Collision Risk Research Articles

Ship collision risk assessment: A multi-criteria decision-making framework based on Dempster–Shafer evidence theory

Ship collision risk modelling is dominated by the utilization of the experts’ experiential insights and knowledge. To construct an effective assessment model for evaluating potential ship collision risks grounded in expert judgments, this paper introduces a novel multi-criteria decision-making framework based on Dempster–Shafer evidence theory (DSET). Firstly, a novel index-based assessment system is provided to generate the potential collision risk value of ships based on automatic identification system (AIS) data, which encompasses ship condition parameters, deviation coefficient, and frequency coefficient. Further, to generate the weights of these indicators, a framework to extract expert opinions is provided, which includes two parts: a) converting experts’ opinions into D-S framework through hesitant fuzzy linguistic terms and b) generating experts’ opinions using improved DSET. Regarding part a, a methodology is proposed to evaluate the trustworthiness of experts by considering both the level of uncertainty in their opinions and the degree of similarity between their viewpoints. Concerning part b, strategies are integrated to ascertain the most suitable weight for the basic probability assignment, thereby mitigating conflicts within the evidence. Finally, based on the collected AIS data, the proposed framework has been applied to six of the world's busiest waterways: (1) the Strait of Malacca, (2) the English Channel, (3) the Panama Canal, (4) the Suez Canal, (5) the Danish Straits, and (6) the Strait of Hormuz. Subsequently, comprehensive analytical results and recommendations are furnished, such as wide and smooth channels showing elevated risk.

Barrier Lyapunov function‐based nonlinear cooperative control approach for dual‐quadrotor slung load system

AbstractDue to the limited payload capacity of single‐quadrotor unmanned aerial vehicles (UAVs) in cargo transportation, there is a pressing need for the collaborative transportation of heavy goods using multiple quadrotor UAVs. In this context, the dual‐quadrotor aerial transportation system stands out for its heightened research significance, driven by its stringent requirements for controller swing reduction performance. In practical transportation scenarios, uncertainties such as disturbances and emergency avoidance constraints elevate the risk associated with excessive variations in the relative distances between quadrotor aircraft during collaborative transportation. Building upon the established nonlinear model by D'Alembert, this paper proposes a nonlinear coordinated control strategy based on the barrier Lyapunov function. The strategy employs an innovative energy storage approach, incorporating cooperative error constraints to effectively restrict relative errors between quadrotor aircraft, thereby mitigating the risk of collisions. Furthermore, this control strategy facilitates rapid convergence of the positions of quadrotor aircraft and the swing angles of suspended loads. Theoretical analyses, grounded in LaSalle's invariance theorem, provide evidence of the stability of the closed‐loop system. Finally, comparative assessments conducted using the Matlab/Simulink visualization platform validate the superior control performance of the designed controller in collaborative transportation scenarios.

Initial orbit determination via artificial intelligence for too-short arcs

In the framework of Space Traffic Management (STM) the growing number of space debris and the consequent increased risk of collisions have become a topic of great interest. Moreover, the difficulty of detecting and characterizing a Resident Space Object (RSO) in terms of orbital parameters makes this research field very challenging. In most cases the available observations have a very short duration and an accurate estimation of the orbit is very difficult. This paper introduces the Initial Orbit Determination via Artificial intelligence (IODA) algorithm, a reliable and accurate orbit estimator combining metaheuristic and artificial intelligence frameworks to perform precise orbit determination from Very Short Arcs (VSAs). Starting from an initial orbit estimate derived through an Artificial Bee Colony (ABC) algorithm, a Physics Informed Extreme Learning Machine is employed to increase the accuracy of the predicted orbit. Results on simulated data have shown the robustness of the algorithm to random parameters initialization and a percentage of success of 89% of retrieving the reference orbit with an error under 1 km and 0.01 km/s on the initial target’s state.

A Study on A Novel Collision Risk Prediction Map for Maritime Traffic Surveillance Based on Ship Domain

A Study on A Novel Collision Risk Prediction Map for Maritime Traffic Surveillance Based on Ship Domain

A human-like collision avoidance method for USVs based on deep reinforcement learning and velocity obstacle

Collision avoidance is critical in unmanned surface vehicles (USVs) and is especially challenging in scenarios involving ships of different intelligence levels. To address this problem, inspired by risk analysis and collision avoidance habits of seafarers, this paper proposes a human-like collision avoidance method based on deep reinforcement learning (DRL) and velocity obstacle (VO). It first introduces a navigation impact factor (NIF) calculation module based on fuzzy theory to simulate human beings’ attention mechanisms when facing multiple ships. To be compatible with human beings’ regulations, the convention on the International Regulations for Preventing Collisions at Sea (COLREGs) is first to be integrated into the calculation of the NIF calculation module, and the VO algorithm is incorporated into the reward function, which can reduce collision risks in paths and improve safety requirements. In addition, a series of reward functions are carefully designed to balance safety, smoothness, and driving operation. To validate the performance, experiments are conducted on our virtual simulation platform. The results show that our algorithm can accurately assess the impact of target ships (TS) on own ship (OS) in complex environments and can obey the COLREGs. Collision avoidance can be achieved effectively and the path is smoother.

Leveraging computer vision for predicting collision risks: a cross-sectional analysis of 2019–2021 fatal collisions in the USA

ObjectiveThe USA has higher rates of fatal motor vehicle collisions than most high-income countries. Previous studies examining the role of the built environment were generally limited to small geographic areas...

Revisiting Older Drivers' Risks of At-fault Motor Vehicle Collisions in Japan.

In Japan, older drivers have been encouraged to surrender their driving licenses for traffic safety, despite the potential adverse social and health outcomes of driving cessation. We reconsidered such policies and social pressure by comparing the risk of at-fault motor vehicle collisions (MVCs) across the age groups of drivers. Using the national data of police-reported MVCs that occurred between 2016 and 2020, we examined the number of at-fault MVCs per licensed driver (MVC rate) and the number of fatally and non-fatally injured persons per at-fault MVC by the sex and age groups of at-fault drivers. The MVC rate of older drivers was higher than that of middle-aged drivers but lower than that of young drivers. The number of injured persons among the collided counterparts (collided car occupants, motorcyclists, bicyclists, and pedestrians) per MVC caused by older drivers was not greater than that by drivers in other age groups. In fatal MVCs caused by older drivers, drivers themselves or their passengers tend to be killed rather than their collided counterparts. Overall, the results were mostly consistent between male and female drivers. The risk of at-fault MVCs increased with the advancing age of drivers after middle age; however, this risk among older drivers did not exceed that among young drivers, without posing a high risk of injuries to their collided counterparts.

An innovative inertial parameters identification method for non-cooperative space targets based on electrostatic interaction

Inertial characteristics of non-cooperative targets are crucial for space capture and subsequent on-orbit servicing. Previous methods for identifying inertial parameters involve proximity operations, which are associated with the risk of collision with non-cooperative targets. This paper introduces a long-range, contactless method for identifying the inertial parameters of a non-cooperative target during the pre-capture phase. Specifically, electrostatic interaction is used as an external excitation to alter the target’s motion. A force estimation algorithm that uses measurements from visual and potential sensors is proposed to estimate the electrostatic interaction and eliminate the need for force sensors. Furthermore, a recursive estimation–identification framework is presented to concurrently estimate the coupled motion state, weak electrostatic interaction, and inertial parameters of the target. The simulation results show that the proposed method extends the identification distance to 170 times that of the previous method while maintaining high identification precision for all parameters.

Flight situational assessment based on complex network and MFIM-TOPSIS

In order to objectively and accurately assess the airborne flight posture in the trajectory-based operation (TBO) mode, a flight posture assessment method based on complex network and MFIM-TOPSIS is proposed. Firstly, the precise protection zone of different aircraft types is established by analyzing the risk of lateral collision between aircraft, and the flight conflict network is constructed by using the three-dimensional velocity barrier method, which is close to the TBO air operation environment, based on which six network topology indexes, such as total node degree, average point strength and network density, are selected to build the flight attitude assessment index system. To weaken the influence of the assessment subjectivity, an interaction matrix that can reflect subjective factors-objective factors is proposed. The maximum information coefficient is used to improve the coding of interaction matrix. Finally the flight attitude level status is assessed by using the superiority-disadvantage solution distance method. The simulation analysis and the example analysis of Takasaki Airport show that the evaluation model can accurately assess the air traffic situation and provide auxiliary information for controllers' decision making and command.

Machine learning meets Kepler: inverting Kepler’s equation for All vs All conjunction analysis

The number of satellites in orbit around Earth is increasing rapidly, with the risk of collision rising accordingly. Trends of the global population of satellites need to be analyzed to test the viability and impact of proposed rules and laws affecting the satellite population and collision avoidance strategies. This requires large scale simulations of satellites that are propagated on long timescales to compute the large amounts of actionable close encounters (called conjunctions), which could lead to collisions. Rigorously checking for conjunctions by computing future states of orbits is computationally expensive due to the large amount of objects involved and conjunction filters are thus used to remove non-conjuncting orbit pairs from the list of possible conjunctions. In this work, we explore the possibility of machine learning (ML) based conjunction filters using several algorithms such as eXtreme Gradient Boosting, TabNet and (physics-informed) neural networks and deep operator networks. To show the viability and the potential of ML based filters, these algorithms are trained to predict the future state of orbits. For the physics-informed approaches, multiple partial differential equations are set up using the Kepler equation as a basis. The empirical results demonstrate that physics-informed deep operator networks are capable of predicting the future state of orbits using these equations (RMSE: 0.136) and outperform eXtreme Gradient Boosting (RMSE: 0.568) and TabNet (RMSE: 0.459). We also propose a filter based on the trained deep operator network which is shown to outperforms the filter capability of the commonly used perigee-apogee test and the orbit path filter on a synthetic dataset, while being on average 3.2 times faster to compute than a rigorous conjunction check.

Pavement maintenance and surface treatment: Evaluation and prevention towards road safety aspect along Ampang Jaya road

Abstract The severity of road damage, such as surface cracking or potholes putting drivers of moving vehicles at greater risk of collisions. This study aims to determine and propose the maintenance procedure and surface treatment activity carried out along Jalan Taman Putra, Jalan Bunga Raya and Jalan Perdana Utama in Ampang Jaya, Selangor. From a visual inspection, Jalan Bunga Raya and Jalan Perdana Utama was rated as B and required immediate repairs, while Rating C required to be fixed for user comfort on Jalan Taman Putra. Patch defects account for 45 % and 46 % of all defects on Jalan Taman Putra and Jalan Bunga Raya routes, respectively. Meanwhile, most defects on Jalan Perdana Utama were delamination, accounting for 38 % of all defects. The statistics on the number of road accidents show that pavement restoration work is effective when it results in a decrease in accidents between 2015 and 2021 by 42 % in Jalan Taman Putra, 33 % in Jalan Bunga Raya, and 12 % in Jalan Perdana Utama. Rehabilitating the roads had a favourable impact on Ampang Jaya, Selangor's low traffic accidents rate and raised levels of road safety. To improve road safety features, efficient supervision and maintenance efficiency are required.

Obstacle Avoidance and Path Planning Methods for Autonomous Navigation of Mobile Robot.

Path planning creates the shortest path from the source to the destination based on sensory information obtained from the environment. Within path planning, obstacle avoidance is a crucial task in robotics, as the autonomous operation of robots needs to reach their destination without collisions. Obstacle avoidance algorithms play a key role in robotics and autonomous vehicles. These algorithms enable robots to navigate their environment efficiently, minimizing the risk of collisions and safely avoiding obstacles. This article provides an overview of key obstacle avoidance algorithms, including classic techniques such as the Bug algorithm and Dijkstra's algorithm, and newer developments like genetic algorithms and approaches based on neural networks. It analyzes in detail the advantages, limitations, and application areas of these algorithms and highlights current research directions in obstacle avoidance robotics. This article aims to provide comprehensive insight into the current state and prospects of obstacle avoidance algorithms in robotics applications. It also mentions the use of predictive methods and deep learning strategies.

Simulation study on the damage behavior of tantalum target plates under high-velocity projectile impact

This study aims to explore the damage behavior of tantalum target plates under high-velocity projectile impact, addressing the threats of damage in the aerospace field due to debris and other factors. With the advent of low-cost space launches and large satellite projects, spacecraft such as space stations are facing increasingly severe collision risks. The research utilizes numerical simulation techniques, based on ANSYS/AUTODYN software and the Smoothed Particle Hydrodynamics (SPH) method, to simulate the impact process of projectiles of different velocities (1 ∼ 10 km/s) and materials (aluminum and steel) on tantalum target plates. The results are intended to provide theoretical support for the design of spacecraft protection structures and contribute data support for establishing a millimeter-level space debris impact database.

Enhancing intersection safety in autonomous traffic: A grid-based approach with risk quantification

Existing studies on autonomous intersection management (AIM) primarily focus on traffic efficiency, often overlooking the overall intersection safety, where conflict separation is simplified and traffic conflicts are inadequately assessed. In this paper, we introduce a calculation method for the grid-based Post Encroachment Time (PET) and the total kinetic energy change before and after collisions. The improved grid-based PET metric provides a more accurate estimation of collision probability, and the total kinetic energy change serves as a precise measure of collision severity. Consequently, we establish the Grid-Based Conflict Index (GBCI) to systematically quantify collision risks between vehicles at an autonomous intersection. Then, we propose a traffic-safety-based AIM model aimed at minimizing the weighted sum of total delay and conflict risk at the intersection. This entails the optimization of entry time and trajectory for each vehicle within the intersection, achieving traffic control that prioritizes overall intersection safety. Our results demonstrate that GBCI effectively assesses conflict risks within the intersection, and the proposed AIM model significantly reduces conflict risks between vehicles and enhances traffic safety while ensuring intersection efficiency.

Collision Risk Warning Model for Construction Vehicles Based on YOLO and DeepSORT Algorithms

Collision Risk Warning Model for Construction Vehicles Based on YOLO and DeepSORT Algorithms

Pilot study of a pedestrian collision detection test for a multisite trial of field expansion devices for hemianopia.

Performance-based outcome measures are crucial for clinical trials of field expansion devices. We implemented a test simulating a real-world mobility situation, focusing on detection of a colliding pedestrian among multiple noncolliding pedestrians, suitable for measuring the effects of homonymous hemianopia and assistive devices in clinical trials. In preparation for deploying the test in a multisite clinical trial, we conducted a pilot study to gather preliminary data on blind-side collision detection performance with multiperiscopic peripheral prisms compared with Fresnel peripheral prisms. We tested the hypothesis that detection rates for colliding pedestrians approaching on a 40° bearing angle (close to the highest collision risk when walking) would be higher with 100Δ oblique multiperiscopic (≈42° expansion) than 65Δ oblique Fresnel peripheral prisms (≈32° expansion). Six participants with homonymous hemianopia completed the test with and without each type of prism glasses, after using them in daily mobility for a minimum of 4 weeks. The test, presented as a video on a large screen, simulated walking through a busy shopping mall. Colliding pedestrians approached from the left or the right on a bearing angle of 20 or 40°. Overall, blind-side detection was only 23% without prisms but improved to 73% with prisms. For multiperiscopic prisms, blind-side detection was significantly higher with than without prisms at 40° (88 vs. 0%) and 20° (75 vs. 0%). For Fresnel peripheral prisms, blind-side detection rates were not significantly higher with than without prisms at 40° (38 vs. 0%) but were significantly higher with prisms at 20° (94 vs. 56%). At 40°, detection rates were significantly higher with multiperiscopic than Fresnel prisms (88 vs. 38%). The collision detection test is suitable for evaluating the effects of hemianopia and prism glasses on collision detection, confirming its readiness to serve as the primary outcome measure in the upcoming clinical trial.

Design and application of automatic driving emergency collision avoidance control algorithm based on artificial intelligence technology

The topic of car intelligence has seen a surge in research interest in automobile active collision avoidance systems in recent years. When there is an obstruction in front of the vehicle, active braking or active steering can be used to prevent a collision, which effectively increases driving safety. Research on intelligent automotive active collision avoidance technology is valuable both academically and practically. This paper aims to enhance the adaptability of active collision avoidance systems to various working conditions. To this end, we propose a design scheme that synchronizes the steering and braking collision avoidance strategies. Additionally, we derive a risk assessment model that takes into account both vehicle dynamics instability factors and vehicle collision factors at the same time. This approach effectively addresses the challenge of effectively assessing the risk of self-driving cars in emergency situations and helps to quantify the risk associated with them. Lastly, a simulation and experimental analysis are conducted on the active collision avoidance strategy that is suggested in this study. The findings demonstrate that the active collision avoidance control system for intelligent vehicles, both longitudinal and transverse, developed in this paper can accurately assess collision risk, make decisions, and manage the collision avoidance process. It can also further decrease the frequency of collision accidents.

Safety-Aware Age of Information (S-AoI) for Collision Risk Minimization in Cell-Free mMIMO Platooning Networks

Safety-Aware Age of Information (S-AoI) for Collision Risk Minimization in Cell-Free mMIMO Platooning Networks

Research on Ship Collision Risk Calculation in Port Navigation Waters Based on Ising Model and AIS Data

Research on Ship Collision Risk Calculation in Port Navigation Waters Based on Ising Model and AIS Data

Analysis and Prevention of Road Accidents for Gautam Buddha Nagar District (U.P)

Road accidents pose a significant threat to public safety, necessitating proactive measures for analysis and prevention. This abstract delves into the analysis and prevention of road accidents in the Kasna to Surajpur region over a seven-year period from 2015 to 2021. Through data gathered from multiple police stations, the study aims to identify accident causes, locations, and frequencies to pinpoint blackspots for future road safety audits. Additionally, a novel solution leveraging Pascal's law is proposed to minimize accidents and we can determine the Number of vehicles and Types of vehicles. The system, triggered by vehicle movement, alerts drivers on the opposite side of the road to reduce speed, thus mitigating collision risks. This innovative approach offers promise in enhancing road safety and reducing accidents