Encounter Situations Research Articles

Intelligent ship collision avoidance model integrating human thinking experience

With the maturation of intelligent ship collision avoidance technology, the acceptability of this technology has gained attention and the ship collision avoidance model is confronting interpretability and trustworthiness difficulties. Therefore, this paper integrates human thinking experience into actual collision avoidance decision-making to propose an intelligent collision avoidance model that is more in line with the operator ‘s understanding, thus improving trustworthiness and autonomy. The general architecture of collision avoidance model is divided into two modules: a basic model and an analytical model, by adopting from human thinking patterns of fast and slow. The basic model considers the COLREGS and navigation experience to solve the encounter situation of two ships. The analytical model addresses the collision avoidance issues in complex scenarios with multi-ships, by introducing the repulsive force and the driving force parts of the social force model, which are used to implement course mapping adjustment and gentle course fluctuation respectively. Additionally, a field theory-based effect evaluation model is designed to blend human experience with algorithmic benefits for making the optimal decision in complex scenarios. The results show that when the basic model and the analytical model work together, this architecture performs effectively in a variety of scenarios. It is worth mentioning that the collision avoidance model can potentially be developed to advance collision-free and human-like process trajectory navigation in multi-ship situations.

Ship collision avoidance control based on improved velocity obstacle method

The velocity obstacle method has extensive research in collision avoidance, which uses circles to represent robots or ships to find a collision-free velocity. To better describe the motion characteristics of ships, an improved velocity obstacle method based on elliptical ship domain is proposed in this paper. In the previous research on collision avoidance, the ship domains of the own ship and the target ship are seldom considered at the same time. This paper considers the ship domains of the own ship and the target ship at the same time and judges whether the ship domains of the two ships overlap based on the generalized characteristic polynomial. The heading angle is discretized to construct the velocity obstacle (VO) by calculating the collision velocity at different heading angles. Velocities on the boundaries of the velocity obstacle are used to find an optimal heading angle. The algorithm is tested in different encounter situations and successfully realizes collision avoidance.

Trajectory planning for unmanned surface vehicles in multi-ship encounter situations

Unmanned surface vehicles (USVs) can encounter traffic ships while navigating toward the target location. For the USVs, collision avoidance (CA) trajectories need to be planned according to the international regulations for preventing collisions at sea (COLREGS). A novel trajectory planning approach is proposed for the collision-free trajectories planning of USVs in the case of multiship encounters. Unlike the existing trajectory planning approaches, the proposed approach uses the holistic thinking to simplify the analysis of encounter situations. Ships approaching from all sides of the USV are treated as one or two equivalent obstacles based on consistent offset velocity direction (COVD) method. Furthermore, planned velocity is designed using the proposed CA strategy and kinematic constraints. This strategy is compliant with COLREGS and includes an emergency CA module to further ensure a safe distance between the USV and traffic ships. The performance of the proposed trajectory planning approach is verified through physical simulations using an existing simulator. The simulation results show that the proposed trajectory planning approach can implement multiple USVs to simultaneously avoid collisions and reach their respective target positions. Moreover, the approach remains effective when other USVs do not follow the COLREGS protocols.

Identifying influential ships in multi-ship encounter situation complex network based on improved WVoteRank approach

The ship encounter situation perception is of great significance to improving the safety and efficiency of the Vessel Traffic Service (VTS). The traffic complexity and complex network theories are the critical techniques for evaluating the influential ships in the multi-ship encounter. This paper proposes an influential ships identification method based on improved WVoteRank approach. It considers ships' spatial compactness and risk connectivity and divides the ships in the area into multi-ship encounter clusters. At the same time, the complex network of ship encounter situation is constructed, and the improved WVoteRank approach is introduced to identify influential ships. An evaluation is carried out through the AIS data of Ningbo-Zhoushan port in China. The experimental results reveal that the approach: 1) can effectively identify the ship encounter clusters and influential ships, which simultaneously helps VTS further grasp the spatio-temporal characteristics of complex traffic areas distribution. 2) the improved WVoteRank approach is practical and advanced for multi-ship encounter situation complex network. Therefore, it provides new insights into enhancing marine traffic monitoring capabilities, helping VTS to improve the efficiency and intelligence of marine traffic services.

Modeling Human Encounter Situation Awareness Results Using Support Vector Machine Models

This study constructs a support vector machine model based on supervised learning to model the results of situation awareness for ship collision avoidance. To explain the model, collision risk situations were defined, and human situation recognition results were collected in the specified cases. Moreover, it was used to build predictors and outcome variables. Finally, the constructed variable was applied to the classification model. This model provides insight into the results of the navigator’s encounter situation awareness when collision avoidance is required. The results indicate that the proposed model can be used to predict human situation awareness outcomes in given cases.

Mid-Infrared Sheep Segmentation in Highland Pastures Using Multi-Level Region Fusion OTSU Algorithm

In highland pastures, grazing is a common method for managing sheep due to the abundance of grassland resources. However, it is easy for sheep to encounter situations such as stray, deviation and attacks from natural enemies; therefore, the remote monitoring of sheep in the highland pastures is an urgent problem to be solved. This paper proposes a mid-infrared sheep segmentation method based on the multi-level region fusion maximum between-class variance algorithm, i.e., OTSU algorithm, for sheep surveillance. First, a mean adjustment OTSU algorithm is designed to better distinguish the interference areas in the background. Second, the Butterworth high-pass filter is combined with the mean adjustment OTSU segmentation algorithm to remove the high-brightness interference areas in the background with slow gray intensity changes. Finally, after filtering out the large area background and small stray point, the two processed results above are fused with the AND logical operation to obtain a final segmentation result. Our algorithm is evaluated using three objective evaluation indicators: the root mean square error (RMSE), structural similarity index metric (SSIM), and peak signal to noise ratio (PSNR). The RMSE, SSIM, PSNR of highland wetland image are 0.43187, 0.99526, and 29.16353. The RMSE, SSIM, PSNR of sandy land image are 0.87472, 0.98388, and 23.87430. The RMSE, SSIM, PSNR of grassland image are 0.65307, 0.99437, and 30.33159. The results show that our algorithm can meet the requirements for the mid-infrared sheep segmentation in highland pastures.

A Novel Ship Domain-oriented Parameter of Ship Collision Risk Considering the Ship Maneuverability and Encounter Situation

The identification of ship collision risks is an important element in maritime safety and management. The concept of the ship domain has also been studied and developed since it was proposed. Considering the existing trend that the ship domain is increasingly widely used in collision risk-related research, a new domain-oriented collision risk factor, i.e., the current state of domain (CSD), is introduced in this paper, which can effectively reflect the current state and show a certain predictability of collision risk from the perspective of the ship domain. To further prove the rationality of the CSD, a series of different simulations consisting of three typical encounter scenarios were conducted, verifying the superiority of the proposed parameter.

Ship Intrusion Collision Risk Model Based on a Dynamic Elliptical Domain

To improve navigation safety in maritime environments, a key step is to reduce the influence of human factors on the risk assessment of ship collisions by automating the decision-making process as much as possible. This paper optimizes a dynamic elliptical ship domain based on Automatic Identification System (AIS) data, combines the relative motion between ships in different encounter situations and the level of ship intrusion in the domain, and proposes a ship intrusion collision risk (SICR) model. The simulation results show that the optimized ship domain meets the visualization requirements, and the intrusion model has good collision risk perception ability, which can be used as the evaluation standard of ship collision risk: when the SICR is 0.5–0.6, the ship can establish a collaborative collision avoidance decision-making relationship with other ships, and the action ship can take effective collision avoidance action at the best time when the SICR is between 0.3 and 0.5. The SICR model can give navigators a more accurate and rapid perception of navigation risks, enabling timely maneuvering decisions, and improving navigation safety.

CNN-Based Vessel Meeting Knowledge Discovery From AIS Vessel Trajectories

How to extract a collection of trajectories for different vessels from the raw AIS data to discover vessel meeting knowledge is a heavily studied focus. Here, the AIS database is created based on the raw AIS data after parsing, noise reduction and dynamic Ramer-Douglas-Peucker compression. Potential encountering trajectory pairs will be recorded based on the candidate meeting vessel searching algorithm. To ensure consistent features extracted from the trajectories in the same time period, time alignment is also adopted. With statistical analysis of vessel trajectories, sailing segment labels will be added to the input feature. All motion features and sailing segment labels are combined as input to one trajectory similarity matching method based on convolutional neural network to recognize crossing, overtaking or head-on situations for each potential encountering vessel pair, which may lead to collision if false actions are adopted. Experiments on AIS data show that our method is effective in classifying vessel encounter situations to provide decision support for collision avoidance.

A real-time multi-ship collision avoidance decision-making system for autonomous ships considering ship motion uncertainty

Ship autonomous collision avoidance has attracted increasing attention in recent years. However, more attention is paid to the scenario in which the target ship keeps its course and speed. Less attention is paid to the development of a collision avoidance decision-making system under the uncertainty of the target ship's movement in a complex multi-ship encounter situation. Based on the idea of model predictive control (MPC), this paper proposes an autonomous ship collision avoidance decision-making system (CADMS) suitable for the uncertainty of ship motion. The CADMS includes four modules: collision risk analysis module, control and execution module, ship trajectory prediction module and collision avoidance decision-making model. The proposed model can be implemented in the collision avoidance decision-making system for safe navigation or it can be included in the ship autonomous navigation process. The decision-making model is achieved from a risk identification-motion prediction-ship control-scheme implementation perspective, and the dynamic and uncertainty features of the ship action (i.e., alter course or change speed) are considered in the modelling process. The real-time rolling update of ship collision avoidance decisions is realised based on the time series rolling method. Four scenarios are designed to demonstrate the collision avoidance decision-making system's performance. The results show that the proposed collision avoidance decision-making system is a reasonable and effective system for collision avoidance, particularly in multi-ship encounter situations of target ships suddenly altering course or change speed.

Ship Autonomous Collision-Avoidance Strategies—A Comprehensive Review

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.

An Intelligent Algorithm for USVs Collision Avoidance Based on Deep Reinforcement Learning Approach with Navigation Characteristics

Many achievements toward unmanned surface vehicles have been made using artificial intelligence theory to assist the decisions of the navigator. In particular, there has been rapid development in autonomous collision avoidance techniques that employ the intelligent algorithm of deep reinforcement learning. A novel USV collision avoidance algorithm based on deep reinforcement learning theory for real-time maneuvering is proposed. Many improvements toward the autonomous learning framework are carried out to improve the performance of USV collision avoidance, including prioritized experience replay, noisy network, double learning, and dueling architecture, which can significantly enhance the training effect. Additionally, considering the characteristics of the USV collision avoidance problem, two effective methods to enhance training efficiency are proposed. For better training, considering the international regulations for preventing collisions at sea and USV maneuverability, a complete and reliable USV collision avoidance training system is established, demonstrating an efficient learning process in complex encounter situations. A reward signal system in line with the USV characteristics is designed. Based on the Unity maritime virtual simulation platform, an abundant simulation environment for training and testing is designed. Through detailed analysis, verification, and comparison, the improved algorithm outperforms the pre-improved algorithm in terms of stability, average reward, rules learning, and collision avoidance effect, reducing 26.60% more accumulated course deviation and saving 1.13% more time.

Anticancer drug therapy for patients with renal dysfunction.

Anticancer drug therapy for cancer is developing rapidly, including molecular-targeted drugs and immune checkpoint inhibitors that are used in clinical settings in addition to conventional cytotoxic drugs. In daily clinical practice, clinicians sometimes encounter situations in which the effects of these chemotherapeutic agents are considered unacceptable in high-risk patients with liver or renal dysfunction, those undergoing dialysis and older adults. There is no clear evidence regarding anticancer drugs administration to patients with renal dysfunction. However, there are indications for dose setting based on the theory of the renal function responsible for drug excretion and past administration experience. This review outlines anticancer drugs' administration in patients with renal dysfunction.

Spatial-temporal analysis method of ship traffic accidents involving data field: An evidence from risk evolution of ship collision

Collision accident is the commonest type of ship traffic accident. Analysis of the spatial-temporal characteristics during the ship encounter process is helpful to the discovery of the accident evolution mechanism. The spatial-temporal analysis method was developed for collision accidents from maritime accident investigation reports to reveal the evolution characteristics of potential collision risk (PCR) during the two ships' encounters. First, a four-dimensional interpolation algorithm under the Radar window was proposed by analyzing the uncertainty of risk collision from different ship parameters. Second, a data field cognitive model with Gaussian mixture clustering on PCR was constructed according to the Radar information of the ship encounter process. Last but not least, combining different collision avoidance scenarios under the ship domain, the spatial-temporal characteristics of PCR were discussed to reveal the evolution mechanism. Empirical evidence shows that the laws of time are equivalent to space in the four stages of the ship encounter process. Ship's parameters make a difference in risk perception under the Radar window. Different encounter situations have significant impacts on Radar-based collision avoidance.

Context-dependent alarm responses in wild vervet monkeys

The alarm calls of nonhuman primates are occasionally cited as functionally equivalent to lexical word meaning in human language. Recently, however, it has become increasingly unlikely that one-to-one relations between alarm call structures and predator categories are the default, mainly because many call types are produced in multiple contexts, requiring more complex notions of meaning. For example, male vervet monkeys produce the same alarm calls during encounters with terrestrial predators and neighbouring groups, suggesting that recipients require additional information to attribute meaning to the calls. We empirically tested the hypothesis that vervet monkeys take contextual information into account when responding to each other's alarm calls. In playback experiments, we exposed subjects to recordings of male alarm barks during actual intergroup encounters (predator unlikely) or when there was no intergroup encounter (predator likely). Subjects responded more strongly in the no intergroup encounter situations, typically associated with discovering a hiding predator, measured in terms of startle responses, vigilance behaviour and gazing towards the presumed caller. We discuss the significance of using contextual information for meaning attribution in nonhuman primate communication.

A collaborative collision avoidance strategy for autonomous ships under mixed scenarios

AbstractShip collision avoidance has always been one of the classic topics in the field of marine research. In traditional encounter situations, officers on watch (OOWs) usually use a very high frequency (VHF) radio to coordinate each other. In recent years, with the continuous development of autonomous ships, there will be a mixed situation where ships of different levels of autonomy coexist at the same time. Under such a scenario, different decision makers have different perceptions of the current scene and different decision-making logic, so conventional collision avoidance methods may not be applicable. Therefore, this paper proposes a collaborative collision avoidance strategy for multi-ship collision avoidance under mixed scenarios. It builds a multi-ship cooperative network to determine cooperative objects and timing, at the same time. Based on a cooperative game model, a global collision avoidance responsibility distribution that satisfies group rationality and individual rationality is realised, and finally achieves a collaborative strategy according to the generalised reciprocal velocity obstacle (GRVO) algorithm. Case studies show that the strategy proposed in this paper can make all ships pass each other clearly and safely.

Complex encounter situation modeling and prediction method for unmanned ships based on bounded rational game

Comprehensive, accurate, and timely cognition of navigation situation is a prerequisite to make safe, efficient, and scientific decisions for unmanned ships. Previous research on this topic has been more focused on situation perception, and lacks studies of mathematical representation, modeling, and prediction for multi-ship complex encounter situation from an integrated perspective. Based on this, vectorized model is constructed to express the situation for the target ship in open waters. Through analysis of bounded rational dynamic game between ships, the navigation situation of the target ship can be predicted. Historical data of Automatic Identification System (AIS) both from public data set and experiments is used to verify the prediction method. The results show that the research can provide theoretical basis for studies on multi-ship encounter situation, and supply a reference for navigation decisions of unmanned ships.

How business-to-business salespeople deal with buying center dissenters

Salespeople routinely sell to multiple members with influence over the purchasing decision in the customer organization. Given these buying center members’ varying needs, wants, perspectives, and motivations, dispositions toward a sales discussion can be heterogeneous. Salespeople may encounter situations in which members of the buying center react positively to the sales discussion, with the exception of a dissenter who exerts a negative impact on the salesperson in the interaction. Through a qualitative research design, this article provides insight into salespeople’s approaches in dealing with a dissenter in the buying center. The findings provide an integrated understanding of salespeople’s experiences of dissent encounters in business-to-business selling. In-meeting mitigation and post-meeting remediation strategies are advanced along with influencing situational factors. In illustrating how salespeople handle dissenters, the article contributes to scholarship, provides managerial guidance, and advances avenues for future research.

On securing continuity of eddy covariance flux time-series after changing the measurement height: Correction for flux differences due to the footprint difference

During long-term flux monitoring, we occasionally encounter situations where the existing measurement setup must be changed. Considering that one of the main purposes of long-term flux monitoring is identifying long-term variability and trends, which are relatively small, it is important to understand or correct for flux differences caused by the measurement setting changes. In this study, before changing the flux measurement height of the CRK (Cheorwon Rice paddy, Korea) site from 10 m to 5 m, we installed another eddy covariance system at 5 m and compared the sensible and H2O/CO2/CH4 fluxes at both heights from April 2020 to April 2021. Although random errors could explain substantial portions of the flux differences between the two heights, it was confirmed that systematic errors also existed because the means of the flux differences were not zero, and the distributions of the flux differences were also significantly skewed. Further analysis showed that the primary cause of these systematic errors was the footprint differences rather than the difference in turbulent transport between the two heights. Based on the results, we proposed a strategy for securing the continuity of the flux time-series, which is necessary to understand the long-term variability using all data before and after a measurement change, and discussed the necessity of such a comparative observation.

“Studying cognitive reappraisal as an antidote to the effect of negative emotions on medical residents’ learning: a randomized experiment”

BackgroundMedical trainees often encounter situations that trigger emotional reactions which may hinder learning. Evidence of this effect on medical trainees is scarce and whether it could be counteracted is unclear. This study investigated the effect of negative emotions on medical residents’ learning and whether cognitive reappraisal counteracts it.MethodsNinety-nine medical residents participated in a three-phase experiment consisting of: (1) watching a video, either a neutral or an emotion-induction version, the latter either followed by cognitive reappraisal or not (2) learning: all participants studied the same medical text; study-time and cognitive engagement were measured; (3) test: a recall-test measured learning. Data was analysed using Chi-square test and one-way ANOVA.ResultsStudy time significantly varied between conditions (p = 0.002). The two emotional conditions spent similar time, both significantly less than the neutral condition. The difference in test scores failed to reach significance level (p = 0.053). While the emotional conditions performed similarly, their scores tended to be lower than those of the neutral condition.ConclusionNegative emotions can adversely affect medical residents’ learning. The effect of emotions was not counteracted by cognitive reappraisal, which has been successfully employed to regulate emotions in other domains. Further research to examine emotion regulation strategies appropriate for medical education is much needed.