Intelligent Collision Avoidance Research Articles

Unmanned Ship Collision Avoidance Action Plan Deduction Method under Man–Machine Interactive Negotiation in Collision Avoidance Scenarios

With the development of artificial intelligence technology, the future water traffic environment will present a new pattern of coexistence of manned ships and unmanned ships, because unmanned ships are different from manned ships in situation understanding, collision avoidance decision-making, and so on. Therefore, the obstacle avoidance planning between unmanned ships and manned ships becomes extremely complex, and collision avoidance behavior scheme deduction becomes a key step in solving the problems related to situation understanding and collision avoidance decision-making in collision avoidance scenarios. In this paper, the situation understanding of the pilot for different collision avoidance situations is integrated into the dynamic obstacle avoidance model, and an intelligent navigation collision avoidance system is proposed to assist in deducing the collision avoidance action plan of the unmanned ship in the man–machine coexistence scenario. The intelligent navigation collision avoidance system is divided into two parts, namely a ship situation understanding part and a ship obstacle avoidance part, wherein ship situation understanding is used for realizing the transition of the collision state of the unmanned ship in the deduction process by constructing a collision-state set and a behavior decision set by using a finite state machine (FSM). Regarding ship obstacle avoidance, ship velocity obstacle is calculated based on the reciprocal velocity obstacle method (RVO), and the collision avoidance action is selected by using the behavior decision generated by the FSM to realize the dynamic collision avoidance deduction of the unmanned ship. In this study, the validity and effectiveness of the intelligent navigation collision avoidance system proposed in this paper are verified by case studies in a variety of collision avoidance scenarios. The system successfully solves the problem of intelligent collision avoidance planning, provides reliable support for the intelligent collision avoidance of unmanned ships, provides a feasible solution for safety and efficiency in sea navigation, and provides a valuable reference for the design and development of future intelligent navigation collision avoidance systems for ships.

Multi-ship encounter situation graph structure learning for ship collision avoidance based on AIS big data with spatio-temporal edge and node attention graph convolutional networks

With the increasing number of ships on the sea, the frequency multi-ship encounters situation was becoming more common than two-ship encounter. The complexity and risk of the navigation will exponentially increase with the more ships involved. Relying solely on the International Regulations for Preventing Collisions at Sea (COLREGS) (objective knowledge) was insufficient to handle the multi-ship intelligent collision avoidance problem, also needed the ship officer's good seamanship (subjective knowledge). In this study, we propose a methodology that combines subjective insights from AIS big data with objective analysis through multi-ship encounters recognition with graph convolutional networks (GCN). (1) The ship encounter 8-azimuths map was utilized to identify the two-ship encounter situation (25 types) from the AIS data. (2) Identify the multi-ship encounters trajectory data by cross-matching the two-ship encounter data. (3) To handle the intricate relationship information between the multiple ships which were transformed into a graph structure using graph theory. (4) Finally, a spatial-temporal edge and node attention graph convolutional network (ST-ENAGCN) was proposed with the graph convolutional unit and long short-term memory (LSTM) unit. The 2022 Ningbo Sea area AIS big data was utilized to achieve graph-structured learning regarding human experiences during the multi-ship encounters situation. The results indicate that the ST-ENAGCN model can understand the complex marine traffic situation to make the own ship collision avoidance decisions. This study contributes significantly to the increased efficiency and safety of sea operations in complex marine traffic conditions and support autonomous navigation of swarm of MASSs under the human-machine hybrid (HMH) conditions to better understand the collision avoidance intention of the ship officer in the multi-ship encounters situation.

Development of pedestrian collision avoidance strategy based on the fusion of Markov and social force models

Abstract. In urban traffic, accurate prediction of pedestrian trajectory and advanced collision avoidance strategy can effectively reduce the collision risk between intelligent vehicles and pedestrians. In order to improve the prediction accuracy of pedestrian trajectory and the safety of collision avoidance, a longitudinal and lateral intelligent collision avoidance strategy based on pedestrian trajectory prediction is proposed. Firstly, the process of a pedestrian crossing the road is considered as a combination of free motion described by first-order Markov model and the constrained motion presented by improved social force model. The predicted pedestrian trajectory is obtained by weighted fusion of the trajectories of the two models with a multiple linear regression algorithm. Secondly, according to the predicted pedestrian trajectory and time to collision (TTC) the longitudinal and lateral collision avoidance strategy is designed. The improved artificial potential field method is used to plan the lateral collision avoidance path in real time based on the predicted pedestrian position, and a fuzzy controller is constructed to obtain the desired deceleration of the vehicle. Finally, the pedestrian motion fusion model and the longitudinal and lateral collision avoidance strategy are verified by Prescan and Simulink co-simulation. The results show that the average displacement error (ADE) and final displacement error (FDE) of pedestrian trajectory based on pedestrian motion fusion model are smaller compared with a Markov model and improved social force model, and the proposed pedestrian collision avoidance strategy can effectively achieve longitudinal and lateral collision avoidance.

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.

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.

Autonomous Navigation Decision-Making Method for a Smart Marine Surface Vessel Based on an Improved Soft Actor–Critic Algorithm

In this study, an intelligent hybrid algorithm based on deep-reinforcement learning (DRL) is proposed to achieve autonomous navigation and intelligent collision avoidance for a smart autonomous marine surface vessel (SMASV). First, the kinematic model of the SMASV is used, and clauses 13 to 17 of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) are introduced. Then, the electronic chart is rasterized and used for path planning. Next, states, actions, and reward functions are designed, and collision avoidance strategies are formulated. In addition, a temperature factor and a constrained loss function are used to improve the soft actor–critic (SAC) algorithm. This improvement reduces the challenges of hyperparameter adjustment and improves sampling efficiency. By comparing the improved SAC algorithm with other deep-reinforcement learning (DRL) algorithms based on strategy learning, it is proved that the improved SAC algorithm converges faster than the other algorithms. During the experiment, some unknown obstacles are added to the simulation environment to verify the collision-avoidance ability of the trained SMASV. Moreover, eight sea areas are randomly selected to verify the generalization ability of the intelligent-navigation system. The results show that the proposed method can plan a path for the SMASV accurately and effectively, and the SMASV decision-making behavior in the collision-avoidance process conforms to the COLREGs in both unknown and dynamic environments.

Improved DQN for Dynamic Obstacle Avoidance and Ship Path Planning

The avoidance of collisions among ships requires addressing various factors such as perception, decision-making, and control. These factors pose many challenges for autonomous collision avoidance. Traditional collision avoidance methods have encountered significant difficulties when used in autonomous collision avoidance. They are challenged to cope with the changing environment and harsh motion constraints. In the actual navigation of ships, it is necessary to carry out decision-making and control under the constraints of ship manipulation and risk. From the implementation process perspective, it is a typical sequential anthropomorphic decision-making problem. In order to solve the sequential decision problem, this paper improves DQN by setting a priority for sample collection and adopting non-uniform sampling, and it is applied to realize the intelligent collision avoidance of ships. It also verifies the performance of the algorithm in the simulation environment.

Research on MASS Collision Avoidance in Complex Waters Based on Deep Reinforcement Learning

The research on decision-making models of ship collision avoidance is confronted with numerous challenges. These challenges encompass inadequate consideration of complex factors, including but not limited to open water scenarios, the absence of static obstacle considerations, and insufficient attention given to avoiding collisions between manned ships and MASSs. A decision model for MASS collision avoidance is proposed to overcome these limitations by integrating the strengths of model-based and model-free methods in reinforcement learning. This model incorporates S-57 chart information, AIS data, and the Dyna framework to improve effectiveness. (1) When the MASS’s navigation task is known, a static navigation environment is built based on S-57 chart information, and the Voronoi diagram and improved A* algorithm are used to obtain the energy-saving optimal static path as the planned sea route. (2) Given the small main dimensions of an MASS, which is easily affected by wind and current factors, the motion model of an MASS is established based on the MMG model considering wind and current factors. At the same time, AIS data are used to extract the target ship (manned ship) data. (3) According to the characteristics of the actual navigation of ships at sea, the state space, action space, and reward function of the reinforcement learning algorithm are designed. The MASS collision avoidance decision model based on the Dyna-DQN model is established. Based on the DQN algorithm, the agent (MASS) and the environment interact continuously, and the actual interaction data generated are used for the iterative update of the collision avoidance strategy and the training of the environment model. Then, the environment model is used to generate a series of simulated empirical data to promote the iterative update of the strategy. Using the waters near the South China Sea as the research object for simulation verification, the navigation tasks are divided into three categories: only considering static obstacles, following the planned sea route considering static obstacles, and following the planned sea route considering both static and dynamic obstacles. The results show that through repeated simulation experiments, an MASS can complete the navigation task without colliding with static and dynamic obstacles. Therefore, the proposed method can be used in the intelligent collision avoidance module of MASSs and is an effective MASS collision avoidance method.

Collision avoidance for autonomous ship using deep reinforcement learning and prior-knowledge-based approximate representation

Reinforcement learning (RL) has shown superior performance in solving sequential decision problems. In recent years, RL is gradually being used to solve unmanned driving collision avoidance decision-making problems in complex scenarios. However, ships encounter many scenarios, and the differences in scenarios will seriously hinder the application of RL in collision avoidance at sea. Moreover, the iterative speed of trial-and-error learning for RL in multi-ship encounter scenarios is slow. To solve this problem, this study develops a novel intelligent collision avoidance algorithm based on approximate representation reinforcement learning (AR-RL) to realize the collision avoidance of maritime autonomous surface ships (MASS) in a continuous state space environment involving interactive learning capability like a crew in navigation situation. The new algorithm uses an approximate representation model to deal with the optimization of collision avoidance strategies in a dynamic target encounter situation. The model is combined with prior knowledge and International Regulations for Preventing Collisions at Sea (COLREGs) for optimal performance. This is followed by a design of an online solution to a value function approximation model based on gradient descent. This approach can solve the problem of large-scale collision avoidance policy learning in static-dynamic obstacles mixed environment. Finally, algorithm tests were constructed though two scenarios (i.e., the coastal static obstacle environment and the static-dynamic obstacles mixed environment) using Tianjin Port as an example and compared with multiple groups of algorithms. The results show that the algorithm can improve the large-scale learning efficiency of continuous state space of dynamic obstacle environment by approximate representation. At the same time, the MASS can efficiently and safely avoid obstacles enroute to reaching its target destination. It therefore makes significant contributions to ensuring safety at sea in a mixed traffic involving both manned and MASS in near future.

An intelligent communication system for collision avoidance on roads: A smart city application

Intelligent communication systems have become the predominant paradigm in vehicular ad hoc networks. Current research in this area is limited to the dissemination of emergency information to vehicles after an accident. However, such emergency situations necessitate prompt dissemination of information to the relevant parties prior to the collision save lives. This paper proposes an intelligent collision avoidance routing technique for VANETs (CARVNET) to address this formidable obstacle. The proposed intelligent communication system aims to avoid collisions on roads by disseminating emergency warning messages instantly to the concerned entities as soon as it detects a driver’s abnormal health condition. Simulation studies are conducted in NETSIM to validate the proposed technique. The experimental results demonstrate that the proposed system outperforms existing communication systems by improving throughput by up to 50%, packet transmission by 20%, packet delivery ratio by 10%, and delay to a negligible degree.

A Novel Driving-Strategy Generating Method of Collision Avoidance for Unmanned Ships Based on Extensive-Form Game Model with Fuzzy Credibility Numbers

This study aims to solve the problem of intelligent collision avoidance of unmanned ships at sea, and it proposes a novel driving strategy generating method of collision avoidance based on an extensive-form game mode with fuzzy credibility numbers. The innovation of this study is to propose an extensive-form game model of unmanned ships under the situation of two-sides clamping and verify the validity by fuzzy credibility. Firstly, this study divides the head-on situation of the ship at sea quantitatively to help the unmanned ship take targeted measures when making collision avoidance decisions. Secondly, this study adopts an extensive-form game model to model the problem of collision avoidance of an unmanned ship in the case of clamping on two sides. Thirdly, the extensive-form game model is organically combined with the fuzzy credibility degree to judge whether the collision avoidance game of unmanned ship achieves the optimal collision avoidance result. The effectiveness of the introduced game model is verified by case analysis and simulation. Finally, an illustrative example shows that the proposed mathematical model can better help unmanned ships make real-time game decisions at sea in the scenario of two-sides clamping effectively.

Intelligent Collision Avoidance Method for Ships Based on COLRGEs and Improved Velocity Obstacle Algorithm

Collision prevention is critical for navigational safety at sea, which has developed rapidly in the past decade and attracted a lot of attention. In this article, an improved velocity obstacle (IVO) algorithm for intelligent collision avoidance of ocean-going ships is proposed in various operating conditions, taking into count both a ship’s manoeuvrability and Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). An integrated model combines a three-degree-of-freedom manoeuvring model with ship propeller characteristics to provide a precise prediction of ships in various manoeuvring circumstances. In the given case, what is different to present studies, this improved algorithm allows for decision-making in two ways: altering course and changing speed. The proposed technique is demonstrated in a variety of scenarios through simulation. The findings reveal that collision-avoidance decision-making can intelligently avoid collisions with the target ships (TSs) in multi-ship situations.

A COLREGs-Compliant Collision Avoidance Decision Approach Based on Deep Reinforcement Learning

It is crucial to develop a COLREGs-compliant intelligent collision avoidance system for the safety of unmanned ships during navigation. This paper proposes a collision avoidance decision approach based on the deep reinforcement learning method. A modified collision avoidance framework is developed that takes into consideration the characteristics of different encounter scenarios. Hierarchical reward functions are established to assign reward values to constrain the behavior of the agent. The collision avoidance actions of the agent under different encounter situations are evaluated on the basis of the COLREGs to ensure ship safety and compliance during navigation. The deep Q network algorithm is introduced to train the proposed collision avoidance decision framework, while various simulation experiments are performed to validate the developed collision avoidance model. Results indicate that the proposed method can effectively perform tasks that help ships avoid collisions in different encounter scenarios. The proposed approach is a novel attempt for intelligent collision avoidance decisions of unmanned ships.

Research on Unmanned Ship Path Planning based on RRT Algorithm

The increase of water shipping density, coupled with the increasing complexity of the waterway, the ship's intelligent collision avoidance system plays a pivotal role in the transportation safety of shipping. Therefore, it has gradually become the focus of water traffic safety research. In recent years, China is still facing a period of great development in navigation technology, safety, green, digital, intelligent will become the development trend. Although the waterway environment has been improving, the increase of shipping vessels has led to frequent accidents in the waterway, and the traditional management methods and means lack real-time monitoring and scheduling of vessels, and the management lacks intelligence and information technology. In this paper, we solve the ship path problem from the point of path planning of intelligent ships. RRT* algorithm is a common method for path planning, but the traditional RRT* algorithm has problems in ship path planning, slow convergence speed, many path turns and large curvature fluctuations. For these problems, an improved fast extended random tree algorithm (Bi-RRT) is proposed. The improved RRT* algorithm is simulated and verified by MATLAB simulation platform. The simulation results show that the research algorithm can shorten the planning time, reduce the number of iterations, and has better feasibility and effectiveness.

A method to extract the Quaternion Ship Domain parameters from AIS data

The ship domain concept is of great interest for ship traffic modelling, risk assessment and intelligent collision avoidance. The paper proposes and applies a method to define AIS data-based empirical polygonal ship domains, based on traffic density matrices that are derived around each reference ship from an AIS dataset. A modified Quaternion Ship Domain allowing for different shapes for each quadrant is proposed, which results in a better fitting to the empirical domain. The parameters of Quaternion Ship Domains that best fit the empirical polygonal domains are determined for cargo ships and tankers of different lengths. Violations of the Quaternion Ship Domain are then used as an indicator of collision risk that is graphically represented in the study area, providing important information to support maritime traffic monitoring and control tasks.

DCA-Based Collision Avoidance Path Planning for Marine Vehicles in Presence of the Multi-Ship Encounter Situation

The problem of ship collision avoidance path planning is one of the key problems in the ship motion control field. Aiming at the high computational time problem of path planning in multi-ship encounter situations and the impact of the target ship’s action changes on path planning, this paper proposes a dynamic path-planning method based on dynamic cluster analysis (DCA), which is used to dynamically cluster target ships with similar attributes into a group ship, reducing the number of calculated targets and improving the efficiency of path planning. Taking into full consideration the action requirements of the International Regulations for Preventing Collisions at Sea (COLREGs), the course alteration action matrix (CAAM) for collision avoidance is established to limit the space of candidate solutions. On the basis of the rapid optimization capability of the deterministic optimization algorithm (DOA), a dynamic monitoring mechanism is introduced to establish a multi-ship encounter intelligent collision avoidance decision-making model that meets the needs of real-time collision avoidance. The simulation results showed that the method can obtain a dynamic collision avoidance path that is safe and feasible.

Intelligent Collision Avoidance Method for Motion Obstacles of Unmanned Vehicles

At present, the active technology of automobiles is becoming more and more mature and the emergence of driverless vehicles makes it a hotspot in the field of road safety. A new intelligent collision avoidance method for unmanned vehicle motion obstacles is proposed. The kinematics model of unmanned vehicles is established and linearized to obtain the kinematics linear tracking error model of unmanned vehicles and predict the future behavior of unmanned vehicles. The intelligent collision avoidance can be achieved by improving the artificial potential field model of the unmanned vehicle after understanding the future behavior and obstacle information of the unmanned vehicle. The experimental results show that the method has a high detection rate and success rate of obstacle avoidance and low total time-consuming in the process of behavior selection and path planning. It can quickly make collision avoidance responses and reduce the possibility of collision.

A Multiple-Output Hybrid Ship Trajectory Predictor With Consideration for Future Command Assumption

Onboard sensors contribute to data-driven understanding of complex and nonlinear ship dynamics in real time. By using sensors, precise ship trajectory prediction plays a key role in intelligent collision avoidance. A hybrid predictor makes prediction based on a mathematical model of which error is compensated by a black-box model. A Multiple-output Hybrid Predictor (MHP), which makes a long-horizon prediction at a time based on onboard sensor data, was developed in the previous study. However, it cannot handle a time series of future command assumption. This limitation hinders an MHP from being applied to the evaluation of future command assumption in the predictive decision making. A novel architecture of MHP presented in this study converts a long time series of future command assumption into a fixed-length model-based-predicted vessel state; then, it is included in inputs of a black-box error compensator. This idea is robust to multidimensionality of commands and long control horizon. Assuming a low-fidelity vessel model and a limited data are available, simulation experiments are conducted. The effect of environmental disturbances and maneuverings on the prediction performance is examined comprehensively for the first time. The present study successfully incorporates a long time series of future command assumption in an MHP. It reduces the mean error by 81.8% compared to a model-based predictor and by 45.6% compared to a data-driven predictor under Beaufort wind force scale 4 wave, wind, and ocean current. Present study expands the application of MHP to the predictive decision making of future autonomous ships.

Intelligent cooperative collision avoidance via fuzzy potential fields

SummaryThis paper proposes an intelligent cooperative collision avoidance approach combining the enhanced potential field (EPF) with a fuzzy inference system (FIS) to resolve local minima and goal non-reachable with obstacles nearby issues and provide a near-optimal collision-free trajectory. A genetic algorithm is utilized to optimize parameters of membership function and rule base of the FISs. This work uses a single scenario containing all issues and interactions among unmanned aerial vehicles (UAVs) for training. For validating the performance, two scenarios containing obstacles with different shapes and several UAVs in small airspace are considered. Multiple simulation results show that the proposed approach outperforms the conventional EPF approach statistically.

A semi-supervised deep learning model for ship encounter situation classification

Maritime safety is an important issue for global shipping industries. Currently, most of collision accidents at sea are caused by the misjudgement of the ship’s operators. The deployment of maritime autonomous surface ships (MASS) can greatly reduce ships’ reliance on human operators by using an automated intelligent collision avoidance system to replace human decision-making. To successfully develop such a system, the capability of autonomously identifying other ships and evaluating their associated encountering situation is of paramount importance. In this paper, we aim to identify ships’ encounter situation modes using deep learning methods based upon the Automatic Identification System (AIS) data. First, a segmentation process is developed to divide each ship’s AIS data into different segments that contain only one encounter situation mode. This is different to the majority of studies that have proposed encounter situation mode classification using hand-crafted features, which may not reflect the actual ship’s movement states. Furthermore, a number of present classification tasks are conducted using substantial labelled AIS data followed by a supervised training paradigm, which is not applicable to our dataset as it contains a large number of unlabelled AIS data. Therefore, a method called Semi-Supervised Convolutional Encoder–Decoder Network (SCEDN) for ship encounter situation classification based on AIS data is proposed. The structure of the network is not only able to automatically extract features from AIS segments but also share training parameters for the unlabelled data. The SCEDN uses an encoder–decoder convolutional structure with four channels for each segment (distance, speed, Time to the Closed Point of Approach (TCPA) and Distance to the Closed Point of Approach (DCPA)) been developed. The performance of the SCEDN model are evaluated by comparing to several baselines with the experimental results demonstrating a higher accuracy can be achieved by our proposed model.