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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.

Collision-risk measurements are crucial for ships, as they are necessary for collision avoidance decision making. However, collision risks between ships have not been quantified in unified standards. In this study, a critical collision index is proposed to describe the critical degree of collision risks between ships. Based on the field theory, a collision-risk field was introduced to build a field strength model based on the collision index. The model synthetically considers the influences of distance at closest point of approach, time to closest point of approach, and the relative bearing of coming ships. Moreover, the real time to the closest point of approach was used for describing the collision risk between ships. In addition, encounter situations and collision risks in the field were simulated using the field strength model and isorisk lines. The results are in agreement with the real collision-risk perceptions of Officers on Watch. It was shown that the proposed ship critical collision index can play an important role in ship collision avoidance and early warning systems.

The identification of risks associated with collision for vessels is an important element in maritime safety and management. A vessel collision avoidance system is a topic that has been deeply studied, and it is a specialization in navigation technology. The automatic identification system (AIS) has been used to support navigation, route estimation, collision prediction, and abnormal traffic detection. This article examined the main elements of ship collision, developed a mathematical model for the risk assessment, and simulated a collision assessment based on AIS information, thereby providing meaningful recommendations for crew training and a warning system, in conjunction with the AIS on board.

Crosswalks present a major threat to pedestrians, but we lack dense behavioral data to investigate the risks they face. One of the breakthroughs is to analyze potential risky behaviors of the road users (e.g., near-miss collision), which can provide clues to take actions such as deployment of additional safety infrastructures. In order to capture these subtle potential risky situations and behaviors, the use of vision sensors makes it easier to study and analyze potential traffic risks. In this study, we introduce a new approach to obtain the potential risky behaviors of vehicles and pedestrians from CCTV cameras deployed on the roads. This study has three novel contributions: (1) recasting CCTV cameras for surveillance to contribute to the study of the crossing environment; (2) creating one sequential process from partitioning video to extracting their behavioral features; and (3) analyzing the extracted behavioral features and clarifying the interactive moving patterns by the crossing environment. These kinds of data are the foundation for understanding road users’ risky behaviors, and further support decision makers for their efficient decisions in improving and making a safer road environment. We validate the feasibility of this model by applying it to video footage collected from crosswalks in various conditions in Osan City, Republic of Korea.

Ship collisions caused by navigation officer error significantly threaten the safety of marine navigation. An increased fear of collision for navigation officers in a close-quarters situation (CQS) may lead to failure to perform the prescribed collision-avoidance measures. This study measured the perceived collision risk (PCR) for 30 coast guard navigation officers according to their heart-rate variability in CQSs, then identified the key factors influencing measured PCR values. The PCR was measured in four types of simulated CQS, with two ships approaching each other from 2.5 nautical miles to the point of collision. The maximum PCR bearing was identified at a relative bearing of 135° based on the “own ship.” Multiple regression analysis showed that the navigator's personality factors, i.e., their onboard career and license rating, negatively (−) influenced the 95% confidence level. However, navigator age did not have significant effects on PCR. This investigation of PCR factors can help novice navigators avoid collisions due to fear or panic in a CQS with a target ship.

Korea has been operating multi-purpose low-earth orbit (LEO) satellites such as the Korea multi-purpose satellite (KOMPSAT) since 1999 and the Communication, Ocean, and Meteorological Satellite (COMS), which was launched into geostationary orbit in 2006. The Korea Aerospace Research Institute (KARI) consequently became concerned about the deteriorating space debris environment. This led to the instigation, in 2011, of a project to develop the KARI space debris collision risk management system (KARISMA). In 2014, KARISMA was adopted as an official tool at the KARI ground station and is operated to mitigate collision risks while being continuously upgraded with input from satellite operators. The characteristics and architecture of KARISMA are described with detailed operational views. The user-friendly user interfaces including 2D and 3D displays of the results, conjunction geometries, and so on, are described in detail. The results of our analysis of the space collision risk faced by the KOMPSAT satellites as determined using KARISMA are presented, as well as optimized collision avoidance maneuver planning with maneuvering strategies for several conjunction events. Consequently, the development of KARISMA to provide detailed descriptions is expected to contribute significantly to satellite operators and owners who require tools with many useful functions to mitigate collision risk.

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

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

Urbanization increasingly exposes birds to multiple sources of direct anthropogenic mortality. Collisions with buildings, and windows in particular, are a top bird mortality source, annually causing 365–988 million fatalities in the United States. Correlates of window collision rates have been studied at the scale of entire buildings and in relation to the surrounding landscape, and most studies have only assessed correlates for all birds combined without considering season- and species-specific risk factors. In Stillwater, Oklahoma, USA, we conducted bird collision surveys at 16 buildings to assess building structural-, vegetation-, and land cover-related collision correlates. Unlike past studies, we focused at the scale of individual building façades, and in addition to considering correlates for total collisions, we assessed correlates for different seasons and separately for 8 collision-prone species. Several façade-related features, including proportional glass coverage, façade length, and façade height, were positively associated with total collisions and collisions for most separate seasons and species. Total collisions were also greater at alcove-shaped façades than flat, curved, and portico-shaped façades. We found that collision correlates varied among seasons (e.g., surrounding lawn cover important in summer and fall, but not spring) and among species (e.g., surrounding impervious cover positively and negatively related to collisions of Painted Bunting [Passerina ciris] and American Robin [Turdus migratorius], respectively). Given the importance of glass proportion, collision reduction efforts should continue to focus on minimizing and/or treating glass surfaces on new and existing buildings. Our species- and season-specific assessments indicate that management of some collision risk factors may not be equally effective for all seasons and species. Future research, policy, and management that integrates information about collision risk for all bird species and seasons, and at multiple scales from building façades to the surrounding landscape, will be most effective at reducing total mortality from bird–window collisions.

We investigate maritime accidents near Bornholm Island in the Baltic Sea, focusing on one of the most recent vessel collisions and a way to improve maritime safety as a prevention strategy. By leveraging Long Short-Term Memory autoencoders, a class of deep recurrent neural networks, this research demonstrates a unique approach to forecasting vessel trajectories and assessing collision risks. The proposed method integrates trajectory predictions with statistical techniques to construct probabilistic boundaries, including confidence intervals, prediction intervals, ellipsoidal prediction regions, and conformal prediction regions. The study introduces a collision risk score, which evaluates the likelihood of boundary overlaps as a metric for collision detection. These methods are applied to simulated test scenarios and a real-world case study involving the 2021 collision between the Scot Carrier and Karin Hoej cargo ships. The results demonstrate that CPR, a non-parametric approach, reliably forecasts collision risks with 95% confidence. The findings underscore the importance of integrating statistical uncertainty quantification with deep learning models to improve navigational decision-making and encourage a shift towards more proactive, AI/ML-enhanced maritime risk management protocols.