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The airport apron is a high-risk area for aircraft collisions due to its heavy operational load and high aircraft density. Currently, existing quantitative models for apron collision risk provide limited consideration and classification of risk areas. In response, this paper proposes a Petri net-based method for assessing aircraft collision risk. The method predicts the probability of aircraft reaching different areas at different times based on operational data, enabling the calculation of collision risks within the Petri net framework. This approach highlights areas with potential collision risks and provides a classification evaluation. Subsequently, aircraft path re-planning is carried out to reduce collision risks. The model simplifies the complex operations of the apron system, making the calculation process clearer. The results show that, during the mid-phase of aircraft taxiing, there is a significant deviation between the actual and ideal positions of aircraft. Areas with high taxiway occupancy are more prone to collision risks. On peak days, due to relatively high flight volumes, the frequency of collision risks is 14% higher than on regular days, with an average risk increase of 23.3%, and the risks are more concentrated. Therefore, reducing collision risks through path planning becomes more challenging. It is recommended to focus attention on areas with high taxiway occupancy during peak periods and carefully plan routes to ensure apron safety. [ABSTRACT FROM AUTHOR]

Low-altitude airspace is developing rapidly, but the utilization rate of airspace resources is low. Therefore, in order to solve the problem of the safe operation of the fusion of large UAVs and manned aircraft in the same airspace, this paper analyzes the theoretical calculation of the collision risk of the fusion operation of manned aircraft and UAVs at Feng Ming Airport in Zigong, verifying that while assessing the safety spacing of 10 km in the lateral direction, it further simulates the possibility of calculating the theoretical smaller safety spacing. The study will propose a new theory of error spacing safety margin and improve it according to the traditional Event collision risk model, combining the error spacing safety margin to establish an improved collision model more suitable for the fusion operation of manned and unmanned aircraft and reduce the redundancy of calculation. The error factors affecting manned and unmanned aircraft at Zigong Airport are analyzed, and theoretical calculations are analyzed by combining the actual data of Zigong Airport. Finally, the Monte Carlo simulation method is used to solve the error, substitute the calculation results, and simulate a section of the trajectory of the fusion operation for the reverse argument. The theoretical calculation results show that the collision risk from 10 km to 8 km satisfies the lateral target safety level (TSL) specified by ICAO under both traditional and improved models. The collision risk calculated by the improved model incorporating the error spacing safety margin is smaller, which enhances the safety of the model calculations. The results of the study can provide theoretical references for the fusion operation of manned and unmanned aircraft. [ABSTRACT FROM AUTHOR]

Autonomous vehicles are rapidly advancing and have the potential to revolutionize transportation in the future. This paper primarily focuses on vehicle motion trajectory planning algorithms, examining the methods for estimating collision risks based on sensed environmental information and approaches for achieving user-aligned trajectory planning results. It investigates the different categories of planning algorithms within the scope of local trajectory planning applications for autonomous driving, discussing and differentiating their properties in detail through a review of the recent studies. The risk estimation methods are classified and introduced based on their descriptions of the sensed collision risks in traffic environments and their integration with trajectory planning algorithms. Additionally, various user experience-oriented methods, which utilize human data to enhance the trajectory planning performance and generate human-like trajectories, are explored. The paper provides comparative analyses of these algorithms and methods from different perspectives, revealing the interconnections between these topics. The current challenges and future prospects of the trajectory planning tasks in autonomous vehicles are also discussed. [ABSTRACT FROM AUTHOR]

In recent years, the application prospect of urban logistics unmanned aerial vehicles has attracted extensive attention. The high-density operation of UAVs requires autonomous separation maintenance capability. To achieve autonomous separation maintenance, it is necessary to conduct autonomous track prediction and formulate the required separation accordingly. Based on the target level of safety requirements for UAV operation, aiming at the autonomous separation maintenance ability of UAVs and considering the accuracy of track prediction, a method to calculate the required separation between UAVs is proposed. This study consists of two parts. Firstly, based on historical data, the position prediction error of the flight track is investigated. Using a machine learning model, a two-stage track prediction method, which involves classification followed by prediction, is proposed for urban logistics UAV track data. Subsequently, based on the track prediction error distribution, by designing a gas model and a position error probability model, a separation-formulating model for urban logistics UAVs in free flight is proposed in which UAV maneuverability is considered. By applying this model, the required separation is formulated for UAVs. When the required separation is set to 48.5 m, the overall collision risk meets the TLS requirements. The research provides a feasible method for establishing autonomous separation for urban logistics UAVs. [ABSTRACT FROM AUTHOR]

When car following is controlled by human drivers (i.e., by their behavior), the traffic system does not meet stability conditions. In order to ensure the safety and reliability of self-driving vehicles, an additional hazard warning system should be incorporated into the adaptive control system in order to prevent any possible unavoidable collisions. The time to contact is a reasonable indicator of potential collisions. This research examines systems and solutions developed in this field to determine collision times and uses various alarms in self-driving cars that prevent collisions with obstacles. In the proposed analysis, we have tried to classify the various techniques and methods, including image processing, machine learning, deep learning, sensors, and so on, based on the solutions we have investigated. Challenges, future research directions, and open problems in this important field are also highlighted in the paper. [ABSTRACT FROM AUTHOR]

With the rapid development of unmanned aerial vehicle technology and its increasing application across various fields, current airspace resources are insufficient for unmanned aerial vehicles' needs. This paper, taking Zigong General Aviation Airport in Sichuan as a case study, explores the lateral safety spacing in a mixed operation mode of unmanned aerial vehicles and manned aircraft. Currently, there are no standardized regulations for the safe spacing of the fusion operation of unmanned and manned aircraft. Theoretical research is essential to provide a reference for actual operations. It introduces the UM-Event (unmanned and manned aircraft-event) collision risk model, an adaptation of the Event collision risk model, considering factors like communication, navigation, surveillance performance, human factors, collision avoidance equipment performance, and meteorology. Safety spacing was determined via simulation experiments and actual data analysis, adhering to the target safety level (TSL). Findings indicate that surveillance performance has a minor impact on safety spacing, while communication and navigation significantly influence it. The safety spacing, influenced solely by CNS (communication performance, navigation performance, surveillance performance) and combined factors, increased from 4.42 to 4.47 nautical miles. These results offer theoretical guidance for unmanned aerial vehicle safety in non-segregated airspace. [ABSTRACT FROM AUTHOR]

As one of the key technologies restricting the development of intelligent ships, autonomous collision avoidance has attracted the attention of many scholars all over the world. Existing research on collision-avoidance behavior focuses more on collision risk assessment and local path-planning methods for studies on the human-like sequential logic of the whole collision-avoidance process, as well as the decision-making process of various stages. Further in-depth thinking is needed urgently. Based on this, a construction method of a human-like sequential decision chain for the autonomous collision avoidance of unmanned ships is proposed through the construction of a collision-avoidance rule base and strategy set, efficient data access based on the Knowledge Graph concept, global collision risk assessment considering sequential decision process, and the construction of a complete collision-avoidance logic process to simulate the decision-making process of humans in complex multi-ship encounters in open waters. For multi-ship encounter scenarios, considering the sequential decision-making process of collision avoidance, a method was proposed to divide the collision risk of the target ship into direct collision risk and potential collision risk. The validity and reliability of the constructed sequential decision chain are verified by simulation experimental results. The results show that the method is effective for collision avoidance (especially multi-ship collision avoidance) in open waters and can provide a theoretical basis and technical support with good interpretability for the decision-making process of an unmanned ship's autonomous collision avoidance. [ABSTRACT FROM AUTHOR]

Commercial development of tidal stream energy is hampered by technical and financial challenges, and impeded by uncertainty about potential environmental effects that drive environmental risk assessments and permitting (consenting) processes. The effect of greatest concern for operational tidal stream energy devices is the potential for marine animals to collide with turbine blades, resulting in injury or death. Due to the turbulent and often turbid waters that frequently characterize tidal turbine sites, there is an absence of empirical evidence about collisions with marine animals. This paucity of observations often leads to risk-averse permitting decisions that further restrict the deployment of tidal energy devices that are needed to collect this evidence. This paper relies on the framework of stressors and receptors that is widely used in marine energy studies and outlines a stepwise probabilistic methodology that applies existing knowledge to further elucidate the risk to marine animals from operational tidal turbines. A case study using striped bass from the Bay of Fundy, Canada, accompanies the methodology, to partially demonstrate its application. [ABSTRACT FROM AUTHOR]

Understanding how marine animals behave around tidal turbines is essential if we are to quantify how individuals and populations may be affected by the installation of these devices in the coming decades. Our particular interest is in collision risk, and how this may be affected by the fine-scale behaviour of seals and small cetacean species around devices. We report on a study in which multibeam sonar data were collected close to an operational tidal turbine in Scotland continuously over a twelve-month period. The sonars provide high-resolution (a few cm) data over a 120° angle out to a range of 55 m at a rate of 10 frames per second. We describe a system which uses automatic computer algorithms to detect potential targets of interest, verified by human analysts using a sophisticated computer user interface to confirm detections and assign target species. To date, we have identified 359 tracks of marine mammals in the data, as well as several thousand tracks from fish and diving birds. These are currently being parameterised to study how these species react to the moving turbine rotors, and the data are now being used to explore the development of improved automated detection and classification algorithms. [ABSTRACT FROM AUTHOR]

In recent years, the increasing volume and complexity of ship traffic has raised the probability of collision accidents in ports, waterways, and coastal waters. Due to the relative rarity of collision accidents, near misses have been used in the research to study the collision risk in the relevant water areas. However, the factor of near miss identification is usually limited to the relative distance between ships, and the instantaneous quantification and geographical distribution of collision risk is not paid enough attention. Therefore, this article proposed a domain-based regional collision risk model that can quantify the collision risk by detecting near miss scenarios. The proposed model is capable of quantifying the collision risk in the water area instantaneously and periodically and can be used to depict the geographical distribution of collision risks in combination with a grid method and the spatial interpolation technique. To validate the proposed model, some experimental case studies were carried out using automatic identification system (AIS) data from the Bohai Strait. The results show the capability and advantage of the proposed model in regional collision risk identification and visualization, which is helpful for maritime surveillance when monitoring and organizing ship traffic and may therefore contribute to the improvement of maritime safety. [ABSTRACT FROM AUTHOR]

Different toll collection types of vehicles and different distribution of tollbooths lead to the toll plaza diverging area becoming a typical vehicle weaving area with frequent crossing behaviors and conflicts on highways. This study aims to identify contributing factors to conflict risks of four RP by developing random parameters ordered logit models with heterogeneity in means and variances. The model can flexibly capture the unobserved heterogeneity of the contributing factors in different vehicle-following patterns. Real-world vehicle trajectory data obtained from the toll plaza diverging area in Nanjing, China, are used for model estimation. The results show that vehicle-following patterns with the same toll collection types have a higher percentage of severe conflict risks. The average acceleration of the following vehicles, lane-marking indicator, the initial lanes and lane changes of vehicles are significantly associated with the collision risk levels. The standard deviation of surrogate safety measures of all vehicles in sub-segments are found to differ significantly between vehicle-following patterns. Furthermore, a series of likelihood ratio tests are adopted to test the spatial dependence in sub-segments of the diverging area. The findings of this study could provide valuable information for safety improvement in toll plazas. [ABSTRACT FROM AUTHOR]

With the increasing volume of ship traffic, maritime traffic safety is facing a great challenge because the traffic in port becomes more and more crowded and complicated, which will make ship collisions more likely to happen. As a special water area of the port, the anchorage is also threatened by collision risk all the time. For accurately assessing the collision risk in anchorage and its adjacent waters in real time, this paper proposed an analytic model based on Automatic Identification System (AIS) data. The proposed anchorage collision risk model was established in microscopic, macroscopic, and complexity aspects, which considered ship relative motion, anchorage characteristics, and ship traffic complexity, respectively. For validation, the AIS data of the anchorages near the Shandong Peninsular were used to carry out a series of experiments. The results show that the proposed model can identify the anchorage collision risk effectively and has an advantage in dealing with complicated scenarios. The proposed anchorage collision risk model can help maritime surveillance better monitor and organize the ship traffic near the port and provide mariners with a reference about the collision risk situation of the anchorage on their route, which are important to improving maritime traffic safety. [ABSTRACT FROM AUTHOR]

The demand for air mobility services will depend on the safety of these operations but also on the transportation time savings in congested urban areas. An adequate air space structure is therefore essential to achieve both objectives. Corridors, the most extended solution proposed nowadays, can meet the safety requirements necessary for air taxi operations, but they are rigid (point-to-point solutions) and would increase delays. As an alternative, this paper presents the airspace structure proposed in the SESAR AMU-LED Project, based on layers to assure both safety and efficiency of air taxi operations. In this proposal, small UAS will fly in the bottom part, called the Very Low Level, whereas air taxis will fly in the upper part. The paper applies a collision risk model to determine the minimum required safety buffer between both layers to assure the necessary safety levels. The results obtained show that a buffer of 10 m between them would meet the required safety levels for air taxi operations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The increasing number of wind energy sites developed globally, has consequently resulted in a green-on-green predicament, due to an increase in avian mortality caused by collisions with wind turbines. The proportion of collision-related fatalities is not evenly distributed across species, indicating that some species groups are more prone to turbine collision. Such differences between species have been proposed to be affiliated with species-specific foraging and flight behavior. The aim of this study is to investigate how the flight behavioral traits; head position, active flight, track symmetry, and track tortuosity can be used to model collision risk along with other influencing factors i.e., weather variables (temperature, wind speed, and cloud coverage) and temporal variables (time of day and time of year). The study also sought to investigate the species-specificity of the four traits in relation to the phylogenetic relatedness of the study species. This was achieved through a case study at a wind farm on the Swedish island of Gotland in which the behavior of birds from 11 different genera was studied. The flight behavior of these species was assessed using data collected by the IdentiFlight system, e.g., flight trajectories and images of the birds throughout their flight track. The results confirm the species-specificity of the four flight behaviors and indicated that all four traits can be used to predict collision risk along with species as a categorical factor. The framework provided in this study along with the results of the case study can be used to identify risk-prone species based on phylogenetic relatedness and flight behavior. [ABSTRACT FROM AUTHOR]

Although the International Regulations for Preventing Collision at Sea (COLREGs) provide guidelines for determining the encounter relations between vessels and assessing collision risk, most collision accidents occur in crossing situations. Accordingly, prior studies have investigated methods to identify the relation between the give-way and stand-on vessels in crossing situations to allow the stand-on vessel to make the optimal collision-avoidance decision. However, these studies were hindered by several limitations. For example, the collision risk at the current time (t) was evaluated as an input variable obtained at the current time (t), and collision-avoidance decisions were made based on the evaluated collision risk. To address these limitations, a collision risk prediction system was developed for stand-on vessels using a fuzzy inference system based on near-collision (FIS-NC) and a sequence model to facilitate quicker collision avoidance decision making. This was achieved by predicting the future time point (t + i) collision risk index (CRI) of the stand-on vessel at the current time point (t) when the own-ship is determined to be the stand-on vessel in different encounter relations. According to the performance verification results, navigators who use the developed system to predict the CRI are expected to avoid collisions with greater clearance distance and time. [ABSTRACT FROM AUTHOR]

Autonomous surface vehicles need to be at least as safe as conventional vessels, if not safer, when navigating on waters. With a great deal of navigation algorithms for surface vessels out there, the safety of their produced paths is questionable, and, in most cases, complicated to assess and compare. Hence, this paper proposes a method for extended collision risk assessment for paths generated by autonomous navigation algorithms as follows: (1) static, dynamic, and historic risk factors are calculated; (2) individual risk value is determined using a fuzzy inference system; (3) the extended collision risk assessment (ECRA) score is acquired using a root-mean-square method. Finally, a comparison of the ECRA score of each path determines the path with the lowest risk. The validation results show that the proposed method is able to detect lower/higher risk scenarios and assign an adequate risk value in most cases. Risk reduction for cautious paths varies up to 8.43%, while risk increases for incautious paths—up to 57.98%. The results indicate that the method could be used for navigation algorithm evaluation and comparison with some improvements. This research also reveals several promising future directions and applications of the method. [ABSTRACT FROM AUTHOR]

A diversified energy portfolio may include marine energy in the form of current energy converters (CECs) such as tidal or in-river turbines. New technology development in the research stage typically requires monitoring for environmental effects. A significant environmental effect of concern for CECs is the risk of moving parts (e.g., turbine blades) colliding with animals such as fishes. CECs are installed in energetic locations in which it is difficult to operate sensors to fulfill monitoring requirements for informing collision risk. Collecting data (i.e., about blade strikes or near-misses) that inform interactions of fishes with CECs is usually attempted using active acoustic sensors or video cameras (VCs). Limitations of low-light conditions or water turbidity that preclude effective use of VCs are overcome by using high-resolution multibeam echosounders (or acoustic cameras (ACs)). We used an AC at two sites to test its ability to detect artificial and real fish targets and determine if strike, near-miss, and near-field behavior could be observed. Interactions with fish and artificial targets with turbines have been documented but strike confirmation with an AC is novel. The first site was in a tidal estuary with a 25 kW turbine and water clarity sufficient to allow VC data to be collected concurrently with AC data showing turbine blade strike on tethered artificial fish targets. The second site was a turbid, debris-laden river with a 5 kW turbine where only AC data were collected due to high water turbidity. Data collection at the second site coincided with downstream Pacific salmon (Oncorhynchus spp.) smolt migration. Physical fish capture downstream of the turbine was performed with an incline plane trap (IPT) to provide context for the AC observations, by comparing fish catches. Discrimination between debris and fishes in the AC data was not possible, because active movement of fishes was not discernable. Nineteen fishes were released upstream of the turbine to provide known times of possible fish/turbine interactions, but detection was difficult to confirm in the AC data. ACs have been used extensively in past studies to count large migratory fish such as Pacific salmon, but their application for small fish targets has been limited. The results from these two field campaigns demonstrate the ability of ACs to detect targets in turbid water and observe blade strikes, as well as their limitations such as the difficulty of distinguishing small fishes from debris in a high-energy turbid river. Recommendations are presented for future applications associated with CEC device testing. [ABSTRACT FROM AUTHOR]

According to International Regulations for Preventing Collision at Sea, collision avoidance started from assessing the collision risk. In particular, the radar was mentioned as suitable equipment for observation and analysis of the collision risk. Thus, many researches have been conducted by utilizing the radar. Fuzzy Inference System based on Type-1 Fuzzy Logic (T1FIS) using Distance to Closest Point of Approach ( D C P A ) and Time to Closest Point of Approach ( T C P A ) computed via the radar has been largely used for assessing the collision risk. However, the T1FIS had significant limitations on the membership function not including linguistic and numerical uncertainties. In order to solve the issue, we developed the Fuzzy Inference System based on Interval Type-2 Fuzzy Logic (IT2FIS) as follows: (i) the T1FIS was selected among proposed methods based on the type-1 fuzzy logic; (ii) we extended the T1FIS into the IT2FIS by gradually increasing the Footprint of Uncertainty (FOU) size taking into consideration symmetry, and (iii) numerical simulations were conducted for performance validation. As a result, the IT2FIS using the FOU size "±5%" (i.e., interval 10% between upper membership function and lower membership function) not only computed the appropriate and linear collision risk index smoothly until near-collision situation but also help to overcome uncertainties that exist in real navigation environments. [ABSTRACT FROM AUTHOR]

Uncertainty surrounding the potential environmental impacts of marine energy (ME) has resulted in extensive and expensive environmental monitoring requirements for ME deployments. Recently, there have been more ME deployments and associated environmental data collection efforts, but no standardized methodologies for data collection. This hinders the use of previously collected data to inform new ME project permitting efforts. Triton Field Trials (TFiT), created at the Pacific Northwest National Laboratory by the United States (U.S.) Department of Energy, explores ways to promote more consistent environmental data collection and enable data transferability across ME device types and locations. Documents from 118 previous ME projects or ME-related research studies in the U.S. and internationally were reviewed to identify the highest priority stressor–receptor relationships to be investigated and the technologies and methodologies used to address them. Thirteen potential field sites were assessed to determine suitable locations for testing the performance of relevant monitoring technologies. This introductory paper provides an overview of how priority research areas and associated promising technologies were identified as well as how testing locations were identified for TFiT activities. Through these scoping efforts, TFiT focused on four activity areas: collision risk, underwater noise, electromagnetic fields, and changes in habitat. Technologies and methodologies were tested at field sites in Alaska, Washington, California, and New Hampshire. Detailed information on the effectiveness of the identified methodologies and specific recommendations for each of the four focus areas are included in the companion papers in this Special Issue. [ABSTRACT FROM AUTHOR]

Understanding the environmental effects of marine energy (ME) devices is fundamental for their sustainable development and efficient regulation. However, measuring effects is difficult given the limited number of operational devices currently deployed. Numerical modeling is a powerful tool for estimating environmental effects and quantifying risks. It is most effective when informed by empirical data and coordinated with the development and implementation of monitoring protocols. We reviewed modeling techniques and information needs for six environmental stressor–receptor interactions related to ME: changes in oceanographic systems, underwater noise, electromagnetic fields (EMFs), changes in habitat, collision risk, and displacement of marine animals. This review considers the effects of tidal, wave, and ocean current energy converters. We summarized the availability and maturity of models for each stressor–receptor interaction and provide examples involving ME devices when available and analogous examples otherwise. Models for oceanographic systems and underwater noise were widely available and sometimes applied to ME, but need validation in real-world settings. Many methods are available for modeling habitat change and displacement of marine animals, but few examples related to ME exist. Models of collision risk and species response to EMFs are still in stages of theory development and need more observational data, particularly about species behavior near devices, to be effective. We conclude by synthesizing model status, commonalities between models, and overlapping monitoring needs that can be exploited to develop a coordinated and efficient set of protocols for predicting and monitoring the environmental effects of ME. [ABSTRACT FROM AUTHOR]

Maritime accidents such as ship collisions pose continuous risks to individuals and society with due to their severe consequences on human life, economic and environmental losses, etc. Supervising the maritime traffic in the different regions and maintaining its safety level is an essential task for stakeholders such as maritime safety administrations. In this research, a new ship collision risk analysis method is developed with the utilisation of AIS (Automatic Identification System) data. A velocity obstacle-based risk measurement is applied to measure the risk of collision between multiple ships from the velocity perspective, based on which, the collision risk and the complexity of the encounter situation are obtained at the same time. Secondly, a density-based clustering technique is introduced to identify the hotspots of ship traffic in the region as an indicator for maritime safety operators. A case study using historical AIS data was implemented to verify the effectiveness of the proposed approach in a manner that simulates the real-time data scenario. Furthermore, a comparison between existing risk analysis method is conducted to validate the proposed method. [ABSTRACT FROM AUTHOR]

The growth of the marine renewable energy sector requires the potential effects on marine wildlife to be considered carefully. For this purpose, utilization distributions derived from animal-borne biologging and telemetry data provide accurate information on individual space use. The degree of spatial overlap between potentially vulnerable wildlife such as seabirds and development areas can subsequently be quantified and incorporated into impact assessments and siting decisions. While rich in information, processing and analyses of animal-borne tracking data are often not trivial. There is therefore a need for straightforward and reproducible workflows for this technique to be useful to marine renewables stakeholders. The aim of this study was to develop an analysis workflow to extract utilization distributions from animal-borne biologging and telemetry data explicitly for use in assessment of animal spatial overlap with marine renewable energy development areas. We applied the method to European shags (Phalacrocorax aristotelis) in relation to tidal stream turbines. While shag occurrence in the tidal development area was high (99.4%), there was no overlap (0.14%) with the smaller tidal lease sites within the development area. The method can be applied to any animal-borne bio-tracking datasets and is relevant to stakeholders aiming to quantify environmental effects of marine renewables. [ABSTRACT FROM AUTHOR]

Ship collision avoidance measures are important for reducing marine accidents caused by human factors and various natural environmental factors and can also prevent property loss and casualties. In recent years, various methods have been used to study collision avoidance, including ship domain models. This paper proposes a ship domain model based on fuzzy logic aimed at providing early warning of ship collision risk and a reasonable reference that can be used in combination with the International Regulation for Preventing Collisions at Sea (COLREGs). The composition fuzzy inference combining more than one fuzzy inference process is first used to introduce as many factors as possible related to ship collision risk for calculating the ship domain. In this way, the calculation of the ship domain size is more accurate, and a more accurate reference can be provided to sailors, which could save both time and labor by reducing errors. A fuzzy inference system based on if-then fuzzy rules was established in MATLAB and simulation experiments were conducted. The simulation results suggest that the proposed method is feasible and can help sailors make subjective decisions to effectively avoid the occurrence of collision accidents. [ABSTRACT FROM AUTHOR]

In order to avoid collision, ships usually take collision avoidance manoeuvres, such as course alteration. However, the effect of such a manoeuvre is influenced by ship manoeuvrability. This paper proposed an improved danger sector model to identify the collision risk between two encountering ships. The proposed model is improved from the danger sector model, which is based on a course alteration manoeuvre by taking ship manoeuvrability into consideration. To involve ship manoeuvrability in the proposed model, the turning circle of a ship is determined when it calculates the size of the danger sector. For validating the proposed model, the Automatic Identification System (AIS) data in the northern Yellow Sea of China were used to carry out some experiments of different encountering scenarios. The results show that the proposed improved danger sector model can identify the collision risk between two encountering ships effectively and has advantages compared with the original danger sector model. The proposed model can help mariners and maritime surveillance operators have a more accurate and effective assessment of ship collision risk in the water area, which can contribute to the improvement of navigational safety. [ABSTRACT FROM AUTHOR]

Expansion of marine aquaculture into more remote areas will likely accelerate over the next decade. Integrating Marine Renewable Energy (MRE) generation technologies (e.g., wind turbines) into remote, off-grid aquaculture sites will reduce reliance on fossil fuels by allowing localised low-carbon power generation, but may also result in novel environmental pressures. In this study, we undertook a thought experiment to assess the potential for increased collision risks to local marine and coastal bird species of integrating small wind turbines (4 units; combined capacity of 200 MWh) into a generalised marine fish farm in western Scotland (UK). Potential risks to bird species were assessed using a bespoke Sensitivity Index (SI) based on 12 factors, including population size, adult survival rate, UK conservation status, flight manoeuvrability, nocturnal flight activity, habitat preference, sensitivity to wind farms, attraction to fish farms for feeding and/or resting, and attraction to other marine anthropogenic structures/activities. SI scores varied substantially between species, but large gulls (Larus sp.) and European shag (Phalacrocorax aristotelis) were expected to be at the greatest potential risk. The general lack of information on interactions between birds and fish farms represented a significant knowledge gap, and greater focus on these interactions is needed to improve future risk assessments. [ABSTRACT FROM AUTHOR]

The continuous growth in maritime traffic and recent developments towards autonomous navigation have directed increasing attention to navigational safety in which new tools are required to identify real-time risk and complex navigation situations. These tools are of paramount importance to avoid potentially disastrous consequences of accidents and promote safe navigation at sea. In this study, an adaptive ship-safety-domain is proposed with spatial risk functions to identify both collision and grounding risk based on motion and maneuverability conditions for all vessels. The algorithm is designed and validated through extensive amounts of Automatic Identification System (AIS) data for decision support over a large area, while the integration of the algorithm with other navigational systems will increase effectiveness and ensure reliability. Since a successful evacuation of a potential vessel-to-vessel collision, or a vessel grounding situation, is highly dependent on the nearby maneuvering limitations and other possible accident situations, multi-vessel collision and grounding risk is considered in this work to identify real-time risk. The presented algorithm utilizes and exploits dynamic AIS information, vessel registry and high-resolution maps and it is robust to inaccuracies of position, course and speed over ground records. The computation-efficient algorithm allows for real-time situation risk identification at a large-scale monitored map up to country level and up to several years of operation with a very high accuracy. [ABSTRACT FROM AUTHOR]

White storks (Ciconia ciconia) are birds that make annual long-distance migration flights from their breeding grounds in the Northern Hemisphere to the south of Africa. These trips take place in the winter season, when the temperatures in the North fall and food supply drops. White storks, because of their large size, depend on the wind, thermals, and orographic characteristics of the environment in order to minimize their energy expenditure during flight. In particular, the birds adopt a soaring behavior in landscapes where the thermal uplift and orographic updrafts are conducive. By attaining suitable soaring heights, the birds then use the wind characteristics to glide for hundreds of kilometers. It is therefore expected that white storks would prefer landscapes that are characterized by suitable wind and thermal characteristics, which promote the soaring and gliding behaviors. However, these same landscapes are also potential sites for large-scale wind energy generation. In this study, we used the observed data of the white stork movement trajectories to specify a data-driven agent-based model, which simulates flight behavior of the white storks in a dynamic environment. The data on the wind characteristics and thermal uplift are dynamically changed on a daily basis so as to mimic the scenarios that the observed birds experienced during flight. The flight corridors that emerge from the simulated flights are then combined with the predicted surface on the wind energy potential, in order to highlight the potential risk of collision between the migratory white storks and hypothetical wind farms in the locations that are suitable for wind energy developments. This work provides methods that can be adopted to assess the overlap between wind energy potential and migratory corridors of the migration of birds. This can contribute to achieving sustainable trade-offs between wind energy development and conservation of wildlife and, hence, handling the issues of human–wildlife conflicts. [ABSTRACT FROM AUTHOR]