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\"State-of-the-art in research in this challenging yet crucial and topical field, addressing the challenges associated with sense and avoid systems in UASs/ UAVs in their complexity and entirety. Sense and avoid systems are a key technology in the fastest growing field of aircraft development - unmanned aircraft systems. Sense and Avoid in UAS: Research and Applications addresses the challenges associated with sense and avoid systems in UASs/ UAVs in their complexity and entirety. Encompassing the state-of-the-art in research in this challenging yet crucial and topical field, it isauthored by leading practitioners and researchers from three different continents worldwide working on multi-million research programmes such as ASTRAEA. Highly original, it fulfils the current gap in the published literature on sense and avoid covering views and analyses from sensing to guidance to human factors to regulatory issues. The authors assume some basic knowledge of aviation navigation and aerodynamics, but address principles rather than complex mathematics. Addresses the challenges associated with sense and avoid systems in UASs/ UAVs in their complexity and entirety Fulfils the current gap in published literature on sense and avoid Covers views and analyses from sensing to guidance to human factors to regulatory issues Authored by leading researchers as well as industry practitioners worldwide\"--

The collision avoidance system (CAS) is a mandatory monitoring apparatus equipped in all aircraft to safeguard flight safety. The CAS scans the predefined regions in a systematic manner for a certain length of time to detect any approaching aircraft that could potentially pose a threat. Thus, CAS requires a focused multi-element radiator which can encompass the complete azimuth region. Recent years have seen a growing emphasis on enhancing the efficiency of CAS antennas because of several constraints, such as low gain (3.6 dB), larger dimensions, substantial side-lobe amplitude (− 7 dB), and challenges with beam adaptation. The current research strives to enhance the gain of a CAS antenna by incorporating the basic idea of metamaterials (MTMs). Therefore, a compact floral-shaped double negative (DNG) MTM design is proposed. The CAS antenna routes the signal throughout the complete azimuth region, so the designed MTM must be proficient to withstand its DNG characteristics for different incident angles. Hence, the proposed design is tested at various incident angles spanning between to along the azimuth region, at a deviation. The results indicate that the proposed structure retains its DNG behavior in the desired frequency range, regardless of the incident angles. The computed effective medium ratio of the structure is 13.47 at the CAS central frequency (1.06 GHz), highlighting its compactness and efficacy. Furthermore, to analyze the function of the structure on the antenna, the unit-element (UE) is expanded to a 5 × 4 array and deployed as an additional layer on the radiator at a predetermined distance. The addition of MTM to the radiator outperformed the conventional radiator by enhancing the antenna gain, by 2.6 dB, respectively. Additionally, to confirm the experimental findings, the UE and array designs are fabricated, and the fabrication results align closely with the simulation results.

Ship collision avoidance is a complex process that is influenced by numerous factors. In this study, we propose a novel method called the Optimal Collision Avoidance Point (OCAP) for unmanned surface vehicles (USVs) to determine when to take appropriate actions to avoid collisions. The approach combines a model that accounts for the two degrees of freedom in USV dynamics with a velocity obstacle method for obstacle detection and avoidance. The method calculates the change in the USV’s navigation state based on the critical condition of collision avoidance. First, the coordinates of the optimal collision avoidance point in the current ship encounter state are calculated based on the relative velocities and kinematic parameters of the USV and obstacles. Then, the increments of the vessel’s linear velocity and heading angle that can reach the optimal collision avoidance point are set as a constraint for dynamic window sampling. Finally, the algorithm evaluates the probabilities of collision hazards for trajectories that satisfy the critical condition and uses the resulting collision avoidance probability value as a criterion for course assessment. The resulting collision avoidance algorithm is optimized for USV maneuverability and is capable of handling multiple moving obstacles in real-time. Experimental results show that the OCAP algorithm has higher and more robust path-finding efficiency than the other two algorithms when the dynamic obstacle density is higher.

This work addresses the issue of multi-ship collision avoidance decision-making complex encounter situations, and proposes a novel velocity varying-steering collision avoidance method based on an improved particle swarm optimization (IPSO) algorithm. The proposed method establishes a limited range based on the International Regulations for Preventing Collisions at Sea (COLREGs) and creates a multi-objective model, in which the collision risk of ships, the energy loss caused by velocity varying and the voyage loss caused by steering are taken into account. To obtain an optimal solution of the multi-objective model, an IPSO is introduced to determine the feasible solution domain for ship collision avoidance decision making(CADM). The proposed CADM is validated by numerical simulations and navigation simulator. The results indicate that the recommended velocity and course can effectively remove the risk of collision between the ship and target ships.

Intelligent Transportation System (ITS) is continuously evolving alongside communication technologies and autonomous driving, giving way to new applications and services. Considering the significant rise in traffic casualties, protecting vulnerable road users (VRU), such as pedestrians, cyclists, motorcycles, animals, etc., has become ever more critical. That said, technological advances alone can not meet the requirements of such crucial applications. Therefore, combining them with architectural revolutions, particularly cloud, fog, and edge computing, is essential. In this review, we scrutinize the VRU safety application with regard to technological evolution. This review establishes the foundations for designing resilient, more reliable, end-to-end VRU protection services. It illustrates the possibility of combining the performance of different technologies through exploiting 5G architectural advantages (function placement, direct/indirect communication, etc.) for the intended application. In the context of 5G architecture, collision avoidance systems consider network and application-related challenges and solutions. This survey provides standardization, studies, and project efforts related to the use case and considers the different types of messages in the V2VRU communication-based safety application. We investigate how adapting the application parameters to the network state and devices’ available resources can use network resources efficiently and provide reliable services.

According to the statistics of maritime accidents, most collision accidents have been caused by human factors. In an encounter situation, the prediction of ship’s trajectory is a good way to notice the intention of the other ship. This paper proposes a methodology for predicting the ship’s trajectory that can be used for an intelligent collision avoidance algorithm at sea. To improve the prediction performance, the density-based spatial clustering of applications with noise (DBSCAN) has been used to recognize the pattern of the ship trajectory. Since the DBSCAN is a clustering algorithm based on the density of data points, it has limitations in clustering the trajectories with nonlinear curves. Thus, we applied the spectral clustering method that can reflect a similarity between individual trajectories. The similarity measured by the longest common subsequence (LCSS) distance. Based on the clustering results, the prediction model of ship trajectory was developed using the bidirectional long short-term memory (Bi-LSTM). Moreover, the performance of the proposed model was compared with that of the long short-term memory (LSTM) model and the gated recurrent unit (GRU) model. The input data was obtained by preprocessing techniques such as filtering, grouping, and interpolation of the automatic identification system (AIS) data. As a result of the experiment, the prediction accuracy of Bi-LSTM was found to be the highest compared to that of LSTM and GRU.

Driver Readiness (DR) refers to the likelihood of drivers successfully recovering control from automated driving and is correlated with collision avoidance. When designing Driver Monitoring Systems (DMS) it is useful to understand how driver states and DR interact, through predictive modelling of collision probability. However, collisions are rare and generate imbalanced datasets. Whilst rebalancing can improve model stability, reliability of correction methods remains untested in automotive research. Furthermore, it is not yet clear the extent to which certain features of driver state are associated with the probability of a collision during critical scenarios. The current study therefore had two general aims. The first was to examine statistical model reliability when using imbalance-corrected datasets; the second was to investigate the predictive utility of gaze entropy and pupil diameter in assessing collision risk during critical transitions of control from a simulated hands-off SAE L2 driving experiment. Dataset rebalancing reduced prediction accuracy and overestimated collision probabilities, aligning with prior findings on its limitations. Erratic, spatially distributed gaze fixations were associated with higher collision probability, whilst increased mental workload (indexed via mean pupil diameter) had minimal impacts. We discuss why in many situations researchers should be wary of rebalancing their datasets, and underscore gaze behaviour’s importance in DR estimation and the challenges of dataset rebalancing for predictive DR modelling.

Most maritime accidents are caused by human errors or failures. Providing early warning and decision support to the officer on watch (OOW) is one of the primary issues to reduce such errors and failures. In this paper, a quantitative real-time multi-ship collision risk analysis and collision avoidance decision-making model is proposed. Firstly, a multi-ship real-time collision risk analysis system was established under the overall requirements of the International Code for Collision Avoidance at Sea (COLREGs) and good seamanship, based on five collision risk influencing factors. Then, the fuzzy logic method is used to calculate the collision risk and analyze these elements in real time. Finally, decisions on changing course or changing speed are made to avoid collision. The results of collision avoidance decisions made at different collision risk thresholds are compared in a series of simulations. The results reflect that the multi-ship collision avoidance decision problem can be well-resolved using the proposed multi-ship collision risk evaluation method. In particular, the model can also make correct decisions when the collision risk thresholds of ships in the same scenario are different. The model can provide a good collision risk warning and decision support for the OOW in real-time mode.

Autonomous decision-making for ships to avoid collision is core to the autonomous navigation of intelligent ships. In recent years, related research has shown explosive growth. However, owing to the complex constraints of navigation environments, the Convention of the International Regulations for Preventing Collisions at Sea, 1972 (COLREGs), and the underactuated characteristics of ships, it is extremely challenging to design a decision-making algorithm for autonomous collision avoidance (CA) that is practically useful. Based on the investigation of many studies, current decision-making algorithms can be attributed to three strategies: alteration of course alone, alteration of speed alone, and alteration of both course and speed. This study discusses the implementation methods of each strategy in detail and compares the specific ways, applicable scenes, and limiting conditions of these methods to achieve alteration of course and/or speed to avoid collision, especially their advantages and disadvantages. Additionally, this study quantitatively analyzes the coupling mechanisms of alterations of course and speed for autonomous CA decision-making under different encounter situations, supplementing and optimizing the decision-making theory for ship autonomous CA. Finally, several feasible algorithms and improvement schemes for autonomous CA decision-making, combined with course and speed alterations, are discussed.

Aiming at the problem that in the research and development process of the intelligent ship perception system, the fuzzy understanding of the perception demand causes the perception equipment to be inconsistent with the ship’s autonomous collision avoidance needs, based on the application practice of the 1972 “International Maritime Collision Avoidance Regulations” and the good marine craftsmanship of the mariners Based on the comparison and experience summary method, the ship pilot’s perception of navigation obstacles is analyzed, and the requirements for intelligent ship navigation perception at sea are proposed from five aspects: vision, ARPA radar, AIS, human-computer interaction, and electronic chart, it can provide theoretical support for the research of intelligent ship sensing system.