Collision Risk Index Research Articles

Adaptive collision avoidance decision system for autonomous ship navigation

Autonomous navigation for ships is recognised as a pivotal trend in the future maritime industry, especially autonomous avoidance collision decision-making in complex multi-ship encounter scenarios, which constitutes a significant area of research. This paper presents an adaptive autonomous navigation decision system for ships, which addresses the problems posed by intricate situations. A dynamic collision risk index (CRI) model based on the dangerous manoeuvring intervals is established, derived using an improved velocity obstacle (VO) algorithm, which incorporates ship manoeuvrability constraints and the Convention on International Regulations for preventing collisions at sea (COLREGs). Autonomous navigation decision-making is achieved by combining the fuzzy PID (Proportional–Integral–Derivative) course control method and the trajectory tracking of the adaptive Line-of-Sight (LOS) algorithm. Combining the Model Predictive Control (MPC) idea, this approach enables ships to navigate autonomously and adaptively in challenging water conditions. To evaluate the effectiveness of the proposed approach, two case studies involving the five-ship encounter situation and the six-ship encounter situation are conducted. The results demonstrate that the presented autonomous navigation decision-making approach efficiently tracks the planned route while safely avoiding all target ships. Moreover, the decision-making exhibits adaptability in coping with the dynamic manoeuvres of the target ships, including alterations in the course and changes in speed. These findings confirm that the proposed system significantly enhances the ability of autonomous ships to navigate safely and adaptively in complex multi-ship encounter scenarios, demonstrating its practical relevance and effectiveness for real-world maritime applications.

Comparison of Collision Avoidance Algorithms for Unmanned Surface Vehicle Through Free-Running Test: Collision Risk Index, Artificial Potential Field, and Safety Zone

This paper details the development of a collision avoidance algorithm for unmanned surface vehicles (USVs) and its validation using free-running tests. The USV, designed as a catamaran, incorporates a variety of sensors for its guidance, navigation, and control system. It performs turning maneuvers using thrusters positioned on the port and starboard sides. The robot operating system is used to streamline communication, transmitting data such as position, orientation, and situational information from diverse sensors. Using the collision risk index (CRI) method, the algorithm calculates risk based on the distance to obstacles and the angle to the desired waypoint, directing the USV on a path with minimized risk. Noise within the data captured by the two-dimensional light detection and ranging system is filtered out using the k-dimensional tree and Euclidean distance methods, ensuring single obstacles are distinctly identified. To assess the efficacy of the CRI-based collision avoidance algorithm, it was benchmarked against other algorithms rooted in the artificial potential field and safety zone methods within an artificial tank setting. The results highlight the CRI method’s superior time efficiency and optimality in comparison to its counterparts.

Collision Avoidance for Unmanned Surface Vehicles in Multi-Ship Encounters Based on Analytic Hierarchy Process–Adaptive Differential Evolution Algorithm

Path planning and collision avoidance issues are key to the autonomous navigation of unmanned surface vehicles (USVs). This study proposes an adaptive differential evolution algorithm model integrated with the analytic hierarchy process (AHP-ADE). The traditional differential evolution algorithm is enhanced by introducing an elite archive strategy and adaptively adjusting the scale factor F and the crossover factor CR to balance global and local search capabilities, preventing premature convergence and improving the search accuracy. Additionally, the collision risk index (CRI) model is optimized and combined with the quaternion ship domain, enhancing the precision of CRI calculations and USV autonomous collision avoidance capabilities. The improved CRI model, the International Regulations for Preventing Collisions at Sea, and the optimal collision avoidance distance were incorporated as evaluation factors in a fitness function assessment, with weights determined through the AHP to enhance the rationality and accuracy of the fitness function. The proposed AHP-ADE algorithm was compared with the improved particle swarm algorithm, and the performance of the algorithm was comprehensively evaluated using safety, economy, and operational efficiency. Simulation experiments on the MATLAB platform demonstrated that the proposed AHP-ADE algorithm exhibited better performance in scenarios involving multiple ship encounters, thus proving its effectiveness.

A Fuzzy Fusion Method for Multi-Ship Collision Avoidance Decision-Making with Merchant and Fishing Vessels

In multi-vessel collision avoidance decision-making, the collision between merchant and fishing vessels is a significant challenge. This paper proposes a fuzzy fusion method for making avoidance decisions under the influence of the navigation environment. First, C-means clustering was used to collect and analyze Automatic Identification System (AIS) data from fishing vessels. On this basis, the environment collision risk was determined using fuzzy reasoning. Second, the basic collision risk is obtained by calculating the DCPA and TCPA, and the integrated Collision Risk Index (CRI) is concluded by fuzzy logic through basic collision risk and the environment collision risk. The similar cases are extracted from the fuzzy case database, and collision avoidance decisions for merchant vessels are formulated following fuzzy adjustments. Finally, to validate the method, data from Chengshantou coastal waters is employed for verification. The results show that it can provide theoretical guidance and practical value for merchant vessels in making collision avoidance decisions.

Ship Trajectory Planning and Optimization via Ensemble Hybrid A* and Multi-Target Point Artificial Potential Field Model

Ship path planning is the core problem of autonomous driving of smart ships and the basis for avoiding obstacles and other ships reasonably. To achieve this goal, this study improved the traditional A* algorithm to propose a new method for ship collision avoidance path planning by combining the multi-target point artificial potential field algorithm (MPAPF). The global planning path was smoothed and segmented into multi-target sequence points with the help of an improved A* algorithm and fewer turning nodes. The improved APF algorithm was used to plan the path of multi-target points locally, and the ship motion constraints were considered to generate a path that was more in line with the ship kinematics. In addition, this method also considered the collision avoidance situation when ships meet, carried out collision avoidance operations according to the International Regulations for Preventing Collisions at Sea (COLREGs), and introduced the collision risk index (CRI) to evaluate the collision risk and obtain a safe and reliable path. Through the simulation of a static environment and ship encounter, the experimental results show that the proposed method not only has good performance in a static environment but can also generate a safe path to avoid collision in more complex encounter scenarios.

Research on ship navigation strategy in dynamic sea ice environments based on flexibility velocity obstacles algorithm

Sea ice constitutes one of the primary risks in Arctic navigation. Currently, how to maneuver a ship within a sea ice environment relies predominantly on the experience of the pilot. In this paper, based on satellite image data and an improved sea ice target matching approach, a dynamic ship navigation environment within the sea ice zone is established. Subsequently, we developed a sea ice collision risk index (ICRI), which takes into account the relative motion relationship between the sea ice and the ship as well as the sea ice area parameter. Finally, a flexible velocity obstacle (FVO) algorithm is proposed by the Arctic ice navigation proposal, enabling the optimization of ship routes and preventing ships from passing through the central region of large floating ice. The results show that the method proposed in this paper successfully constructs the navigation environment model in the drifting ice region, and can provide real-time maneuvering guidance for ships, effectively reduce the risk of sea ice collision, and optimize the navigation path planning in the ice region. It can improve the safety and pre-planning efficiency of ship navigation in the ice region.

Risk Analysis and Visualization of Merchant and Fishing Vessel Collisions in Coastal Waters: A Case Study of Fujian Coastal Area

The invasion of ship domains stands out as a significant factor contributing to the risk of collisions during vessel navigation. However, there is a lack of research on the mechanisms underlying the collision risks specifically related to merchant and fishing vessels in coastal waters. This study proposes an assessment method for collision risks between merchant and fishing vessels in coastal waters and validates it through a comparative analysis through visualization. First of all, the operational status of fishing vessels is identified. Collaboratively working fishing vessels are treated as a unified entity, expanding their ship domain during operation to assess collision risks. Secondly, to quantify the collision risk between ships, a collision risk index (CRI) is proposed and visualized based on the severity of the collision risk. Finally, taking the high-risk area for merchant and fishing vessel collisions in the Minjiang River Estuary as an example, this paper conducts an analysis that involves classifying ship collision scenarios, extracts risk data under different collision scenarios, and visually analyzes areas prone to danger. The results indicate that this method effectively evaluates the severity of collision risk, and the identified high-risk areas resulting from the analysis are verified by the number of accidents that occurred in the most recent three years.

Novel collision risk measurement method for multi-ship encounters via velocity obstacles and temporal proximity

The collision risk assessment of multi-ship encounters in busy waters is of great significance for collision risk early warning and the intelligent supervision of maritime traffic. In this paper, a quantitative and visual evaluation method is proposed to describe changes in multi-ship collision risk in the velocity space according to multi-ship collision risk characteristics. Non-linear velocity obstacles and closest point of approach were combined to measure the risk of ship collision from the perspective of both the difficulty and urgency of the encounter. The proposed method was verified via case studies using historical Automatic Identification System data for the East China Sea. The method advantages were confirmed via comparison to the conventional time-varying collision risk and collision risk index methods. The results show that the proposed method can quantify changes in multi-ship collision risk more intuitively and effectively. The proposed method could be further developed as a new risk indicator for maritime traffic safety management and could aid in the navigation safety of manned and unmanned ships.

A machine vision method for the evaluation of ship-to-ship collision risk

The development of ship technology and information technology has been driving the continuous improvement of ship intelligence, with safety being an inevitable requirement in the shipping industry. A machine vision-based ship collision warning method is proposed for high monitoring system cost and limited information acquisition in safety design of autonomous ship navigation. The method combines machine learning with image algorithms. Firstly, the backbone of YOLOv7 detector is replaced by EfficientFormerV2 network to achieve model lightweight while ensuring detection accuracy. Public datasets SeaShips, Flow and self-made ship pictures are combined, and the network is trained on this dataset. StrongSORT is used for target tracking. Secondly, a data fusion algorithm is introduced to determine the target point at the bow-bottom of the ship based on the time-varying attitude of the camera and the time-series features of the bounding boxes. Ship navigation trajectory estimation is performed using image algorithms. Finally, a collision evaluation model is established to calculate the collision risk index. Experimental results demonstrate that the improved YOLOv7 network maintains similar mAP.5 and Recall compared to the original model, while reducing the parameters by 31.2 % and GFLOPs by 58.4 %. The accuracy of target ship trajectory estimation is high, with MAE values below 1.5 % and RMSE values below 2 % in experiments. In ship collision warning experiments, the proposed method accurately identifies navigating vessels, estimates the trajectories, and provides timely warnings for imminent collision accidents. Compared to traditional ship collision warning methods, this paper offers a more intelligent and lightweight solution.

Assessing ship collision risks in maritime transport based on seafarers’ experience

Assessing ship collision risks considering human factors presents persistent challenges due to the multivariate uncertainties faced by seafarers. Therefore, this study proposes a fuzzy-logic-based method to model ship collision risks in an area considering human factors. This method utilizes a hierarchical fuzzy system consisting of two fuzzy systems, fuzzy system 1 and fuzzy system 2, which are used to calculate the temporary risks and human factors, respectively. Aggregating the risks from both subsystems produces the overall collision risk index. This approach streamlines on-board risk appraisal, thereby reducing the navigator workload. While the current study delineates the risk modeling methodology, succeeding works validate the framework through simulated experiments. With adequate verification, this hierarchical fuzzy approach can assist next-generation risk-regulated navigation practices and enrich maritime agencies' traffic risk management.

Assessing ship collision risks in maritime transport based on seafarers’ experience

Assessing ship collision risks considering human factors presents persistent challenges due to the multivariate uncertainties faced by seafarers. Therefore, this study proposes a fuzzy-logic-based method to model ship collision risks in an area considering human factors. This method utilizes a hierarchical fuzzy system consisting of two fuzzy systems, fuzzy system 1 and fuzzy system 2, which are used to calculate the temporary risks and human factors, respectively. Aggregating the risks from both subsystems produces the overall collision risk index. This approach streamlines on-board risk appraisal, thereby reducing the navigator workload. While the current study delineates the risk modeling methodology, succeeding works validate the framework through simulated experiments. With adequate verification, this hierarchical fuzzy approach can assist next-generation risk-regulated navigation practices and enrich maritime agencies' traffic risk management.

The Impact of Connected and Autonomous Vehicle Platoon’s Length on Expressway Traffic Flow Characteristics Based on Symmetry Lane Changing Rules

This study mainly investigates the maximum length of CAV (Connected and Autonomous Vehicle) platoons in a heterogeneous traffic flow environment. By employing MATLAB to simulate the heterogeneous traffic flow on expressways, this study focuses on the maximum platoon length of CAV platoons and explores their impact on the traffic flow characteristics on expressways. Firstly, based on four different car-following modes of heterogeneous traffic flow, F-STCA (Flexible–Symmetric Two-Lane Cellular Automata Model) and the symmetric lane-changing strategy, the study refines and improves the construction of the NaSch (Nagel and Schreckenberg) model introduced into the Gipps safety distance formula. The whole improvement process is based on the acceleration decay characteristics of vehicles on expressway. Secondly, the congestion situations under different maximum platoon lengths are compared using fundamental heat maps of the simulation data. The evolution of the fundamental diagram with changes in maximum platoon length is studied to investigate the impact and magnitude of maximum platoon lengths on the road capacity under different CAV permeabilities. Finally, the study explores the stability and safety of heterogeneous traffic flow involving CAV platoons using SD (Standard Deviation) and TERCRI (Time-Exposed Rear-End Collision Risk Index). The results show that when the CAV’s permeability does not reach a high level on heavily trafficked sections of the expressway, considering the overall average speed, the maximum platoon length should not be set too long and should be around five vehicles. This restriction does not apply when the CAV permeability approaches 100%.

Ship collision avoidance route planning using CRI-based A∗ algorithm

This study presents a novel ship route planning algorithm that takes into account both operational economy and safety by integrating the A∗ algorithm with a collision avoidance algorithm that evaluates the Collision Risk Index (CRI) between the own ship and the target ship. The CRI-based A∗ algorithm defines a penalty zone, allowing the own ship to explore safe routes based on the International Regulations for Preventing Collisions at Sea 1972 (COLREGs) and performs an adaptive and effective node search on an extended local map grid according to various encounter situations. The proposed algorithm is validated through simulations of head-on, fine-broad crossing, converging crossing, and overtaking encounters, indicating an economical and safe optimum route compared to conventional ship domain-based route planning.

Collision Risk Index Calculation Based on an Improved Ship Domain Model

The traditional ship collision risk index model based on the distance at the closest point of approach (DCPA) and the time to the closest point of approach (TCPA) is insufficient for estimating ship collision risk and planning collision avoidance operations. This paper constructs an elliptical, dynamic ship domain that changes with ship speed and maneuverability parameters to overcome subjective human factors. Based on the constructed domain model, the concept of the ship domain proximity factor is introduced to improve the ship collision risk model based on DCPA and TCPA, and a risk calculation function model that considers the safety of ship navigation is constructed. The numerical calculation of the improved collision risk index calculation model confirms that the enhanced model has a higher rate of identification of risk between ships. The model is more compatible with the requirements of ship navigation decision-making and can provide theoretical support and a technical basis for research on ship collision avoidance decision-making.

Critical Collision Risk Index Based on the Field Theory

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.

Ship Collaborative Path Planning Method Based on CS-STHA

Ship path planning is one of the key technologies for ship automation. Establishing a cooperative collision avoidance (CA) path for multi-ship encounters is of great value to maritime intelligent transportation. This study aims to solve the problem of multi-ship collaborative collision avoidance based on the algorithm of Conflict Search (CS) and Space-Time Hybrid A-star (STHA). First, a static CA path is searched for each ship by using the space-time Hybrid A-star algorithm, and the conflict risk area is determined according to the ship safety distance constraint and fuzzy Collision Risk Index (CRI). Secondly, the space-time conflict constraint is introduced into the multi-ship cooperative CA scheme, and the binary tree is used to search for an optimal navigation path with no conflict and low cost. In addition, the optimal path is smoothed by using cubic interpolation to make the path consistent with actual navigation practice and ship maneuvering characteristics. Finally, considering the constraints of the International Regulations for Preventing Collisions at Sea (COLREGs), the typical two-ship and multi-ship encounter scenarios are designed and simulated to verify the effectiveness of the proposed method. Furthermore, a comparative analysis of actual encounters and encounters based on CS-STHA is also carried out. The results indicate that the proposed algorithm in the study can obtain an optimal CA path effectively and provide a reference of CA decision-making for autonomous ships.

Collision Avoidance Method for Autonomous Ships Based on Modified Velocity Obstacle and Collision Risk Index

A novel real-time collision avoidance method for autonomous ships based on modified velocity obstacle (VO) algorithm and grey cloud model is proposed. A typical VO algorithm is used to judge whether there is a collision risk for ships in the potential collision area (PCA). Then, in order to quantify the collision risk of ships in different encounter situations within the PCA and trigger a prompt warning of danger of collision, this study sets up a novel collision risk assessment method based on asymmetric grey cloud model (AGC). It can effectively consider the randomness, ambiguity, and incompleteness of the information in the ship collision risk evaluation process. Moreover, reachable collision-free velocity sets under different encounter situations and optimal steering angle model are constructed. A real-time collision avoidance method based on modified VO algorithm and manoeuvring motion characteristics of vessels is put forward. In this model, various constraints are considered including the International Regulations for Preventing Collisions at Sea (COLREGs), ship manoeuvrability, and ordinary practice of seaman. Finally, several case studies are carried out to verify the performance and reliability of the collision avoidance model. The results show that the proposed method can not only effectively identify and quantify the collision risk in real-time but also offer proper collision-free solutions for autonomous ships.

USV collision hazard assessment and track planning algorithm

To improve the autonomous collision avoidance capability of an unmanned surface vehicle (USV) under complex working conditions, this study proposes a local track planning algorithm for USVs based on collision hazard assessment. This algorithm uses hierarchical analysis and discrepancy method to determine the collision risk index (CRI) values of different encounter scenarios, and then introduces fuzzy inference theory to construct a quantitative collision hazard assessment model based on the principle of combining subjective and objective assessment and the pessimistic principle of class classification, which provides a decision basis for a collision avoidance plan. In response to the unexpected encounter, an improved vector field histogram algorithm is proposed to use the CRI value of the obstacle as the key parameter of the histogram, solve the restricted area by removing the grid model and threshold improvement, establish the cost function to calculate the optimal sailing interval under the rules and dynamics constraints of the Convention on International Regulations for Preventing Collisions at Sea (COLREGS), and realise the local target point by tracking local track planning during autonomous collision avoidance. Simulation and on-lake experiments demonstrate the effectiveness of the collision hazard assessment model and local track planning algorithm proposed in this paper.

MULTI-VESSELS COLLISION AVOIDANCE STRATEGY FOR AUTONOMOUS SURFACE VEHICLES BASED ON GENETIC ALGORITHM IN CONGESTED PORT ENVIRONMENT

An improved genetic collision avoidance algorithm is proposed in this study to address the problem that Autonomous Surface Vehicles (ASV) need to comply with the collision avoidance rules at sea in congested sea areas. Firstly, a collision risk index model for ASV safe encounters is established taking into account the international rules for collision avoidance. The ASV collision risk index and the distance of safe encounters are taken as boundary values of the correlation membership function of the collision risk index model to calculate the optimal heading of ASV in real-time. Secondly, the genetic coding, fitness function, and basic parameters of the genetic algorithm are designed to construct the collision avoidance decision system. Finally, the simulation of collision avoidance between ASV and several obstacle vessels is performed, including the simulation of three collision avoidance states head-on situation, crossing situation, and overtaking situation. The results show that the proposed intelligent genetic algorithm considering the rules of collision avoidance at sea can effectively avoid multiple other vessels in different situations.

Fuzzy Inference and Sequence Model-Based Collision Risk Prediction System for Stand-On Vessel.

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.