Collision Risk Research Articles

Forecasting nocturnal bird migration for dynamic aeroconservation: The value of short‐term datasets

Abstract Placing wind turbines within large migration flyways, such as the North Sea basin, can contribute to the decline of vulnerable migratory bird populations by increasing mortality through collisions. Curtailment of wind turbines limited to short periods with intense migration can minimize these negative impacts, and near‐term bird migration forecasts can inform such decisions. Although near‐term forecasts are usually created with long‐term datasets, the pace of environmental alteration due to wind energy calls for the urgent development of conservation measures that rely on existing data, even when it does not have long temporal coverage. Here, we use 5 years of tracking bird radar data collected off the western Dutch coast, weather and phenological variables to develop seasonal near‐term forecasts of low‐altitude nocturnal bird migration over the southern North Sea. Overall, the models explained 71% of the variance and correctly predicted migration intensity above or below a threshold for intense hourly migration in more than 80% of hours in both seasons. However, the percentage of correctly predicted intense migration hours (top 5% of hours with the most intense migration) was low, likely due to the short‐term dataset and their rare occurrence. We, therefore, advise careful consideration of a curtailment threshold to achieve optimal results. Synthesis and applications: Near‐term forecasts of migration fluxes evaluated against measurements can be used to define curtailment thresholds for offshore wind energy. We show that to minimize collision risk for 50% of migrants, if predicted correctly, curtailments should be applied during 18 h in spring and 26 in autumn in the focal year of model assessments, resulting in an estimated annual wind energy loss of 0.12%. Drawing from the Dutch curtailment framework, which pioneered the ‘international first’ offshore curtailment, we argue that using forecasts developed from limited temporal datasets alongside expert insight and data‐driven policies can expedite conservation efforts in a rapidly changing world. This approach is particularly valuable in light of increasing interannual variability in weather conditions.

Point Process Formulation of Long-Term Collision Risk Statistics from Orbital Fragmentation

This paper focuses on the estimation of statistics (mean and variance) on collision risks induced by on-orbit fragmentation, evaluated over a long horizon of time (several decades). Two major contributions are introduced and can be used independently from one another to address the estimation problem. First, a representation of the cloud of debris with point processes is proposed, focusing on the population statistics rather than individual pieces of debris. It allows for the limited propagation of the cloud statistics required for the computation of the risk statistics, and nothing more, reducing significantly the computational costs when compared to traditional methods propagating individual pieces of debris. Then, a novel risk function is proposed, exploiting the ergodicity of the encounter problem to estimate the number of collisions between a pieces of debris and a target, following a rigorous mathematical construction based on well-defined assumptions. These two methods are then combined and illustrated on a simulated scenario and compared with a traditional Monte Carlo approach. The results show that the point process-based estimate of the risk statistics is consistent with the Monte Carlo reference, while being faster to run by at least two orders of magnitude.

Parameter optimization of vibration control system for adjacent building structures based on negative stiffness inerter damper

Building structures are subjected to strong earthquakes, which result in lateral collisions between them. Such collisions often cause severe structural damage and exacerbate the seismic hazard risk of building structures during earthquake events. This paper discusses the application of vibration control devices based on negative stiffness inerter damper in single-story adjacent building structures. The dynamic equations of the vibration control system containing different types of negative stiffness inerter damper under seismic excitation are established as a unified model. The H2 norm theory and Monte Carlo pattern search method are used to optimize the design parameters to improve the vibration control performance of the system, and the dynamic characteristics of the system are investigated. The results demonstrate that attaching negative stiffness inerter damper to adjacent building structures can effectively improve the overall seismic capacity reserve of the building and reduce the risk of collision of adjacent building structures; improve the robustness and stability of the system, and better reduce the displacement response of the building structure under seismic excitation. In addition, the potential of NSID-based vibration control devices to convert seismic energy into usable electricity has been investigated.

Perceptions on the distribution, threats, strandings, and conservation of the Antillean manatee (Trichechus manatus manatus) in the Río Dulce National Park, Izabal, Guatemala

The Antillean manatee in Guatemala faces multiple threats resulting from habitat loss and human interactions such as motorboat collisions and poaching. While some conservation efforts have been initiated in Río Dulce National Park (RDNP), there is a lack of studies examining the current perceptions of threats to the species. This knowledge gap may hinder the development of effective conservation strategies that consider both the species and local communities. Our objective was to analyze the perceptions of key local stakeholders concerning the Antillean manatee, including its distribution, threats, attention to strandings, and the current status of conservation in the protected area of RDNP. From July to October 2019, we conducted 50 semi-structured interviews with technicians and river users, such as fishermen and boat captains. Additionally, we held two workshops: one aimed at developing a participatory mapping exercise and another to validate the information. Each workshop was divided into two groups: one for technicians and another for river users. A total of 68 people participated in the workshops, the majority of respondents were male (n = 47), ranging in age from 19 to 71, and predominantly local to the area. Their activities in the river included fishing, biological monitoring, transportation, and providing tourism services. Perceived threats to the Antillean manatee included motorboat collisions, poaching, bycatch, habitat degradation, and water pollution. The focus groups also identified key observations, such as mother-calf sightings, specific areas of risk for manatee-boat collisions, areas with a higher presence of aquatic vegetation, and additional threats such as habitat degradation and poaching. Our results demonstrate that stakeholders possess a fundamental understanding of the manatee, its threats, and its distribution. They also highlight a noticeable lack of knowledge, specifically regarding the attention given to manatee strandings. It is recommended that conservation strategies include environmental education adapted to local conditions, as well as the promotion of manatee studies and their dissemination at the local level, including attention to strandings in the area.

Time-Delay following Model for Connected and Automated Vehicles with Collision Conflicts and Forced Deceleration

The connected and automated car-following model can provide a model reference for the queue control algorithm of connected and automated driving and has become a hot research topic in the field of connected vehicles and intelligent transportation. A queue of fast-moving vehicles on urban roads can cause traffic congestion when forced to slow down and, in serious cases, can cause rear-impact accidents. Therefore, this paper introduces information on the time delay of information reception and processing, a collision risk quantification factor reflecting the speed characteristics of the front vehicle, and the speed limit and proposes an improved intelligent driver collision quantification model that considers drastic changes in the speed of the front vehicle. Additionally, the model parameters are calibrated using real vehicle data from urban roads combined with an improved salp swarm algorithm. Finally, the evolution rule of disturbance in the traffic flow under different states is analyzed using a time-space diagram, and the DIDM-CSCL model is compared with the classical IDM. The results show that the improved IDM can better describe the following behavior at the microscopic level, which provides a basis for research related to connected and automated driving.

Side Collision Detection Model for Visually Impaired Using Monocular Object-specific Distance Estimation and Multimodal Real-World Location Calculation

Targeting the potential risk of side-vehicle collisions when the visually impaired crosses roads, this study proposed a side collision detection model, including monocular distance estimation, multimodal real-world location estimation, future location prediction and collision warning strategies tailored for visually impaired pedestrians. The proposed model employs YOLOv8 and DeepSort for vehicle detection and tracking, utilizing shallow neural networks for distance estimation based on image information and vehicle position data. Predicted vehicle distances are combined with magnetic field sensor and GPS data to compute and store real-world vehicle locations, and these location data will be used for linear regression to forecast future locations. A warning strategy is then implemented to alert users. Experimental validation shows that the monocular distance estimation network has an Absolute Relative Error of 0.043 and an ALE (Average Localization Error) of 1.249m. In real-world location estimation, the view angle ALE is 0.019, and the location ALE is 1.778m. Regarding location prediction, the accuracy in distinguishing stationary and moving vehicles reaches 0.962, and the predicted curve, based on ground truth and predicted locations, exhibits good alignment. The proposed warning strategy, evaluated on Kitti Tracking Dataset and a self-created dataset, accurately detects the majority of potential collision risks.

Investigation and analysis of ship to platform collision incidents on the UK continental shelf: highlighting trends between the enforcement of offshore regulations and the occurrence of vessel to platform collision incidents

An offshore installation is exposed to ship collision risk from in-field and passing vessels. Both categories of collision have occurred on the United Kingdom Continental Shelf (UKCS), and both have the potential to result in catastrophic damage to the installation, although to date only significant consequences have been observed in UK waters. World-wide, catastrophic collisions with installations have occurred resulting in severe damage to vessels and installations, leading to loss of life and environmental damage. This led to the authors investigate the occurrence and reporting of ship to platform collisions on the UKCS 1971–2017. It was found that there were periodic fluctuations in the occurrence of collision accidents. This research analyses the accident trends in order to determine the whether the fluctuations were a regular occurrence and, if so, what was the cause. 511 ship to platform collision incidents between 1971 and 2017 were subject to analysis, in terms of damage classification, the vessel type, month of occurrence and a comparison of the release of regulations with the fluctuation of incidents. Subsequently, this analysis has identified a key trend between the reporting of offshore collision incidents and the release and enforcement of offshore Safety Case regulations.

A powered descent trajectory planning method with quantitative consideration of safe distance to obstacle

High scientific value areas on celestial bodies such as the Moon and Mars are often located in hazardous terrains. To achieve safe landing exploration, a novel planning method is proposed, which can ensure that the planned trajectory maintains a user-specified distance from obstacles, thus reducing potential collision risk induced by factors such as the body size and model uncertainties. Firstly, the basic model of the trajectory optimization problem and its convexification version is given. The obstacles are modeled as polynomial functions, based on which the “if-then” obstacle avoidance logic explicitly considering the safe distance, is described as a sum of squares constraint. This constraint formulation applies to any obstacle described by a finite number of polynomials, independent of the specific expression of the polynomials (reflecting the shape of the obstacle). Subsequently, the convexification process for the obstacle avoidance constraint is given. Finally, the sequential sum of squares programming problem for the obstacle avoidance trajectory is established, which boils down to a series of semidefinite programming problems. Simulation results show that the closest distance between the planned trajectory and obstacles strictly satisfies the specified distance constraint, and the trajectory could avoid non-convex obstacles. With the promising convergence properties of underlying convex optimization algorithms, advanced autonomous obstacle avoidance guidance schemes are expected to be formed based on the proposed trajectory planning method.

Real-Time 3D Routing Optimization for Unmanned Aerial Vehicle using Machine Learning

In the realm of Unmanned Aerial Vehicles (UAVs) for civilian applications, the surge in demand has underscored the need for sophisticated technologies. The integration of Unmanned Aerial Systems (UAS) with Artificial Intelligence (AI) has become paramount to address challenges in urban environments, particularly those involving obstacle collision risks. These UAVs are equipped with advanced sensor arrays, incorporating LiDAR and computer vision technologies. The AI algorithm undergoes comprehensive training on an embedded machine, fostering the development of a robust spatial perception model. This model enables the UAV to interpret and navigate through the intricate urban landscape with a human-like understanding of its surroundings. During mission execution, the AI-driven perception system detects and localizes objects, ensuring real-time awareness. This study proposes an innovative real-time three-dimensional (3D) path planner designed to optimize UAV trajectories through obstacle-laden environments. The path planner leverages a heuristic A* algorithm, a widely recognized search algorithm in artificial intelligence. A distinguishing feature of this proposed path planner is its ability to operate without the need to store frontier nodes in memory, diverging from conventional A* implementations. Instead, it relies on relative object positions obtained from the perception system, employing advanced techniques in simultaneous localization and mapping (SLAM). This approach ensures the generation of collision-free paths, enhancing the UAV's navigational efficiency. Moreover, the proposed path planner undergoes rigorous validation through Software-In-The-Loop (SITL) simulations in constrained environments, leveraging high-fidelity UAV dynamics models. Preliminary real flight tests are conducted to assess the real-world applicability of the system, considering factors such as wind disturbances and dynamic obstacles. The results showcase the path planner's effectiveness in providing swift and accurate guidance, thereby establishing its viability for real-time UAV missions in complex urban scenarios.

Affine formation maneuver control of underactuated surface vessels: Guaranteed safety under moving obstacles in narrow channels

This research tackles the challenge of dynamic formation control and collision avoidance in underactuated surface vessels (USVs). Firstly, an affine transformation is employed to implement arbitrary configuration transformations within the multi-USV system. Secondly, by considering both distances and velocities between USVs and obstacles, a novel collision avoidance scheme is proposed based on conventional distance-based artificial potential field methods. This method, building on traditional distance-based artificial potential fields, offers a more precise assessment of collision risks, thereby notably shrinking the potential collision zones. Such refinement leads to reduced energy consumption and minimizes disruption to formation integrity. Thirdly, an anti-perturbation formation maneuver safety control scheme is introduced to achieve a time-varying target formation without collisions, ensuring that local position tracking errors remain bounded. The effectiveness of the proposed approaches is substantiated through comprehensive theoretical analysis and extensive numerical simulations, underscoring their practical applicability.

Density-based in-orbit collision risk model valid for any impact geometry

Neglecting small fragments in space debris evolutionary models can lead to a significant underestimation of the collision risk for operational satellites. However, when scaling down to the millimetre range, the debris population grows to over a hundred million objects, making deterministic approaches too computationally expensive. On the contrary, probabilistic models provide a more efficient alternative, which however typically works under some simplifying assumptions on the dynamics, limiting their field of applicability. This work proposes an extension of the density-based collision risk models to any orbital dynamics and impact geometry. The impact rate with a target satellite is derived from a multi-dimensional phase space density function in orbital elements, which discretely varies over both phase space and time. The assumption of a bin-wise constant cloud density allows for the analytical transformation of the six-dimensional distribution in orbital elements into the three-dimensional spatial density function, guaranteeing an efficient and accurate evaluation of the fragments flux. The proposed method is applied to the assessment of the collision risk posed by occurred fragmentation events in different orbital regions on a high-risk target object. The effect on the impact rate of the modelling improvements, compared to previous probabilistic formulations, is discussed.

Multiple targets traversing for unmanned surface vehicles by bundled genetic optimization and fast-marching Q-Learning

This paper discusses the use of unmanned surface vehicles (USVs) in the ocean for traversing multiple targets and proposes a novel multiple targets traversing algorithm that takes into account the multi-level priority of target assignment and path optimization. The algorithm includes a bundled genetic optimization target assignment method and a fast marching-Q-Learning path planning method. Comparative studies with existing methods show that the proposed algorithm have efficient performance, where the calculation time for target assignment has been reduced by 45.64%, and the path length has been further optimized by 15.3%. Considering the velocities of USVs navigation, path turning motion and multi-level priorities constraints are satisfied, and collision risk is avoided in dynamic ocean environment. Hence, the proposed multiple targets traversing algorithm plays an important role in the ocean engineering field of USVs.

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.

Generation of initial debris cloud distributions for breakup events based on CARDC-SBM

With the dramatic increase in the number of resident space objects, the risk of collision between space objects and debris is growing. To protect spacecraft from such collisions, it is crucial to determine the generation patterns of space debris and analyze the evolution of debris clouds. In this paper, a continuous distribution formula is derived for each parameter of the breakup debris based on the China Aerodynamics Research and Development Center spacecraft breakup model (CARDC-SBM). Using CARDC-SBM, we theoretically establish a normal distribution for the separation velocity of the breakup debris in relation to the breakup mass center. Based on this foundation, the mean and standard deviation of the debris separation velocity distribution are empirically modeled using polynomial and exponential functions. Consequently, an empirical formula with a broad range of applicability is derived, which significantly reduces the computation time of debris cloud simulations. The initial velocity distribution given by this empirical formula is employed as the initial condition for the evolution of debris clouds from the perspective of a boundary value problem. Simulations using CARDC-SBM and subsequent analysis of the debris cloud evolution resulting from a breakup event reveal that the distribution of each parameter is determined solely by the relative collision velocity and the spacecraft's nominal density in the impact breakup scenario. Our results show that the empirical formula for the debris cloud distribution provides good agreement with Monte Carlo simulations.

Occlusion-aware collision avoidance trajectory planning with potential collision risk assessment for autonomous vehicle

Motion planning for autonomous vehicles relies heavily on perception and prediction results to find a safe, collision-free local trajectory that adheres to traffic rules. However, vehicle perception is frequently limited by occlusion, and the generation of safe local trajectories with restricted perception is a significant challenge in the field of motion planning. This paper introduces a collision avoidance trajectory planning algorithm that considers potential collision risks, within a hierarchical framework of sampling and optimization. The primary objective of this work is to generate trajectories that are safer and align better with human driver behavior while considering potential collision risks in occluded regions. Specifically, in occlusion scenarios, the state space is discretized, and a dynamic programming algorithm is used for a sampling-based search to obtain initial trajectories. Additionally, the concept of a driving risk field is introduced to describe potential collision risk elements within the human-vehicle-road environment. By drawing inspiration from graph search algorithms, potential collision risk areas are accurately described, and a cost function is proposed for evaluating potential risks in occluded regions. Drivers typically exhibit conservative and cautious driving behavior when navigating through occluded regions. The proposed algorithm not only prioritizes driving safety but also considers driving efficiency, thereby reducing the vehicle's conservativeness when passing through occlusions. The research results demonstrate that the ego vehicle can actively avoidblind spots and tends to move away from occluded regions, aligning more closely with human driver behavior.

Adaptive cruise control design for collision risk avoidance

With advanced technologies such as perception and automatic control, automated vehicles (AVs) are gradually replacing human-driven vehicles (HDVs). They will become a significant component of transportation systems. As the crucial longitudinal control model for AVs, the existing adaptive cruise control (ACC) model faces challenges such as poor linear stability and compromised safety. To solve the problem, this paper proposes an adaptive cruise control model to avoid collision risk. First, the delay-considerate collision risk control strategy (DCRCS) is developed to consider vehicles' perception and control delay. Then, a proposed adaptive cruise control (PACC) model is designed by incorporating DCRCS into the ACC model based on the control gain coefficient. Finally, the effectiveness of the PACC model is discussed and analyzed in terms of stability, safety, efficiency, energy consumption, and pollutant emissions through a combined approach of theoretical analysis and simulation experiments. The result shows that (1) compared to the classical ACC model, the PACC model demonstrates significant improvements in system stability, reduced safety risks, enhanced efficiency, and decreased energy consumption and pollutant emissions. These enhancements are observed when the control gain coefficient is set to a positive value. (2) The equilibrium speed minimally influences the effectiveness of the PACC model. Under varying speed conditions, the PACC model consistently maintains stable performance. (3) The effectiveness of the PACC model is closely related to the selection of the control gain coefficient. Considering the effects of the control gain coefficient on stability, safety, efficiency, energy consumption, and pollutant emissions of the PACC model, the optimal range for the control gain coefficient is in the range of [0.8, 1.0]. In summary, compared to the classical ACC model, the PACC model exhibits outstanding performance in various aspects. It provides theoretical support for the longitudinal control of AVs in the future.

Preliminary Safety Analysis of Megaconstellations in Low Earth Orbit: Assessing Short-Term and Long-Term Collision Risks

The deployment of megaconstellations in low Earth orbit (LEO) poses significant collision risks with space debris. This paper focuses on analyzing the short-term and long-term collision probabilities of megaconstellations to assess their collision risk. Firstly, a short-term collision risk evolution model is developed to accurately address rendezvous collisions. Secondly, a long-term collision risk evolution model is established by considering space object density, space debris attenuation, space target disintegration, and the distribution of disintegration targets. Through simulations conducted on the Starlink Phase I constellation, the results demonstrate a 30–40% increase in short-term collision probability within the constellation shell, a 70.2% probability of at least one collision during the constellation’s lifetime, and a 25.3% increase in secondary collisions following a collision event. This study provides a reference and application for analyzing the orbital safety of LEO megaconstellations and for promoting the sustainable development and utilization of space resources.

Choosing the profile of an inclined – directional well in the Caspian Sea area of Turkmenistan

This paper aims to design a profile for a directional and production-appraisal well, specifically Well No. 707 in the West Cheleken field, situated in the Caspian Sea coastal water area. In the context of rapid changes in the energy sector, this study holds significance, offering economic benefits and contributing significantly to the energy security of the region and the global energy market. The main objective of this study is to optimise directional drilling for exploration wells in the Caspian Sea area of Turkmenistan, specifically focusing on improving efficiency and reducing environmental impact. To achieve this research objective, the author proposes designing a profile for a directional and production-appraisal well, utilising materials from geophysical surveys of previously drilled wells. This approach aims to avoid accidents and complications during the drilling process. Both the Close Approach software and safety regulations in the oil and gas production industry are employed to prevent collisions of wellbores. In conclusion, this research contributes innovative solutions to optimise directional drilling processes for exploration wells. It addresses potential complications in open wells and, importantly, mitigates the risk of wellbore collisions. Keywords: wellbore collision; complication; stabilization; accident; Apsheron; Akchagil; curvature; displacement.

Analysis and Simulation of Lateral Collision Risk under Paired Approach

The paired approach can improve the efficiency of closely spaced parallel runways. Calculating the probability and frequency of horizontal overlap is an indispensable step when evaluating the horizontal collision risk of the paired approach. As the generation of horizontal overlap probability is closely related to horizontal position error, we propose a calculation method of horizontal overlap probability based on position error from the perspective of pilot operation. First, according to the principle of flight mechanics, the attitude adjustment model is established for the horizontal direction of the approach process, and the pilot’s operation model for various position errors is based on the concept of the stochastic process. This attitude adjustment model can replicates the process of the pilot operating the steering column to change the aircraft’s attitude. When combined with the pilot’s operation model, it is possible to simulate the position errors generated during the approach process. Building on this, the horizontal overlapping conditions of two aircraft are analyzed to simulate the horizontal overlap process in the paired approach. The duration and instances of overlap counted and the ratio between these results and the total running time give the overlap probability and frequency. Multiple simulations in MATLAB reveal that higher pilot operating accuracy shortens the time for the aircraft to align with the course, whereas lower accuracy leads to unstable horizontal position errors. Furthermore, the horizontal overlap in paired approaches primarily occurs at the beginning of the procedure, and enhancing the pilot’s operating accuracy does not significantly affect the probability and frequency of horizontal overlap.

A reinforcement learning enhanced pseudo-inverse approach to self-collision avoidance of redundant robots.

Redundant robots offer greater flexibility compared to non-redundant ones but are susceptible to increased collision risks when the end-effector approaches the robot's own links. Redundant degrees of freedom (DoFs) present an opportunity for collision avoidance; however, selecting an appropriate inverse kinematics (IK) solution remains challenging due to the infinite possible solutions. This study proposes a reinforcement learning (RL) enhanced pseudo-inverse approach to address self-collision avoidance in redundant robots. The RL agent is integrated into the redundancy resolution process of a pseudo-inverse method to determine a suitable IK solution for avoiding self-collisions during task execution. Additionally, an improved replay buffer is implemented to enhance the performance of the RL algorithm. Simulations and experiments validate the effectiveness of the proposed method in reducing the risk of self-collision in redundant robots. The RL enhanced pseudo-inverse approach presented in this study demonstrates promising results in mitigating self-collision risks in redundant robots, highlighting its potential for enhancing safety and performance in robotic systems.