Avoidance Decisions Research Articles

A novel deterministic search-based algorithm for multi-ship collaborative collision avoidance decision-making

Ship collision avoidance is critical to maritime safety, but maritime traffic patterns are becoming increasingly complex as the number of vessels of all types increases. Therefore, how to improve the collision avoidance effectiveness and efficiency of two ships and multiple ships is crucial. This study proposes a comprehensive collaborative collision avoidance framework that takes into account both safety and economic factors. By analyzing ship navigation characteristics, we formulate a new collision avoidance cost function that covers navigation safety and economic benefits. Furthermore, in accordance with the COLREGS guidelines, this paper adopts a deterministic search algorithm to calculate collision avoidance trajectories involving two or more vessels, and uses a proportional integral differential (PID) algorithm to adjust ship maneuvering based on the derived collision avoidance decisions. In parallel, we conducted extensive simulation studies to evaluate encounter scenarios involving two and multiple vessels. Results show that the proposed collaborative collision avoidance framework ensures regulatory compliance and also improves navigation safety and economic efficiency. This algorithm has important reference value for ship operators to formulate and implement effective collision avoidance strategies.

Although I know it: Social anxiety is associated with a deficit in positive updating even when the cost of avoidance is Obvious

Social anxiety (SA) is associated with difficulties in positively updating negative social information when new information and feedback about chosen options (actual decisions) are received. However, it is unclear whether this difficulty persists when hidden information regarding unchosen options is explicitly presented. The aim of the current study was to address this gap. Participants (Mturk; n = 191) completed a two-phases novel task. In the task, participants chose to approach or avoid people, represented by images of faces. During the initial (learning) phase, participants learned, in a probabilistic context, which people are associated with negative outcomes and should be avoided, and which are associated with positive outcomes and should be approached. During the subsequent updating phase, people previously associated with negative outcomes became associated with positive outcomes and vice versa. Importantly, participants received feedback not only on their approach (actual) decisions, but also on their avoidance (counter-factual) decisions (e.g., approaching this person would have been beneficial). The results revealed that even when the consequences of avoidance were explicitly presented, SA was associated with difficulty in positive updating of social information. The findings support the view that biased updating of social information is a change-resistant mechanism that may underlie the maintenance of SA.

Analysis of pre-crash scenarios and contributing factors for autonomous vehicle crashes at intersections

Intersections are high-risk locations for autonomous vehicles (AVs). Crash causation analysis based on pre-crash scenarios can provide new insight into these crashes that can lead to effective countermeasures, but there are significant differences in pre-crash scenarios between autonomous and conventional vehicles, and inadequate AV data has put limits on research. The association rule method, however, can yield useful results despite these limits. This study therefore aims to use the method with pre-crash scenarios to understand the characteristics and contributing factors of AV crashes at intersections from the latest 5-year AV crash data. Analysis of 197 AV crashes at intersections revealed 30 types of pre-crash scenarios. The rear-end crash (58.88%) and lane change crash (16.24%) were the most frequently occurring scenarios for AVs. The proportion of AVs being rear-ended by conventional vehicles was 58.38%. The main contributing factors of these two most common AV scenarios were identified by association rules and crash causes were analyzed from the perspective of AV decision-making. The main factors contributing to the AV rear-end scenario were location outside the intersection in the intersection-related area, traffic signal control, autonomous engaged mode, mixed-use or public land, and weekdays, while those for lane change scenarios were on-street parking and the time of 8:00 a.m. Important causes of rear-end crashes attributable to the AV were inadequate stop and deceleration decisions by the AV’s automated driving system (ADS) and insufficient collision avoidance decisions in lane change crashes. Identification of the pre-crash characteristics and contributing factors provide new insight into AV crash causation and can be used in the determination of the AV’s operational design domain and the development and optimization of the AV’s ADS at intersections. These findings can also play a role in guiding traffic safety agencies to discover AV hotspots and propose AV management regulations.

A conflict cluster-based method for collision avoidance decision-making in multi-ship encounter situations

During the process of collision avoidance, especially in a multi-ship encounter situation, the dynamic interactions among individual ships impose a significant impact on collision avoidance decision-making. It is imperative, therefore, that collision avoidance decisions are formulated with a comprehensive consideration of not only the current direct collision conflict but also the potential conflicts due to planned collision avoidance actions. To address this requirement, this paper proposes a dynamic conflict cluster detection method for collision avoidance decision-making in multi-ship encounters. The involved ships are clustered into stable temporal-dependent ship conflict groups taking into account both conflict connectivity and the potential spatiotemporal interactions originating from planned collision avoidance actions. The conflict cluster detection model is implemented within a framework to achieve hierarchical coordinated collision avoidance decision-making. By a simulation experiment of an 11-ship encounter, the proposed method successfully discerns the ships with conflicts and provides feasible collision avoidance decisions. Compared to the non-cluster collision avoidance methods, the proposed method generates the results with acceptable deviating distance and number of collision avoidance actions at minimum computation load. It has been demonstrated that the proposed method is both effective and efficient for officers on board and operators at Vessel Traffic Services centers in real-life navigation.

Research on obstacle avoidance optimization and path planning of autonomous vehicles based on attention mechanism combined with multimodal information decision-making thoughts of robots.

With the development of machine perception and multimodal information decision-making techniques, autonomous driving technology has become a crucial area of advancement in the transportation industry. The optimization of vehicle navigation, path planning, and obstacle avoidance tasks is of paramount importance. In this study, we explore the use of attention mechanisms in a end-to-end architecture for optimizing obstacle avoidance and path planning in autonomous driving vehicles. We position our research within the broader context of robotics, emphasizing the fusion of information and decision-making capabilities. The introduction of attention mechanisms enables vehicles to perceive the environment more accurately by focusing on important information and making informed decisions in complex scenarios. By inputting multimodal information, such as images and LiDAR data, into the attention mechanism module, the system can automatically learn and weigh crucial environmental features, thereby placing greater emphasis on key information during obstacle avoidance decisions. Additionally, we leverage the end-to-end architecture and draw from classical theories and algorithms in the field of robotics to enhance the perception and decision-making abilities of autonomous driving vehicles. Furthermore, we address the optimization of path planning using attention mechanisms. We transform the vehicle's navigation task into a sequential decision-making problem and employ LSTM (Long Short-Term Memory) models to handle dynamic navigation in varying environments. By applying attention mechanisms to weigh key points along the navigation path, the vehicle can flexibly select the optimal route and dynamically adjust it based on real-time conditions. Finally, we conducted extensive experimental evaluations and software experiments on the proposed end-to-end architecture on real road datasets. The method effectively avoids obstacles, adheres to traffic rules, and achieves stable, safe, and efficient autonomous driving in diverse road scenarios. This research provides an effective solution for optimizing obstacle avoidance and path planning in the field of autonomous driving. Moreover, it contributes to the advancement and practical applications of multimodal information fusion in navigation, localization, and human-robot interaction.

Intelligent navigation method for multiple marine autonomous surface ships based on improved PPO algorithm

To achieve the autonomous navigation of multiple Marine Autonomous Surface Ship (MASS), an intelligent planning and decision-making method for MASS based on Rapidly-exploring Random Trees star (RRT-star) and improved Proximal Policy Optimization (PPO) algorithm is proposed. The novelty of the study is: (1) enhancing the traditional PPO algorithm by the Generalized Advantage Estimation (GAE) and the Long Short-Term Memory (LSTM) network, which facilitate accelerated convergence of average reward, predict the state space, and enhance accuracy in estimating the advantage function. (2) proposing a complete reward function, which can not only guide MASS to navigate towards the waypoint, but also ensure that MASS complies with the COLREGs during collision avoidance. It is worth highlighting that the trained network model can be generalized to different scenarios. We attached the trained neural network model to different MASS and simulated it in different open and narrow waters. Even in emergency situations, the method can still make it deviate from COLREGs and make flexible collision avoidance decisions. The results show that this method can handle the multi-MASS encounter situation well and make MASS reach the waypoint safely.

A novel intelligent collision avoidance algorithm based on deep reinforcement learning approach for USV

Enhancing the efficiency of unmanned surface vehicles (USVs) collision avoidance can yield a significant impact, as it can result in safer navigation and lower energy consumption. This paper introduces a robust approach employing deep reinforcement learning theory to facilitate informed collision avoidance decisions within intricate maritime environments. The restrictions on USV maneuverability and international regulations for preventing collisions at sea are studied and quantified, particularly focusing on the shape and size changes of the ship’s domain caused by USV speed. Based on the deep Q network, an improved methodology is designed, incorporating a noisy network, prioritized experience replay, dueling neural network architecture, and double Q learning, resulting in a highly efficient sampling, exploration, and learning process. To curtail computational expenses associated with USVs, a novel dynamic area restriction technique is proposed. Furthermore, an innovative USV state clipping method is introduced to mitigate training complexities. By utilizing the Unity platform, a virtual environment characterized by complexity and stochasticity is constructed for training and testing the collision avoidance of USVs This novel approach surpasses the performance of the pre-improvement algorithm across multiple collision avoidance effectiveness indicators and performance metrics.

Power pose effects on approach and avoidance decisions in response to social threat.

Individuals' opportunities for action in threatening social contexts largely depend on their social power. While powerful individuals can afford to confront aggressors and dangers, powerless individuals need others' support and better avoid direct challenges. Here, we investigated if adopting expansive or contracted poses, which signal dominance and submission, impacts individuals' approach and avoidance decisions in response to social threat signals using a within-subject design. Overall, participants more often chose to avoid rather than to approach angry individuals, but showed no clear approach or avoidance preference for fearful individuals. Crucially, contracted poses considerably increased the tendency to avoid angry individuals, whereas expansive poses induced no substantial changes. This suggests that adopting power-related poses may impact action decisions in response to social threat signals. The present results emphasize the social function of power poses, but should be replicated before drawing strong conclusions.

Ecological uncertainty and antipredator behaviour: an integrative perspective

Due to its unforgiving nature, predation pressure exerts strong selection pressure on the behaviour of prey animals. As a result, prey are forced to balance the conflicting demands of successfully detecting and avoiding predators and the need to engage in other fitness-related activities such as foraging, mating and social behaviour. Here, we provide an overview of the role that individual predator avoidance decisions plays in constraining behavioural phenotypes and how past experience with risks shapes current (and future) trade-offs, physiological and life history investments. Critically, access to reliable risk assessment information allows prey to respond to spatially and temporally variable predation risks. Uncertainty of predation risks is expected to limit the ability of prey to make short- and longer-term adjustments responses to predation threats, potentially increasing the indirect costs of predation. We describe a ‘landscape of information’ in which prey rely on publicly available risk assessment information to reduce the uncertainty of predation risks associated with variable threats and the potential impact of natural and anthropogenic environmental factors which may limit information availability. Despite a long tradition of research into the antipredator trade-offs made by prey animals, there remain a number of important unanswered questions.

A partially observable multi-ship collision avoidance decision-making model based on deep reinforcement learning

Unmanned ships have drawn widespread attention for their potential to enhance navigational safety, minimize human errors, and improve shipping efficiency. Nevertheless, the complexity and uncertainty of mixed obstacle environments present significant challenges to developing unmanned ships, particularly in collision avoidance decision-making. This paper proposes a new model using the Partially Observable Markov Decision Process (POMDP) to construct a collision avoidance decision-making model in mixed obstacle environments for autonomous ships, which can address the environment’s complexity and uncertainty and improve decision accuracy. An image-state observation method is proposed as images can provide more accurate, rich, and reliable information. A dense reward function is designed to address the issue of sparse rewards in fitting the algorithm. The Proximal Policy Optimization (PPO) algorithm is utilized for model training. Based on this, a route guidance method called the PPO for POMDP with guidelines under dense reward (G-IPOMDP-PPO) is proposed, which can improve training efficiency. Simulations are conducted in various mixed obstacle environments and compared with conventional algorithms. The results show that the proposed model can safely and efficiently make collision avoidance decisions in complex and uncertain environments. This research provides a new solution and theoretical foundation for developing autonomous ships and can be extended to achieving dynamic interactive collision avoidance in mixed obstacle environments.

Political turnover and business decision: A study of Indonesian public companies

Research aims: This study aims to explore whether and to what extent political turnover (a change of local government head) is associated with a firm’s business decisions, such as tax avoidance and corporate investment.Design/Methodology/Approach: Using a sample of companies listed in the Indonesia Stock Exchange from 2012-2021, the authors measured the association between changes in local government leaders and business decisions using panel data regression with Driscoll Kraay’s Standard Error. The authors hand-collected data on changes in local government leaders in 183 Indonesian cities to measure political turnover and merged the data with financial variables collected from the Eikon database.Research findings: This study uncovered that local political turnover in the region of companies' headquarters was negatively associated with tax avoidance decisions. This study also found that local political turnover negatively affected corporate investment decisions during normal/non-crisis periods. Consistent with the Resource Dependence Theory, the results of this study suggest that firms treat relationships with local government as an important resource. Political turnover thus creates uncertainty on the sustainability of the resources, causing firms to adjust their business decisions.Theoretical contribution/Originality: This study provides an understanding of how changes in political conditions in Indonesia change business strategy decisions. Besides, this research area is still under-studied in Indonesia.Research limitation: Since this study focused on the uncertainty caused by political turnover, this study did not measure firms’ political connections.

Path Planning of an Unmanned Surface Vessel Based on the Improved A-Star and Dynamic Window Method

In order to ensure the safe navigation of USVs (unmanned surface vessels) and real-time collision avoidance, this study conducts global and local path planning for USVs in a variable dynamic environment, while local path planning is proposed under the consideration of USV motion characteristics and COLREGs (International Convention on Regulations for Collision Avoidance at Sea) requirements. First, the basis of collision avoidance decisions based on the dynamic window method is introduced. Second, the knowledge of local collision avoidance theory is used to study the local path planning of USV, and finally, simulation experiments are carried out in different situations and environments containing unknown obstacles. The local path planning experiments with unknown obstacles can prove that the local path planning algorithm proposed in this study has good results and can ensure that the USV makes collision avoidance decisions based on COLREGs when it meets with a ship.

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.

Do more masculine-faced CEOs reflect more tax avoidance? Evidence from Indonesia

The present paper provides new empirical evidence on the relationship between CEO facial masculinity and tax avoidance. We use data from non-financial companies listed on the Indonesia Stock Exchange during the period 2010–2019. The findings suggest that the CEO facial masculinity is positively associated with tax avoidance, which is one of the risky financial decisions. We also conducted a battery of robustness tests, including testing the sample of companies with/without a risk management committee, the interaction with CEO age and big 4 audit firm, the implementation of the tax amnesty in Indonesia, and addressing endogeneity using the propensity score matching. The current measurement of CEO masculinity, which is based on the fWHR of the CEO’s photo, could be further developed by future studies using artificial intelligence (AI) technology. This study contributes to the literature on CEO characteristics by filling in the gaps of biological characteristics associated with tax avoidance decisions. This is the first study investigating the relationship between CEO biological characteristics and tax avoidance.

Influences on indoor environmental trigger remediation uptake for children and young people with asthma: A scoping review.

Children and young people (CYP) with asthma can benefit from reduced exposure to indoor environmental allergens and triggers but may not consistently have avoidance strategies implemented. To inform future interventions to increase trigger and allergen avoidance and enhance asthma control, a greater understanding of the influences on avoidance behaviours is necessary. A systematic scoping review was selected to summarize evidence on what influences family uptake of indoor environmental asthma trigger avoidance strategies for CYP with asthma and identify research gaps. Primary studies of any design, including CYP (≤18 years) with asthma, and/or parent-carers, available in English and conducted since 1993, were eligible. Searches included nine databases, hand-searching reference lists and citation searching. Thirty-three articles were included and are summarized narratively due to heterogeneity. Influences appear complex and multifactorial and include barriers to strategy uptake, health beliefs and personal motivation. Research specifically related to family understanding of allergic sensitisation status and exposure risks, and how these may inform avoidance implementation is required. Patient and public involvement (PPI) was not reported in included articles, although two studies used participatory methods. There is limited research on family asthma trigger management, particularly what influences current management behaviours. Variation in families' ability to identify important triggers, understand exposure riskand consistently reduce exposures warrants further exploratory research to explain how families reach avoidance decisions, and what future interventions should aim to address. Further PPI-informedresearch to address such gaps, could enable theory-based, person-centred interventions to improve the uptake of asthma trigger remediation. An asthma-specific PPI group contributed to the decision-making for the funding for the wider project this review sits within. The findings of this scoping review have informed the subsequent phases of the project, and this was discussed with PPI groups (both adult and CYP groups) when proposing the next phases of the project.

Kepemilikan Keluarga dan Penghindaran Pajak di Indonesia: Efek Moderasi dari Kualitas Audit

Tax avoidance is an action that can be taken by companies in an effort to reduce their obligation to pay taxes legally. Family and non-family companies have different characteristics in making tax avoidance decisions where family companies tend to prioritize the company's reputation in the future. This study aims to examine the effect of family ownership on the occurrence of tax evasion, as well as the role of audit quality in moderating this relationship. There are 777 companies listed on the Indonesia Stock Exchange from 2017 to 2021 as the study population. Companies that meet the sample criteria are 239 companies. The data analysis method used in this study is the panel regression method. The results showed that family ownership can influence ETR in a significant positive way. The effect of audit quality cannot strengthen the relationship between family ownership and tax evasion.
 Keywords: Tax Avoidance; Family Ownership; Audit Quality

The Influence of Transfer Pricing and Sales Growth on Decisions Tax Avoidance

The formulation of the problem in this study is how the influence of transfer pricing and sales growth on tax avoidance decisions (a case study on a coal sub-sector mining company listed on the Indonesia Stock Exchange for the 2017-2021 period). The type of research used is associative research. The data used is secondary data. The sample in this study were 12 coal mining companies listed on the Indonesia Stock Exchange in 2017-2021. Data collection technique is document analysis . The data analysis method in this research is quantitative analysis. The results of this study indicate that transfer pricing and sales growth have a significant effect on tax avoidance, partially transfer pricing has a significant effect on tax avoidance , sales growth has no significant effect on tax avoidance.

An interpretable knowledge-based decision support method for ship collision avoidance using AIS data

AIS data include ship spatial-temporal and motion parameters which can be used to excavate the deep-seated information. In this article, an interpretable knowledge-based decision support method is established to guide the ship to make collision avoidance decisions with good seamanship and ordinary practice of seamen using AIS data. First, AIS data is preprocessed and trajectory reconstructed to restore the ship historical navigation state, and a ship encounter identification model is constructed according to the encounter characteristics; Second, a two-stage collision avoidance behavior extraction algorithm is formed to build a behavior knowledge base, and the scenario similarity model is constructed to measure and match similar scenarios based on ship position, motion tendency and collision risk. Then, the Delaunay Triangulation Network is used to fuse ship trajectories of similar scenario to form the collision avoidance path. Finally, a case study is performed using the real AIS data outside Ningbo-Zhoushan Port waters, China, and the effectiveness of the planned path is verified by setting the head-on and crossing situations and comparison between the planned and real paths. Results indicate that the proposed model can extract the ship collision avoidance behavior accurately, and the planned path can ensure navigation safety.

Multi-Ship Encounter Situation Identification and Analysis Based on AIS Data and Graph Complex Network Theory

In order to detect multi-ship encounter situations and improve the safety of navigation, this paper proposed a model which was able to mine multi-ship encounter situations from Automatic identification system (AIS) data and analyze the encounter spatial-temporal process and make collision avoidance decisions. Pairwise encounters identification results and ship motion index were combined into a ship encounter graph network which can use the complex network theory to describe the encounter spatial-temporal process. Network average degree, network average distance and network average clustering coefficient were selected. Based on the recognition results of pairwise encounter identification results, a discrete multi-ship encounter network is constructed. The process of multi-ship encounters from simple to complex to simple is mined based on the process of average network degree from 0 to 0 to obtain a continuous spatial-temporal process. The results can be used for multi-ship encounter situation awareness, multi-ship collision avoidance decision-making and channel navigation evaluation, and also provide data for machine learning. Quaternary dynamic ship domain, fuzzy logic and the weighted PageRank algorithm were used to rank the whole network risk, which is critical to “key ship collision avoidance.” This method overcame the problem that the traditional collision risk evaluation method is only applicable to the difference between two ships and ship perception. The risk rank combined with the artificial potential field method was used. Compared with the traditional artificial potential field method, this method has fewer turns and a smoother trajectory.

Your heart is where your treasure is: Family chairman and tax avoidance in family-controlled firms

This study examines the effects of family chairman on corporate tax avoidance in family-controlled firms. By employing the socioemotional wealth (SEW) theory, we find that family chairmen engage in less tax avoidance than non-family counterparts, because the decisions of family leaders are more affected by the incentive to preserve SEW, which is built on family members’ identification with the family business and their concerns about the family reputation. Such an effect is more pronounced in firms with higher reputational risks. Furthermore, we discover that the chairman plays a dominant role in explaining tax avoidance decisions compared to the CEO and CFO. Our findings are further supported by the change in tax avoidance associated with chairman turnover. We also exclude plausible alternative explanations based on the agency theory, and our results are robust to a battery of sensitivity tests.