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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]

The study addresses the challenge of bird collisions with wind turbines by developing an autonomous risk assessment method. The research uses data from the stereoscopic Bird Protection System (BPS) to anticipate potential collision threats by analysing flight parameters and distance from turbines. The danger factor depends on the flight characteristics of the identified bird species and the parameters of the wind turbine control system. The paper proposes an online quantitative risk assessment model that operates in real time, with the aim of minimising unnecessary turbine shutdowns while improving bird conservation. The model is validated through field data from bird flights. The findings suggest that adaptive management of turbine operations based on real-time bird flight data can significantly reduce collision risks without compromising energy production efficiency. The research underscores the balance between ecological considerations and the economic viability of wind energy, proposing an adaptive strategy that reduces unnecessary turbine stoppages while ensuring the safety of avian species.

To accurately describe the operational risks of UAV (unmanned aerial vehicles) in air and determine safety separations, a collision risk model was established that considers both collision probability and collision rate. Based on the characteristics of multi rotor UAVs operating in unstructured airspace, firstly, for unmanned aerial vehicles that cannot obtain dynamic and have no collision avoidance measures in the airspace, a collision probability model was established that combines the uncertainty of heading and position error, and different collision probabilities were used as safety target levels to determine safety separations. Then, for other UAVs in the airspace that can obtain dynamics and take collision avoidance measures, based on their maneuverability, position error, and system error, determine the safety separation and calculate the collision probability. Then, the safety separation was used as the conflict distance to calculate the conflict frequency in the airspace, and finally, the collision risk in the airspace was obtained by combining the collision probability. Taking the logistics UAV as an example, the collision risk and safety separation were calculated using the above method, and two different modes were compared and analyzed. The results show that when it is impossible to obtain dynamic information of other drones in the airspace and there are no collision avoidance measures, the safety separation corresponding to a 5% collision probability is the best option. When dynamic information can be obtained and collision avoidance measures can be taken, the collision risk decreases as the collision probability under conflict conditions decreases. Both models are feasible and effective, and can provide reference for the safety separation of unmanned aerial vehicles operating under these two conditions. [ABSTRACT FROM AUTHOR]

The increase of wind turbine installations to limit climate change may affect bird populations because of collisions with rotor blades. Birds may respond to wind turbine presence along a gradient of behavioral changes: avoiding the wind farm (macro-scale) or the wind turbines either by anticipating wind turbine locations (meso-scale) or engaging in last-minute flee attempts (micro-scale). We investigated flight responses of 25 adult Gyps fulvus (Griffon Vulture) equipped with GPS tags over 3 years in an area that included 10 wind farms in Causses, France. At the macro-scale, foraging range and habitat use revealed that G. fulvus did not avoid wind farms. To investigate avoidance at meso- and micro-scales, we focused on the 4 most visited wind farms. We compared vulture flights to null movement models, which allowed us to keep the correlation between flights and topography while creating movement independent of wind turbine locations. At most sites, G. fulvus did not show avoidance behavior. Overall, our results suggest wind farm-specific responses by soaring birds as a function of landscape topography. Thus, to reduce collision risks, stakeholders should first avoid constructing wind farms close to vulture nesting colonies. When siting, developers should investigate G. fulvus use of local topography to avoid areas in which birds are likely to travel. Finally, managers must prioritize the detection of species not able to avoid turbines when designing preventive measures, such as switching off on-demand technologies. [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]

Placing wind turbines within large migration flyways, such as the North Sea basin, can contribute to the decline of vulnerable migratory bird populations by increasing mortality through collisions. Curtailment of wind turbines limited to short periods with intense migration can minimize these negative impacts, and near‐term bird migration forecasts can inform such decisions. Although near‐term forecasts are usually created with long‐term datasets, the pace of environmental alteration due to wind energy calls for the urgent development of conservation measures that rely on existing data, even when it does not have long temporal coverage.Here, we use 5 years of tracking bird radar data collected off the western Dutch coast, weather and phenological variables to develop seasonal near‐term forecasts of low‐altitude nocturnal bird migration over the southern North Sea.Overall, the models explained 71% of the variance and correctly predicted migration intensity above or below a threshold for intense hourly migration in more than 80% of hours in both seasons. However, the percentage of correctly predicted intense migration hours (top 5% of hours with the most intense migration) was low, likely due to the short‐term dataset and their rare occurrence. We, therefore, advise careful consideration of a curtailment threshold to achieve optimal results.Synthesis and applications: Near‐term forecasts of migration fluxes evaluated against measurements can be used to define curtailment thresholds for offshore wind energy. We show that to minimize collision risk for 50% of migrants, if predicted correctly, curtailments should be applied during 18 h in spring and 26 in autumn in the focal year of model assessments, resulting in an estimated annual wind energy loss of 0.12%. Drawing from the Dutch curtailment framework, which pioneered the 'international first' offshore curtailment, we argue that using forecasts developed from limited temporal datasets alongside expert insight and data‐driven policies can expedite conservation efforts in a rapidly changing world. This approach is particularly valuable in light of increasing interannual variability in weather conditions. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of Nanjing University of Aeronautics & Astronautics is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The lane-changing maneuvers are challenging and contributes to traffic accidents and crashes. They are complicated task that often lead to an increased risk of vehicle collisions and deteriorates traffic flow. This paper proposes a vehicle collision risk model based on vehicle lane-changing movement for autonomous vehicles using the probability threat assessment approach and the lane-changing risk index (LCRI). Given two vehicles, A and B, we first estimate the movements of vehicle B using a GPS device, which accurately identifies the vehicle while changing lanes. We then evaluate the collision risks between vehicles using the probabilistic assessment approach to determine whether a collision event occurs at a later stage. Then, we propose the LCRI, which aims to identify the risk level associated with vehicle lane-changing movement and determine the severity of the crash level. In this study, the HighD vehicle trajectory dataset is used to obtain accurate traffic information and help us investigating collision risks among vehicles. The results show that the proposed model can identify the risks of collision ahead of time. Furthermore, the random parameter ordered logit (RPOL) with heterogeneity model achieves better performance and provides a good fit model and measures for improving traffic safety.

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.

The rapid advancement of unmanned aerial vehicles (UAVs) has introduced new challenges in overseeing and managing their flight operations due to their diverse flight dynamics and performance metrics. To address these complexities, this study introduces a concept of trajectory conformity aimed at enhancing the supervision and control of UAV flights. Trajectory conformity, from a regulatory perspective, is defined as the distribution of deviations between a UAV’s actual flight path and its intended trajectory, offering a measure of system-wide operational error. The concept of conformity is hoped to simplify and strengthen the monitoring process to ensure conflict-free drone flying. The present work is also concerned with the development of a comprehensive UAV collision risk model grounded in trajectory conformity analysis. The normality and homogeneity of UAV trajectory deviations are validated by evaluating the trajectory data obtained from real-world UAV flights. Well clear thresholds between two UAVs operating in three orthogonal directions within the same airspace have been established by the developed model. The results obtained demonstrate the effectiveness in omni-encounter scenarios, underscoring the potential to strengthen safety measures. The present work is expected to enhance UAV safety systems, such as detect and avoid (DAA) and unmanned aircraft system traffic management (UTM), by enabling real-time collision warnings within predefined safety thresholds, supporting proactive risk mitigation. Furthermore, the model’s versatility allows it to be applied to various UAV operational aspects in future works, including route planning, flight procedure design, airspace capacity assessments, and establishment of separation minima.

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.

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]

The lane-changing maneuvers are challenging and contributes to traffic accidents and crashes. They are complicated task that often lead to an increased risk of vehicle collisions and deteriorates traffic flow. This paper proposes a vehicle collision risk model based on vehicle lane-changing movement for autonomous vehicles using the probability threat assessment approach and the lane-changing risk index (LCRI). Given two vehicles, A and B, we first estimate the movements of vehicle B using a GPS device, which accurately identifies the vehicle while changing lanes. We then evaluate the collision risks between vehicles using the probabilistic assessment approach to determine whether a collision event occurs at a later stage. Then, we propose the LCRI, which aims to identify the risk level associated with vehicle lane-changing movement and determine the severity of the crash level. In this study, the HighD vehicle trajectory dataset is used to obtain accurate traffic information and help us investigating collision risks among vehicles. The results show that the proposed model can identify the risks of collision ahead of time. Furthermore, the random parameter ordered logit (RPOL) with heterogeneity model achieves better performance and provides a good fit model and measures for improving traffic safety. [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]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Athletes commonly make decisions about the passability of closing gaps when navigating sport environments. This study examined whether increased temporal pressure to arrive at a desired location modifies these decisions. Thirty participants navigated toward a waypoint in a virtual, sport-inspired environment. To do so, they had to decide whether they could pass through closing gaps of virtual humans (and take the shortest route) or steer around them (and take a longer route). The decision boundary of participants who were time pressured to arrive at a waypoint was biased toward end gaps of smaller sizes and was less reliably defined, resulting in a higher number of collisions. Effects of temporal pressure were minimized with experience in the experimental task. Results indicate that temporal pressure affects perceptual-motor processes supporting information pickup and shapes the information-action coupling that drives compliance with navigation demands. Theoretical and practical implications are discussed. [ABSTRACT FROM AUTHOR]

Safety concerns are progressively emerging regarding the adoption of Unmanned Aircraft Systems (UASs) in diverse civil applications, particularly within the booming air transportation system, such as in Advanced Air Mobility. The outcomes of risk assessment determine operation authorization and mitigation strategies. However, civil UAS operations bring novel safety issues distinct from traditional aviation, like ground impact risk, etc. Existing studies vary in their risk definitions, modelling mechanisms, and objectives. There remains an incomplete gap of challenges, opportunities, and future efforts needed to collaboratively address diverse safety risks. This paper undertakes a comprehensive review of the literature in the domain, providing a summative understanding of the risk assessment of civil UAS operations. Specifically, four basic modelling approaches utilized commonly are identified comprising the safety risk management process, causal model, collision risk model, and ground risk model. Then, this paper reviews the state of the art in each category and explores the practical applications they contribute to, the support offered to participants from multiple stakeholders, and the primary technical challenges encountered. Moreover, potential directions for future work are outlined based on the high-level common problems. We believe that this review from a holistic perspective contributes towards better implementation of risk assessment in civil UAS operations, thus facilitating safe integration into the airspace system.

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.

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.

If ship navigators could access precise collision risk data and guidance for avoidance, safer navigation would be feasible. This study introduces a method for devising avoidance routes based on quantitative collision risk assessment. In particular, we propose an improved isochrone method, a cell-free approach akin to real ship navigation routes. The method uses a novel heuristic function to ensure effective collision avoidance, even in scenarios where conventional heuristic algorithms fail. Through verification cases, we demonstrate that the proposed method successfully avoided collisions, even in situations with unexpected lined-up ships, reducing maximum collision risk by up to 94.0%. Applications of the proposed method with actual AIS (Automatic Identification System) data yield simulation results with successful collision avoidance route planning. Comparing the proposed method to avoidance routes using other existing methods and to the actual ship's route, the proposed method was able to plan a route with a 37.5% improvement in maximum collision risk and a 13.6% improvement in average collision risk.

In this paper, we propose a collision avoidance method based on deep reinforcement learning (DRL) that simultaneously controls the path and speed of a ship. The DRL is actively applied in machine control and artificial intelligence. To verify the proposed method, we applied it to the Imazu problem. It provides benchmark scenarios for collision avoidance. In particular, we compared and analyzed the collision avoidance performance according to the level of learning and various parameters to ensure that the proposed method displays optimal avoidance performance. The results indicated that the proposed method can determine a safe avoidance path for a given situation. Finally, to compare the performance of the proposed method, we compared the collision avoidance method based on the path–speed control of the OS proposed in this study with the collision avoidance method that controls only the path of the OS (Chun et al., 2021). We observed that the proposed method failed in 6 out of 20 scenarios of the Imazu problem when only the path of the OS was controlled. However, it succeeded in collision avoidance in all the 20 scenarios when both path and speed were controlled simultaneously.

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]

Marine current energy converters such as tidal and riverine turbines have the potential to provide reliable, clean power. The risk of collision of fishes with marine energy turbines is not yet well understood, in part due to the challenges associated with observing fish at turbine sites. Turbidity and light availability can limit the effectiveness of optical sensors like video cameras, motivating the use of acoustic cameras for this task. However, challenges persist in collecting and interpreting data acquired from acoustic cameras. Given the limited number of turbine deployments to date, it is prudent to draw on the application of acoustic cameras to monitor fish in other scenarios. This article synthesizes their use for other fisheries applications to inform best practices and set realistic expectations for the results of acoustic camera monitoring at turbine sites. We discuss six key tasks performed with acoustic cameras: detecting objects, identifying objects as fish, counting fish, measuring fish, classifying fish taxonomically and analysing fish behavior. Specific challenges to monitoring fish at turbine sites are discussed. This article is intended to serve as a reference for researchers, regulators and marine energy developers on effective use of acoustic cameras to monitor fish at turbine sites. The studies detailed in this article provide evidence that, in some scenarios, acoustic cameras can be used to inform the risk of fish collision with marine energy turbines but doing so requires careful study design and data processing. [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]

Anthropogenic activities and their influences on aquatic systems is an important topic, especially considering the growing interest in using the earth's resources in a sustainable way. One of those anthropogenic activities is the introduction of renewable technologies into the aquatic environment such as instream turbines. Environmental studies around those technologies are often still ongoing due to their novelty. During the spring of 2018, juvenile individuals of two salmonid species, Atlantic salmon and brown trout were released upstream a vertical axis instream turbine in the river Dal (Dalälven) in eastern Sweden. The aim of this study was to investigate the swimming behavior of the salmonids around a small‐scale prototype vertical axis instream turbine. The swimming pattern and the possible response of avoiding the vertical axis instream turbine were documented with a multi beam sonar. A control area, next to the turbine, was used as reference. No consistent results were shown for trout as they were passing the control area with a statistically high variation, and specimens were rarely observed in proximity of the turbine, neither if the turbine was operating nor at stand still. Salmon clearly avoided the operating turbine, but did not avoid the turbine when it was at stand still, and was often observed swimming straight through the turbine area. These findings indicate that operating this type of instream turbine in a river affects the swimming behavior of Atlantic salmon but is unlikely to affect its migration paths. For brown trout, the statistical results are inconclusive, although data indicate a response of avoiding the turbine. The species are in little risk to suffer physical harm as no fish entered the rotating turbine, despite very turbid water conditions. [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.

Tidal turbines are one source of marine renewable energy but development of tidal power is hampered by uncertainties in fish-turbine interaction impacts. Current knowledge gaps exist in efforts to quantify risks, as empirical data and modeling studies have characterized components of fish approach and interaction with turbines, but a comprehensive model that quantifies conditional occurrence probabilities of fish approaching and then interacting with a turbine in sequential steps is lacking. We combined empirical acoustic density measurements of Pacific herring (Clupea pallasii) and when data limited, published probabilities in an impact probability model that includes approach, entrainment, interactions, and avoidance of fish with axial or cross-flow tidal turbines. Interaction impacts include fish collisions with stationary turbine components, blade strikes by rotating blades, and/or a collision followed by a blade strike. Impact probabilities for collision followed by a blade strike were lowest with estimates ranging from 0.0000242 to 0.0678, and highest for blade strike ranging from 0.000261 to 0.40. Maximum probabilities occurred for a cross-flow turbine at night with no active or passive avoidance. Estimates were lowest when probabilities were conditional on sequential events, and when active and passive avoidance was included for an axial-flow turbine during the day. As expected, conditional probabilities were typically lower than analogous independent events and literature values. Estimating impact probabilities for Pacific herring in Admiralty Inlet, Washington, United States for two device types illustrates utilization of existing data and simultaneously identifies data gaps needed to fully calculate empirical-based probabilities for any site-species combination.

Background: In the context of rapid development of wind energy infrastructure, information on the flight height of birds is vital to assess their collision risk with wind turbines. GPS tags potentially represent a powerful tool to collect flight height data, yet GPS positions are associated with substantial vertical error. Here, we assessed to what extent high-frequency GPS tracking with fix intervals of 2–3 s (GPS remaining turned on between fixes), or barometric altimetry using air pressure loggers integrated in GPS tags, improved the accuracy of height data compared to standard low-frequency GPS tracking (fix interval ≥ 5 min; GPS turned off between fixes). Results: Using data from 10 GPS tag models from three manufacturers in a field setting (194 tags deployed on free-living raptors), we estimated vertical accuracy based on periods when the birds were stationary on the ground (true height above ground was approximately zero), and the difference between GPS and barometric height in flight. In GPS height data, vertical accuracy was mainly driven by noise (little bias), while in barometric data, it was mostly affected by bias (little noise). In high-frequency GPS data, vertical accuracy was improved compared to low-frequency data in each tag model (mean absolute error (AE) reduced by 72% on average; range of mean AE 2–7 vs. 7–30 m). In barometric data, vertical accuracy did not differ between high- and low-frequency modes, with a bias of − 15 to − 5 m and mean AE of 7–15 m in stationary positions. However, the median difference between GPS and barometric data was smaller in flight positions than in stationary positions, suggesting that the bias in barometric height data was smaller in flight. Finally, simulations showed that the remaining vertical error in barometric and high-frequency GPS data had little effect on flight height distributions and the proportion of positions within the collision risk height range, as opposed to the extensive noise found in low-frequency GPS data in some tag models. Conclusions: Barometric altimetry may provide more accurate height data than standard low-frequency GPS tracking, but it involves the risk of a systematic error. Currently, high-frequency GPS tracking provides highest vertical accuracy and may thus substantially advance the study of wind turbine collision risk in birds. [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]

The introduction of more automation into our vehicles is increasing our ability to avoid or mitigate the effects of collisions. Early systems could brake when a likely collision was detected, while more advanced systems will be able to steer to avoid or reconfigure a collision during the same circumstances. This paper addresses how the post-impact motion of an impacted vehicle could be included in the decision-making process of severity minimisation motion planning. A framework is proposed that builds on previous work by the authors, combining models from motion planning, vehicle dynamics, and accident reconstruction. This framework can be configured for different contexts by adjusting its cost function according to relevant risks. Simulations of the unified system are carried out and analysed from the perspective of vehicle model complexity and collision parameters sensitivity. Additionally, effects are highlighted concerning different modelling decisions, with respect to vehicle dynamics models and collision models, that are important to consider in further research. [ABSTRACT FROM AUTHOR]

A 4-year monitoring of bat fauna at Rudine wind farm in Croatia aimed to produce mitigation measures to minimize both the number of bat fatalities and power loss in energy production. During the first 2 years, a high number of carcasses was found from mid-July to the end of October, indicating the need for some mitigation strategy. Based on the results of carcass searches, meteorological data and bat activity monitored at a weather mast and four wind turbine nacelles, mitigation measures were proposed. During the next 2 years, wind turbine curtailment was implemented in the high collision risk period based on critical wind speed thresholds varying from 5.0 to 6.5 ms−1. Estimation of a total number of bat fatalities was conducted with the GenEst software for each monitoring year. A 78% reduction in estimated number of fatalities was recorded indicating the effectiveness of implemented measures. [ABSTRACT FROM AUTHOR]

Alcids generally fly at low altitudes (below 5 m) over the sea but may occasionally fly higher in certain areas, which may put them at risk of collision with turbine blades in offshore wind farms. We used GPS loggers to investigate the location and altitude of flying Rhinoceros Auklets Cerorhinca monocerata. They typically flew at low altitudes averaging < 1 m but flew higher (> 20 m) when returning the last 5-10 km to their colony, which was located at an elevation of 120-130 m above sea level. Thus, the region over which Rhinoceros Auklets fly highest may vary, depending on the location and elevation of their breeding sites. Therefore, it is important to consider spatio-temporal flight altitude patterns when assessing their collision risks with offshore wind turbines. [ABSTRACT FROM AUTHOR]

The rapid development of wind energy in southern Africa represents an additional threat to the already fragile populations of African vultures. The distribution of the vulnerable Cape Vulture Gyps coprotheres overlaps considerably with wind energy development areas in South Africa, creating conflicts that can hinder both vulture conservation and sustainable energy development. To help address this conflict and aid in the safe placement of wind energy facilities, we map the utilization distribution (UD) of this species across its distributional range. Using tracking data from 68 Cape Vultures collected over the last 20 years, we develop a spatially explicit habitat use model to estimate the expected UDs around known colonies. Scaling the UDs by the number of vultures expected to use each of the colonies, we estimate the Cape Vulture population utilization distribution (PUD) and determine its exposure to wind farm impacts. To complement our results, we model the probability of a vulture flying within the rotor sweep area of a wind turbine throughout the species range and use this to identify areas that are particularly prone to collisions. Overall, our estimated PUD correlates well with reporting rates of the species from the Southern African Bird Atlas Project, currently used to assess potential overlap between Cape Vultures and wind energy developments, but it adds important benefits, such as providing a spatial gradient of activity estimates over the entire species range. We illustrate the application of our maps by analyzing the exposure of Cape Vultures in the Renewable Energy Development Zones (REDZs) in South Africa. This application is a scalable procedure that can be applied at different planning phases, from strategic, nationwide planning to project‐level assessments. [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]

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.

Calculating the height at which birds fly over the sea is a challenging task, but remains important to assessing collision risk in proposed offshore wind farm areas for consenting purposes. This could be done by several methods (e.g. GPS or laser rangefinders), but each have biases that make it difficult to generate site-wide assessments. Digital video aerial surveys, which quickly cover large areas, were used to assess flight heights of northern gannets and black-legged kittiwakes using a photogrammetric technique combined with a semi-automated measurement tool. The lengths of birds known to be at sea surface, as identified by reflection on the water, were compared to lengths of birds at unknown height to generate individual flight height profiles. Validation of the method on kittiwake found that LiDAR produced mean flights that were not significantly different to the photogrammetry approach. Validation of the flight height method using man-made objects of known dimensions and heights suggested a 9–18% error (3–6 m at ~ 30 m height). However, the profiles of mean flight height distribution matched patterns in previous work. This method was able to estimate the flying heights of 65% and 75% of flying gannets and kittiwakes, respectively, in this case study. The annual percentage of gannets at collision risk height for a set of turbines with a 30 m air gap was estimated at 29.8%, and 16.1% for kittiwake. This technique can greatly improve our knowledge of the spatial distribution of flight height patterns in marine ecosystems, but also allows stakeholders to assess collision risk more easily within the sphere of offshore wind for the consenting process. [ABSTRACT FROM AUTHOR]

• The probability of traffic conflicts was studied for the lane-shifting sections during the highway reconstruction and expansion activities. To accurately identify the traffic conflict in this complex road section, this study adopted the indicator pair of TTC and TDTC instead of TTC alone. • The correlations between the road and traffic factors and the vehicle collision risk were evaluated via machine learning. The influencing factors were found to be the curve radius, the standard deviation of single-vehicle speed, the cumulative angle in unit length, the vehicle type, the average speed, and the standard deviation of traffic flow speed in decreasing order of their influence on the probability of traffic conflicts. • Based on the conclusions, pertinent measures can be taken to reduce the collision risk of vehicles in the lane-shifting sections. Specific proposals are: diversion of large vehicles; restriction of the vehicle speed in the lane-shifting sections; increase of the curved section radius in line with the specifications; decrease of the cumulative turning angle per unit length i.e. the average turning curvature per unit length. Introduction: There are designated sections for lane-shifting in several highway reconstruction and expansion zones. Similar to the bottleneck sections of highways, these sections are characterized by poor pavement surface conditions, disorderly traffic flow, and high safety risk. This study examined the continuous track data of 1,297 vehicles collected using an area tracking radar. Method: The data from the lane shifting sections were analyzed in contrast with the regular section data. Further, the single-vehicle attributes, traffic flow factors, and the respective road characteristics in the lane-shifting sections were also taken into account. In addition, the Bayesian network model was established to analyze the uncertain interaction between the various other influencing factors. The K-Fold cross validation method was used to evaluate the model. Results: The results showed that the model has a high reliability. The analysis of the model revealed that the significant influencing factors in decreasing order of their influence on the traffic conflict are: the curve radius, cumulative turning angle per unit length, standard deviation of the single-vehicle speed, vehicle type, average speed, and the standard deviation of the traffic flow speed. The probability of traffic conflicts is estimated to be 44.05% when large vehicles pass through the lane- shifting section while it is 30.85% for small vehicles. The probabilities of traffic conflict are 19.95%, 34.88%, and 54.79% when the turning angles per unit length are 0.20 °/m, 0.37 °/m, and 0.63 °/m, respectively. Practical Applications: The results support the view that the highway authorities help reduce traffic risks on lane change sections by diverting large vehicles, implementing speed limits on road sections, and increasing the turning angle per unit length of vehicles. [ABSTRACT FROM AUTHOR]

Collision Avoidance Systems require correct and unambiguous application of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). Ship-to-ship dialogs, aimed at sharing encounter data to comply with COLREGs and reaching maneuvering agreements, would help to reduce the risk of collision. Finite State Machine (FSM) is a mathematical model for describing the sequential behavior of a control program. Sequential function chart (SFC) based on FSM, is a graphical programming language for Programmable Logic Controllers, defined by the international standard IEC 61131-3. In this work, SFC language is used to model and program the set of states and transitions involved in the ship-to-ship dialogs initiated when one of them detects a risky situation. SFC facilitates the development, verification, and maintenance of the control program. The implemented ship-to-ship communications to share data will help in eliminating differences in decision-making and achieving safer encounters. An example of a risky encounter illustrates this assertion, not contemplated in the related studies consulted. The implemented dialogs will enable sharing information on the encounter characteristics and reaching agreements on the maneuvers to be performed, or maintaining a record about disagreements.

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.

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.