the vehicle. On this basis, interface design recommendations are made, particularly around the design of adaptive interfaces for future partially automated vehicles.

The application of NOMA in UAV networks is an effective solution for communications. However, the security risk becomes more serious with the LoS channels and higher transmit power for weaker users in NOMA-UAV networks. In this paper, a UAV-assisted NOMA transmission scheme is proposed to achieve secure downlink transmission via artificial jamming, where a UAV flies straightly to serve multiple ground users in the presence of a passive eavesdropper. Only the closest users to the UAV can be connected in each time slot to achieve high LoS probability. To balance the security and transmission performance, the tradeoff between the jamming power and the sum rate is investigated by jointly optimizing the power allocation, the user scheduling and the UAV trajectory. To address the problem, we first decompose the problem into two subproblems of power allocation and trajectory optimization. Then, they are transformed into convex ones for an iterative algorithm via the first-order Taylor expansion. Finally, simulation results are presented to show the effectiveness of the proposed scheme.\ud

This study delivers a powerful comparison case for six of the most common ray tracing (RT) source models. It demonstrates that in the early stages of the RT algorithm, when only the ray-geometry intersection and ray-reflectance are introduced, the ray source modelling is a pivotal event in the simulation. The six models are compared in a large three-dimensional (3D) scenario of the well-known double-slit experiment, with the comparison metrics delivered by the number of rays that intersect the back screen and the total simulation time. The numerical results for a variable number of 2, 000; 10, 000; 25, 000 and 100, 000 rays that emulate each of the six source models, are accompanied by the simulation's visual output samples to eliminate abstract ambiguities. This work's main contribution applies directly to the RT simulation for wireless ranging, since scientific programming environments such as MA TLAB are extensively utilised in this research field, which provide the required modelling customisation. Moreover, for machine sensing areas involving optical ranging or light detection and ranging (LIDAR) mapping, the presented study provides valuable information about efficient modelling for ray fascicle launching. Furthermore, since RT simulations enable the latest performances in the gaming and animation industries, the basic and clear information presented in this work supports the next generation of their developers in the delivery of hardware and software implementations.

Identification of test scenarios for Automated Driving Systems (ADSs) remains a key challenge for the Verification & Validation of ADSs. Various approaches including data based approaches and knowledge based approaches have been proposed for scenario generation. Identifying the conditions that lead to high severity traffic accidents can help us not only identify test scenarios for ADSs, but also implement measures to save lives and infrastructure resources. Taking a data based approach, in this paper, we introduce a novel accident data analysis method for generating test scenarios where we analyze UK's Stats19 accident data to identify trends in high severity accidents for test scenario generation. This paper first focuses on the severity of the accidents with the goal of relating it to static and time-dependent internal and external factors in a comprehensive way taking into account Operational Design Domain (ODD) properties, e.g. road, environmental conditions, and vehicle properties and driver characteristics. For this purpose, the paper utilizes a data grouping strategy (coarse-graining) and builds a logistic regression approach, derived from conventional regression models, in which emerging features become more pronounced, while uninteresting features and noise weaken. The approach makes the relationship between the factors and outcome variable more visible and hence well suited for the severity analysis. The method shows superior performance as compared to ordinary logistic models measured by goodness of fit and accounting for model variance $(R^{2}=0.05$ for the ordinary model, $R^{2}=0.85$ for the current model). The model is then used to solve the inverse problem of constructing high-risk pre-crash conditions as test scenarios for simulation based testing of ADSs.

Unmanned aerial vehicles (UAVs) have been widely utilized to improve the end-to-end performance of wireless communications. However, its line-of-sight makes UAV communication vulnerable to malicious eavesdroppers. In this paper, we propose two cooperative dual-UAV enabled secure data collection schemes to ensure security, with the practical propulsion energy consumption considered. We first maximize the worst-case average secrecy rate with the average propulsion power limitation, where the scheduling, the transmit power, the trajectory and the velocity of the two UAVs are jointly optimized. To solve the non-convex multivariable problem, we propose an iterative algorithm based on block coordinate descent and successive convex approximation. To further save the on-board energy and prolong the flight time, we then maximize the secrecy energy efficiency of UAV data collection, which is a fractional and mixed integer nonlinear programming problem. Based on the Dinkelbach method, we transform the objective function into an integral expression and propose an iterative algorithm to obtain a suboptimal solution to secrecy energy efficiency maximization. Numerical results show that the average secrecy rate is maximized in the first scheme with propulsion limitation, while in the second scheme, the secrecy energy efficiency is maximized with the optimal velocity to save propulsion power and improve secrecy rate simultaneously.\ud

Typical battery management systems (BMS) do not provide the opportunity of monitoring each parallel string cell individually, rather considering cells operate at common voltage and current levels. In reality, manufacturing induced characteristics imbalances, pack thermal gradients and interconnection resistances impose uneven states among cells. As such, this paper intends to take advantage of common Equivalent Circuit Modelling (ECM) approaches to investigate the incurring interrelationships among 15 cells connected in parallel. The module is discharged under real-world drive cycle conditions in different scenarios, aligning model parameters variation to literature based evidence to accurately replicate their impact on imbalanced performance. The simulation results indicate a 1% ratio between interconnection resistance and internal resistance has the largest influence on cell-to-cell load current imbalance and thermal gradient among the investigated factors. Second, manufacturing related 25% internal resistance variability and poor cooling system design induced thermal gradient caused non-negligible imbalances in cells’ performance. A 9% cell-to-cell variation of capacity, contrarily, emerged as having a limited impact on pack behaviour, although constant current loads could imply different conclusions. Last, between investigated Z and Ladder pack configurations, the first choice guaranteed a more uniform performance. This work draws attention to the fact that the presence of imbalances not captured by the BMS can have a detrimental impact on the performance of different pack areas. Hence, the developed Activate® ECM pack model will be coupled in future work with HyperStudy® optimization tool to perform sensitivity analysis. This is expected to improve and accelerate the pack design process ensuring diagnostics and prevention of protracting imbalances, lowering pack safety and degradation concerns.

Thanks to the recent advancements in the automotive industry, in smart city infrastructure and in electronics miniaturization, low-power wireless sensors are becoming a reference sensing technology connecting the internet of things (IoT) with the conventional world. This study provides an empirical solution to the modern radio location problem of inside-outside position discrimination for a mobile radio frequency (RF) source. The solution is delivered by a detection system that is fully enclosed inside a modern vehicle cabin, whereas the RF ranging is based solely on the received signal strength indicator (RSSI) and the individual sensor’s directivity achieved through shielding. The RF detection system is provided through a low-power wireless sensing network as a complete 2.4 GHz IEEE 802.15.4 solution, anticipating the future integration of this technology in the next generation of smartphones. The RSSI fingerprinting database, which is derived from empirical outdoor measurements for a range up to 5 m, delivers a consistent performance inside the highly RF-reflective vehicle cabin by exploiting the sensor position and directivity, focused on the front of each seat to avoid future human interference. Moreover, a theoretical propagation model based on Friis’ transmission equation constructed on system parameters shows a high correlation with the RSSI fingerprinting experimental model, supporting the consistency of the empirical model, and demonstrating a similar high inside-outside discrimination. The decision algorithm logics used for inside-outside discrimination illustrate a strong example for sensor group decision based on two spatial thresholds: maximum detection range for outside discrimination and the cabin width for inside discrimination. This study’s location system design creates exploitation possibilities beyond the vehicle environment. Various applications that require complete sensor encasement, such as road flushed traffic sensors or underground systems for parking space occupancy detection may benefit from this work’s findings.

Recent developments in the testing and safety assurances of Automated Driving Systems (ADSs) have shifted from traditional distance-based approach (i.e. vehicle miles travelled) to scenario-based approach. Various studies have been conducted on different aspects of the scenario-based testing workflow such as scenario generation, scenario description language, and scenario analysis. This study intends to build on top of the individual functional modules introduced for scenario-based testing and contributes towards a common test framework, based on the experience gained from the Innovate UK’s OmniCAV project. The Test Framework introduced in this paper consists of: Description of Test Scenarios, Different Types of Testing and Allocation of Tests, Generation of Test cases, Data Collection, & Analysis, Correlation of obtained Results and Evidence Gathering for functional and behavioral verification. The framework and processes defined here have relevance to real-world practice and contribute towards both verifiability and certifiability of ADSs.\ud

Accurate and convenient energy consumption models (ECMs) for rotary-wing unmanned aerial vehicles (UAVs) are important for UAV communication designs. Existing models are complex and inconvenient to use. In this letter, a simple and easy-to-use model with closed-form expression as a function of the initial velocity, acceleration and time duration is derived. Using this model, the UAV flight control parameters, such as polling force and tilt angle, are analyzed in analytical form. Numerical results show the validity and reliability of the proposed model.\ud

Employing unmanned aerial vehicles (UAVs) in millimeter-wave (mmWave) networks as relays has emerged as an appealing solution to assist remote or blocked communication nodes. In this case, the network security becomes a great challenge due to the presence of malicious eavesdroppers. In this paper, we perform a secrecy analysis for a UAV-based mmWave relaying network. We first investigate the relaying scheme without jamming where the UAV decodes and forwards the information from the source to the destination with malicious eavesdropping. Furthermore, to enhance the secrecy performance, we propose a cooperative jamming scheme via utilizing the destination and an external UAV to cooperatively disrupt the eavesdroppers at the two stages of relaying, respectively. Using the probability of line-of-sight (LoS) between the UAV and ground nodes, the three-dimensional (3D) antenna gain, and the Nakagami-m small-scale fading model, the secrecy outage probability (SOP) of the two schemes with and without jamming is analyzed. Closed-form expressions for the SOP of the two schemes are obtained by employing the Gauss-Chebyshev quadrature. Simulation results are presented to validate the theoretical expressions of SOP and to show the effectiveness of the proposed schemes.

When power electronics are deployed under the road surface as part of a wireless system it is important to know that their packaging provides adequate heat extraction as well as the required environmental protection - often conflicting requirements. Presently very little can be found in wireless charging standards and literature on the topic of thermal modelling for in-ground components. Yet, this is a topic of great practical significance especially for in-road systems. Traditional cooling methods are not readily applicable underground. This paper uses finite element thermal modelling to investigate the cooling of a representative medium-power in-road wireless system, housed in a sealed ground assembly (GA) chamber and installed to UK requirements (HAUC). The paper quantitatively compares design options and provides practical recommendations for in-road installation thermal management.\ud

Electric power steering (EPS) plays a critical role towards safety and comfort especially for modern vehicles with ever-increasing complexity. The evolution of EPS technologies require not only vehicle dynamics and fuel efficiency but also higher standards of steering function. Apart from providing essential electromechanical energy conversion, the performance of EPS motor has received attention from industry and academic community in terms of high torque/power density and low level of noise and vibration. This paper details aspects of design and analysis of permanent magnet synchronous motor (PMSM) for EPS application. Furthermore, the concept of multi-phase motor is adopted factoring redundancy design in achieving fail-operational for safety critical applications such as steer-by-wire (SbW) system for autonomous driving. The motor topologies investigated have been modelled utilising Motor-CAD software.\ud

High throughput wireless communication between vehicles and infrastructure will rely on millimeter-wave carriers and technology, which provide access to large bandwidths whilst still being able to establish stable links over distances of hundreds of meters. The work presented here, evaluates the quality of such signals in a mini-urban campus environment, by comparing detailed channel sounding results with signal quality parameters such as the error vector magnitude and the established data throughput. This provides insights into the relationships between fundamental parameters used to establish channel models and the quality of service parameters used in applications and standards. For the environment investigated, a strong threshold dependence of the signal quality on the received power is observed and explained in detail.

Unlike aerial base station enabled by a single unmanned aerial vehicle (UAV), aerial coordinated multiple points (CoMP) can be enabled by a UAV swarm. In this case, the management of multiple UAVs is important. This paper considers the power allocation strategy for a UAV swarm-enabled aerial network to enhance the physical layer security of the downlink transmission, where an eavesdropper moves following the trajectory of the swarm for better eavesdropping. Unlike existing works, we use only the large-scale channel state information (CSI) and maximize the secrecy throughput in a whole-trajectory-oriented manner. The overall transmission energy constraint on each UAV and the total transmission duration for all the legitimate users are considered. The non-convexity of the formulated problem is solved by using max-min optimization with iteration. Both the transmission power of desired signals and artificial noise (AN) are derived iteratively. Simulation results are presented to validate the effectiveness of our proposed power allocation algorithm and to show the advantage of aerial CoMP by using only the large-scale CSI.\ud

The electrification of propulsion technologies in aerospace engineering has been considered as the future-vision for aviation industries. The Selection of electrified propulsion architecture for a particular mission-flight has become a new challenge. In this paper, a method to study different propulsion architectures and battery sizing for jetliners using multi-physics modeling is presented. The designed approach is then carried out to investigate conventional and hybrid/electric propulsion architectures of a commercial jetliner (Avro RJ-85). Based on the comparative study, an effective propulsion architecture is also suggested. The designed method is expected to help predict effective propulsion architecture for future aviation.

In this correspondence, covert air-to-ground communication is investigated to hide the wireless transmission from unmanned aerial vehicle (UAV). The warden's total detection error probability with limited observations is first analyzed. Considering the location uncertainty of the warden, a robust resource allocation and UAV trajectory optimization problem with worst-case covertness constraint is then formulated to maximize the average covert rate. To solve this optimization problem, we propose a block coordinate descent method based iterative algorithm to optimize the time slot allocation, power allocation and trajectory alternately. Numerical results demonstrate the effectiveness of the proposed algorithm in covert communication for UAVs.\ud

This paper aims to review the state of the art of Light Detection and Ranging (LiDAR) sensors for automotive applications, and particularly for automated vehicles, focusing on recent advances in the field of integrated LiDAR, and one of its key components: the Optical Phased Array (OPA). LiDAR is still a sensor that divides the automotive community, with several automotive companies investing in it, and some companies stating that LiDAR is a ‘useless appendix’. However, currently there is not a single sensor technology able to robustly and completely support automated navigation. Therefore, LiDAR, with its capability to map in 3 dimensions (3D) the vehicle surroundings, is a strong candidate to support Automated Vehicles (AVs). This manuscript highlights current AV sensor challenges, and it analyses the strengths and weaknesses of the perception sensor currently deployed. Then, the manuscript discusses the main LiDAR technologies emerging in automotive, and focuses on integrated LiDAR, challenges associated with light beam steering on a chip, the use of Optical Phased Arrays, finally discussing current factors hindering the affirmation of silicon photonics OPAs and their future research directions.

For transport logistics, often the most inefficient part of the journey is the route between distribution centre and end customer. This route, referred to as last-mile delivery, generally uses smaller goods vehicles, to deliver low-volumes to multiple destinations. To optimise this process, route planning optimisation software is used, to maximise the number of deliveries a driver can complete in a day. To further optimise this process, companies are starting to test autonomous goods vehicles (AGVs). This paper presents an evaluation of the impact and barriers of AGVs for last-mile delivery in the UK, by conducting a study of people in the logistics industry and experts in autonomous technology. Qualitative analysis is used to identify positive and negative impacts of the introduction of driverless AGVs, and barriers, in terms of government policy and technical restrictions, which could slow down wide-scale adoption. From the results, we find logistics companies are being pressured to reduce lead-times and offer more predictable delivery-times. This is increasing pressure on the workforce, which already has high-turnover and difficulties in recruitment. Therefore, AGVs are considered a solution to a present problem, which is preventing logistics companies growing and achieving delivery targets, driven by public demand.

Wireless power transfer (WPT) is a promising charging technology for battery-limited sensors. In this paper, we study the use of an unmanned aerial vehicle (UAV) as a charger for WPT. Unlike the previous works, our study takes into account the power consumption of the UAV (power consumption during hovering and flight), the charging process from a base station (BS) to the UAV and the conversion loss of the energy harvester. Both one-dimensional (1D) and two-dimensional (2D) WPT systems are considered. The sum-energy received by all sensors is maximized to find the optimal strategy for UAV deployment. Two different charging schemes are proposed. Numerical results show that the sum-energy received by all sensors is determined by sensors' topology, the flight speed of the UAV and the transmit power. They also show that, when the BS charging process and the UAV power consumption are considered in the optimization, the optimal location of the UAV in the 1D and 2D WPT systems is closer to the BS than in the previous works that ignore these two practical factors.\ud

Latest generations of automobiles are gradually being equipped with technologies that have increasing automation, a trend which had led to increase in the system complexity as well as increased human-automation interactions. Failures in such complex human-automation interactions increasingly occur due to the mismatch between what operators know about the system and what the designers expect operators to know. Causes of road accidents also change due to role shift of drivers from controlling the vehicle to monitoring the in-vehicle controllers. Failures in such complex systems involving human-automation interactions increasingly occur due to the emergent behaviours from the interactions, and are less likely due to reliability of individual components. Traditional safety analysis methods fall short in identifying such emergent failures. This paper focuses on using a systems thinking inspired safety analysis method called System Theoretic Process Analysis (STPA) to identify potential failures. The analysis focuses on a SAE Level-4 Vehicle that is in the development phase, and is controlled partially by a safety driver and its built-in Autonomous Driving System (ADS). The analysis yields that while increase in complexity does increase system functionality, it also brings a challenge to evaluate the safety of the system and potentially causes incorrect human-automation interactions, leading to an accident. After the possible inadequate driver-vehicle interactions are identified by STPA, corresponding requirements were then proposed in order to avoid the unsafe behaviour and thus preventing the hazards.\ud

Electrical Rim Driven Fan (RDF) technology is a novel application to aircraft propulsion, and there are many factors that play a part in determining its optimal achievable performance, for example, the electrical, magnetic and thermal properties of the rim driven motor architectures; the mechanical strength, mass and friction properties of the materials used; the aerodynamics of the fans and the efficiency of the motor drive and control circuitries. Each of these factors could easily warrant their own lengthy and in-depth analyses. The aims of this paper are to provide a starting point from which to get a feel for the rough-order-magnitude of an RDFs performance and to elucidate a conventional calculative methodology suitable for a quick and easy reality-check before undertaking more accurate numerical analysis techniques. Initially, the properties of an existing aerospace fan design, namely that of the IAE V2500-A5 turbo-fan engine, is used to validate this approach and then the same methodology is used to estimate a first-guess performance prediction for a range of single-stage RDFs of varying sizes from 100mm to 500mm diameter, operating over a range of speeds from zero to 25 kRPM. Finally, a comparison between a single-stage and a dual-stage (contra-rotating) 200mm diameter RDF for a UAV application is conducted. The performance limits of the RDFs considered in this analysis have been established to ensure that the fan blades are always operating within the subsonic flow regime.

Rim Driven Fans (RDFs) are receiving increased attention amongst zero-emission aerospace technologists as they offer the potential for high Fan Pressure Ratios (FPR) to be efficiently generated and fan flow to be maximised compared with conventional (hub driven) electrical axial-flow fans. In other words; RDFs can provide higher thrust performance and exhaust air velocities than hub driven devices of the same intake area and also offer significant aerodynamic drag reductions. As such they are a feasible propulsion contender for fixed-wing, distributed-thrust, electrical aircraft designs. Thrust-to-weight ratio is a critical design parameter when considering aircraft propulsion devices and so design mass must be optimised accordingly. This paper presents an electromagnetic comparison of copper versus aluminium, slot-less, brushless DC motor (BLDC), stator-wiring architectures. The results of this study identify the performance benefits and limitations of these differing stator wiring materials in the context of a single rotor-stage RDF intended for the propulsion of a small unmanned aircraft.

Understanding human actions from videos captured by drones is a challenging task in computer vision due to the unfamiliar viewpoints of individuals and changes in their size due to the camera’s location and motion. This work proposes DroneCaps, a capsule network architecture for multi-label human action recognition (HAR) in videos captured by drones. DroneCaps uses features computed by 3D convolution neural networks plus a new set of features computed by a novel Binary Volume Comparison layer. All these features, in conjunction with the learning power of CapsNets, allow understanding and abstracting the different viewpoints and poses of the depicted individuals very efficiently, thus improving multi-label HAR. The evaluation of the DroneCaps architecture’s performance for multi-label classification shows that it outperforms state-of-the-art methods on the Okutama-Action dataset.

OmniCAV is laying the foundations for the development of a comprehensive, robust and secure simulator, aimed at providing a certification tool for Connected Autonomous Vehicles (CAVs) that can be used by regulatory and accreditation bodies, insurers and manufacturers to accelerate the safe development of CAVs. To achieve this, OmniCAV is using highly detailed road maps, together with a powerful combination of traffic management, accident and CCTV data, to create a high-fidelity traffic and driving simulation environment to interact with the AV under test. Scenarios for testing are developed and randomised in a holistic way to avoid CAVs training to specific conditions. Critically, the simulator offers coverage of a representative element of the U.K. road network, through encompassing rural roads, peri-urban and urban roads to enable autonomy for all. The validity of the synthetic test environment compared to the real-world is of particular importance, and OmniCAV will be tested and refined through an iterative approach involving real-world comparisons and working in conjunction with a CAV testbed.\ud

This paper investigates the potential performance of high speed SiC cascode JFETs in EV traction inverters with high switching frequencies. Traction inverters implemented with SiC devices have shown improved energy conversion efficiency compared to IGBT based traction inverters however SiC MOSFETs suffer from unstable threshold voltage due to charge trapping at the SiC/SiO 2 (due to high density of traps). Since SiC cascode JFETs combine low voltage silicon MOSFETs (at the input) with high speed/high-power density SiC JFETs (at the output), cascode JFETs combine the electrical gate oxide reliability of silicon devices with the power density of SiC. This paper simulates an EV driving cycle using experimental power loss measurements (at different currents and temperatures) of commercially available 650V SiC cascode JFETs and SiC MOSFETs. The inverter has been simulated at 10, 25 and 50 kHz to investigate the impact of increased switching frequency on device losses. The model is fully electrothermal since conduction and switching losses have been measured at different junction temperatures and used as inputs to the model. The results show the potential of superior performance of the SiC cascode JFET in terms of power loss and junction temperature swings. Furthermore, since higher switching frequencies might be desirable in future high-speed traction motors, the fast switching and low loss performance of SiC Cascode JFETs becomes more attractive.\ud

Smart cells, instrumented with miniature sensors, can help meet both the consumer and regulatory demands for the battery packs of next-generation of electric vehicles (EVs). Currently, adding sensors to battery packs entails increasing vehicle weight and complexity with the associated wiring loom expansion. In this work, we demonstrate sensors for voltage, current and temperature can be installed on cylindrical cells (21700 format) and connected to a data logger via power line communication (PLC). We achieved zero error rate with our laboratory setup, and transfer times of < 40 ms (relative to dedicated wired connection). This reliable PLC method (max. data transfer rate 115 kbps) was sufficient for logging cell data and can operate over a wide DC voltage range (e.g. target 10 to 60 V typical in an EV). Our initial experiments highlight the lack of understanding of the performance of a cell during its lifetime in an EV, where temperature gradients were observed between the terminals (0.2 °C with a peak increase of +0.6 °C, discharge rate of 0.5 C).

Hybrid digital to analog (D-A) precoding is widely used in millimeter wave systems to reduce the power consumption and implementation complexity incurred by the number of radio frequency (RF) chains that consume a lot of the transmitted power in this system. In this paper, an optimal number of RF chains is proposed to achieve the desired energy efficiency (EE). Here, the optimization problem is formulated in terms of fractional programming maximization, resulting in a method with a twofold novelty: First, the optimal number of RF chains is determined by the proposed bisection algorithm, which results in an optimized number of data streams. Second, the optimal analog precoders/combiners are designed by eigenvalue decomposition and a power iteration algorithm, followed by the digital precoders/combiners which are designed based on the singular value decomposition of the proposed effective uplink and downlink channel gains. Furthermore, the proposed D-A systems are designed carefully to attain a lower complexity than the existing D-A algorithms while achieving reasonable performance. Finally, the impact of utilizing a different number of quantized bits of resolution on the EE is investigated. Simulation results show that the proposed algorithms outperform existing algorithms in terms of EE, spectral efficiency, and computational complexity.\ud

Uncertainty and interconnectedness in complex engineering and engineered systems such as power-grids and telecommunication networks are sources of vulnerability compromising the resilience of these systems. Conditions of uncertainty and interconnectedness change over time and depend on emerging socio-technical contexts, thus conventional methods which can conduct normative, descriptive and prescriptive assessment of complex engineering and engineered systems resilience are limited. This paper brings together contributions of experts in complex engineering and engineered systems who have identified six methods, three each for uncertainty and interconnectedness, which form the foundational methods for knowing complex engineering and engineered systems resilience. The paper has reviewed how these methods contribute to overcoming uncertainty or interconnectedness and how they are implemented using case studies in order to illustrate essential approaches to enhancing resilience. It is hoped that this approach will allow the subject to be quantified and best practice standards to develop.

This paper simulates traffic at the Dartford-Thurrock Crossing Tunnel, Kent, UK. Using a traffic simulation model, Connected and Autonomous Freight Vehicles (CAV-F) are simulated alongside conventional light goods vehicles, to determine the feasibility of increasing the traffic throughput at the tunnel. The results show that with the use of CAV-F, the overall traffic flow is increased by $\sim33$% from current flow of $\sim$5,000 vehicles/hr. With the reduction in the headway and standstill distance and increase in scope of intelligent connectivity and traffic speed limit, the average congestion and travel time are reduced even at a higher traffic concentration. By analysing the results, it has thus been possible to highlight the benefits to traffic management and road utilisation by introducing CAV-F into our road network, in the long term.

Due to its agile maneuverability, unmanned aerial vehicles (UAVs) have shown great promise for ondemand communications. In practice, UAV-aided aerial base stations are not separate. Instead, they rely on existing satellites/terrestrial systems for spectrum sharing and efficient backhaul. In this case, how to coordinate satellites, UAVs and terrestrial systems is still an open issue. In this paper, we deploy UAVs for coverage enhancement of a hybrid satellite-terrestrial maritime communication network. Under the typical composite channel model including both large-scale and small-scale fading, the UAV trajectory and in-flight transmit power are jointly optimized, subject to constraints on UAV kinematics, tolerable interference, backhaul, and the total energy of UAV for communications. Different from existing studies, only the location-dependent large-scale channel state information (CSI) is assumed available, because it is difficult to obtain the small-scale CSI before takeoff in practice, and the ship positions can be obtained via the dedicated maritime Automatic Identification System. The optimization problem is non-convex. We solve it by problem decomposition, successive convex optimization and bisection searching tools. Simulation results demonstrate that the UAV fits well with existing satellite and terrestrial systems, using the proposed optimization framework., 30 pages, 8 figures

Many wireless networks operate in a mobile environment with randomly moving user terminals. This letter analytically characterizes the impact of ground user mobility, propagation environment and channel fading on the outage performance of unmanned aerial vehicle (UAV) communications. Closed-form expressions for the outage probability using the random waypoint model for ground user mobility, UAV channel models for different propagation environments and the Nakagami- ${m}$ model for fading channels are derived. Furthermore, the outage analysis takes into account the effect of co-channel interference by both the stationary and mobile users. Numerical results are presented to demonstrate the interplay between the communication performance and the system parameters.