Collision Probability Research Articles

Examining the relationship between wind speed, climatic conditions, and road accidents in Iran

This study investigates the relationship between wind speed, climatic conditions, and road accidents in Iran, focusing on the type of accidents and collisions. The research aims to identify the causes of accidents and provide insights for prediction and decision-making purposes. The study adopts a developmental research approach, analyzing road accident data and wind speed data. Logistic regression is employed to investigate the correlation between wind speed and the type of accidents and collisions. Data mining techniques, specifically the J48 decision tree algorithm, are used to examine the relationship patterns among wind speed, climatic conditions, collision types, and accident types. Additionally, texts and articles related to atmospheric hazards and road accidents are studied, and interviews are conducted with road accident experts and drivers to extract insights into the causes of road accidents in Iran. The findings indicate that wind speed does not have a direct and significant effect on the type of accidents (fatal or non-fatal), but it does influence the type of collisions in road accidents. The decision tree analysis reveals patterns in the relationships between weather conditions, wind speed, collision types, and accident types, enabling the prediction of collision probabilities in different scenarios. The causes of road accidents in Iran are categorized into human factors, secondary causes, and unique causes. Based on the findings, several recommendations are proposed to enhance road safety and reduce accidents in Iran.

Numerical simulation of ash deposition behavior in radiant syngas cooler coupled with impact surface temperature

Radiant syngas cooler (RSC) is a crucial heat recovery device for entrained-flow coal gasification technology. However, ash deposition in an RSC can severely affect operating efficiency and safety. An ash deposition model coupled with the impact surface temperature was established based on the ash particle deposition morphology. The effects of different operating conditions on ash deposition characteristics are discussed. The simulated data agreed well with the industrial data. The results indicate that ash particles were primarily deposited in the collision deposition zone. The maximum deposition thickness of radiation screen and cylinder membrane wall is 9.82 mm and 1.88 mm, respectively. The ash deposition hindered heat transfer process and the highest heat flux was observed in the middle part of the RSC. Ash deposition around 5.3 m increases significantly with an increase in the inlet syngas temperature. Increasing the load increased probability of ash particle collision and the area of influence.

Intrusion Detection in Wireless Sensor Networks Based on IPSO-SVM Algorithm

To optimize node energy consumption and improve its security, this paper uses the DEEC algorithm to layer WSN and reduce the probability of channel information collision and uses the weighted probability of cluster head election to optimize node energy expenditure, so that WSN can obtain a longer lifecycle. Improved Particle Swarm Optimization-based Support Vector Machine (IPSO-SVM) algorithm is used for intrusion detection and experimental testing in WSN. The results showed that the IPSO-SVM algorithm exhibited good convergence, with a convergence step size of 5 steps, which converged earlier than the Support Vector Machine Algorithm based on Particle Swarm Optimization (PSO-SVM), which had a convergence step size of 10 steps. The IPSO-SVM algorithm performed best in WSN intrusion detection, with the highest detection rate of 96.20% in Probe attack data detection, which was 0.80% higher than the Support Vector Machine Algorithm based on Genetic Algorithm (GA-SVM). The PSO-SVM algorithm had the lowest detection rate of 95.20%. The IPSO-SVM algorithm had a minimum false positive rate of 1.54% in Dos attack data detection. In terms of average training time, the IPSO-SVM algorithm had a minimum average training time of 323.45 seconds. Compared to the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm, the Distributed Energy Efficient Clustering (DEEC) algorithm performs better, has less energy consumption, and retains more nodes. The method adopted in this study can make WSN have a longer life cycle and ensure its security.

Confidence based compressed sensing approach with QoS differentiation for reliable grant-free access

With the rapid development of 5G technology, numerous intelligent terminals can be successively connected to a network. This brings significant challenges to existing network access because it increases the collision probability. It is essential for Grant-free random access (GF-RA) to ensure low delay and reliable transmission. Due to multiuser interference and varying environments, user detection can be complicated, which leads to unreliable transmission. To address the limitations above, research on the GF-RA method based on compressed sensing is carried out and it includes the following: (1) The confidence-weighted orthogonal matching pursuit method is used for estimating the number of active users and overcomes the constraint of sparse condition. (2) The confidence-weighted simultaneous hard thresholding (SHT) method is used for active user detection (AUD), which can improve the performance of the SHT algorithm. (3) We conduct collision probability analysis to not only provide the performance upper bound using the multiple preambles method but also infer the collision probability relationship with different priorities. This is helpful to realize diverse quality of service requirements. The numerical results prove that the proposed method is valid for non sparse signals and can realize a high success access rate.

Hybrid Method of Uncertainty Propagation for Near-Earth Conjunction Analysis

In this research, several existing semi-analytical dynamics and uncertainty propagation techniques are combined with conjunction models to achieve fast and accurate probability of collision results. Initial Gaussian uncertainty associated with two objects is split into smaller Gaussian distributions using Gaussian mixture models to achieve mixture components that will maintain linearity over longer propagation times. These mixture components are propagated forward using second-order state transition tensors that can capture the nonlinearity in the propagation accurately by taking into account the desired dynamics. The dynamic solution and these tensors are computed using the Deprit–Lie averaging approach, including transformations between mean and osculating states, which accounts for perturbations due to solar radiation pressure and J2. This simplified dynamic system allows fast and accurate propagation by combining the speed of propagation with averaged dynamics and the accuracy of short-period variation addition. Combined, these mathematical tools are used to propagate the object’s uncertainties forward. The final distributions are compared using analytical conjunction methods to compute the probability of collision, which is then compared to the Monte Carlo result to confirm the method’s validity.

Synthetic orbit uncertainty generation through regression analysis of historical Conjunction Data Messages

In the last decades, the Earth-orbiting population of both active and non-active objects has grown significantly, leading to a substantial increase in number of possible in-orbit collisions. It is therefore crucial to monitor the orbit of space resident objects to assess in advance the threat of risky conjunctions. Within this framework, the 18th Space Defense Squadron (SDS) is consistently updating the orbit of thousands of tracked objects by processing observations of the U.S. Space Surveillance Network (SSN). The determined orbital data is continuously maintained in the Special Perturbation (SP) catalogue and used by the 19th SDS to issue close approach warnings to satellite operators around the globe in the form of Conjunction Data Messages (CDM). The Flight Dynamics (FD) group of the German Space Operation Centre (GSOC) receives on regular basis a subset of the SP catalogue data along with CDMs associated to the fleet of its controlled satellites. The SP ephemerides are in fact provided without any covariance information preventing any computation of the Probability of Collision (Pc). In GSOC FD we are implementing a service to link a series of synthetic orbital error covariance matrices to a given SP ephemeris by statistically analyzing historical CDMs of past events. More than 30 GB of past conjunction data are processed to extract state vector, covariance matrix and object size parameter of already encountered secondary objects. The orbital errors of these last are subsequently categorized and divided into orbital classes to decouple the high correlation the covariance has with respect to solar flux, object dimension, altitude of perigee, eccentricity and orbit inclination. The classification aims at collecting similar CDMs regarding the aforementioned dependencies, and approximates the predicted 1-sigma position errors in the orbital frame by optimal curve-fitting techniques. By evaluation of the curve fitting coefficients of a requested orbit class a covariance matrix can be generated for any prediction time in upcoming CDM refinements and other analyses. The work discusses the limiting cases of the classification approach, bringing possible solutions to the scenario of empty classes. An in-depth characterization of the parameters that affect the orbital errors is in fact performed to individualize the neighboring class that provides the closest and most meaningful covariance timeline. Successively, the effect of using synthetic covariance in a conjunction risk assessment is also explored, adapting the problem on real operations. Lastly, the entire data processing pipeline and how the described service fits into the GSOC Flight Dynamics System (FDS) framework is described.

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.

Bimodal Distribution–Based Collision Probability of Ship with Buoy in Two-Way Navigable Channel

Bimodal Distribution–Based Collision Probability of Ship with Buoy in Two-Way Navigable Channel

Study of the Accuracy of the Ronen Method at Material Interfaces

The Ronen Method (RM) is a nonlinear iterative scheme that demands successive resolutions of the diffusion equation, where local diffusion constants are modified to reproduce more accurate estimates of the neutron currents by a transport operator. The methodology is currently formulated using the formalism of the collision probability method for evaluation of the current. The RM was recently tested on a complete suite of one-dimensional (1-D) multigroup benchmark problems. Small differences in the flux (less than 2%) were reported at material interfaces and close to the vacuum boundary with respect to the reference solution from transport. This work investigates first a possible numerical equivalence between transport and diffusion in some representative 1-D problems from the same benchmark test suite. The equivalence is sought with optimal diffusion coefficients computed using reference transport solutions that allow for adjusting the diffusion model. The RM, which attempts to obtain such equivalent diffusion coefficients without knowing the reference solution, is then compared to the optimal coefficients. The accuracy of the flux distribution at material interfaces is investigated for different approximations of the vacuum boundary and by decreasing progressively the RM convergence tolerance set in the iterative scheme. Using tighter convergence criteria, the RM calculates more accurate flux distributions at all material interfaces, regardless of the value of the diffusion coefficient and the extrapolated distance set at the beginning of the iterative scheme. Maximum flux deviations are remarkably reduced when the RM convergence tolerance is set to eight or more significant digits, leading to improvements in the flux deviation of two orders in magnitude and providing numerical proof for equivalence with transport in the tested configurations.

Probabilistic prediction models of pounding between adjacent buildings during seismic

In recent years, many researchers have studied the probability of collisions between adjacent buildings under earthquake action and proposed some prediction methods, but their accuracy still needs to be improved. This study adopts a machine learning method based on logistic regression models to propose a probability prediction model for adjacent building collisions during earthquake processes. The main content of this study includes: 1. A logistic regression model was used to analysis the factors affecting the seismic impact of adjacent buildings, and an impact prediction model was established; 2. Verify the effectiveness of the logistic regression model through comparative probability analysis.

Gap in drop collision rate between diffusive and inertial regimes explains the stability of fogs and non-precipitating clouds

Rain drops form in clouds by collision of submillimetric droplets falling under gravity: larger drops fall faster than smaller ones and collect them on their path. The puzzling stability of fogs and non-precipitating warm clouds with respect to this avalanche mechanism has been a longstanding problem. How can droplets of diameter around 10 $\mathrm {\mu }$ m have a low collision probability, inhibiting the cascade towards larger and larger drops? Here we review the dynamical mechanisms that have been proposed in the literature and quantitatively investigate the frequency of drop collisions induced by Brownian diffusion, electrostatics and gravity, using an open-source Monte Carlo code taking all of them into account. Inertia dominates over aerodynamic forces for large drops, when the Stokes number is larger than $1$ . Thermal diffusion dominates over aerodynamic forces for small drops, when the Péclet number is smaller than $1$ . We show that there exists a range of size (typically 3–30 $\mathrm {\mu }$ m for water drops in air) where neither inertia nor Brownian diffusion are significant, leading to a gap in the collision rate. The effect is particularly important, due to the lubrication film forming between the drops immediately before collision, and secondarily to the long-range aerodynamic interaction. Two different mechanisms regularise the divergence of the lubrication force at vanishing separation: the transition to a non-continuum regime in the lubrication film, when the separation is comparable to the mean free path of air, and the induction of a flow inside the drops due to shear at their surfaces. In the gap between inertia-dominated and diffusion-dominated regimes, dipole–dipole electrostatic interactions becomes the major effect controlling the efficiency of drop collisions.

Fast and Reliable Computation of Instantaneous Orbital Collision Probability

Because of the increasing number of objects in low Earth orbit, the fast and reliable estimation of the collision risk is an important challenge for spacecraft owners/operators. Among the available risk indicators, this paper focuses on computing the instantaneous probability of collision, which can be modeled as the integral of a three-dimensional Gaussian probability density function over a Euclidean ball. The authors propose an efficient and accurate method for evaluating this integral. It is based on the combination of two complementary strategies. For the first one, convergent series and numerical error bounds are computed. These bounds provide a tradeoff between the accuracy needed and the number of terms to compute. The second one, using divergent series, approximates the value of the integral with a good accuracy in most cases with only a few terms computed. Based on those two methods, a hybrid algorithm is tested on cases borrowed from the literature and compared against existing methods. Several numerical results and comparisons confirm both the efficiency and robustness of this paper’s approach.

Simulation of the mixing component of a horizontal air-assisted centralised wheat metering device

A horizontal air-assisted centralised metering device involves the use of conveying airflow for mixing and conveying during the seeding process, making it suitable for high-speed precision seeding. This study identified key structural parameters for the mixing component. Computational fluid dynamics (CFD) simulations were used to analyse the influence of the key structural parameters on airflow distribution and airflow velocity. The results suggested selecting a contraction section conical angle of 40° with maximal conveying airflow velocity and minimal airflow pressure loss. A mixing chamber length of 25 mm prevented the retention and blockage. An expansion section conical angle of 20° achieved higher airflow velocity within the expansion section. CFD-DEM (Discrete Element Modelling) coupled simulation were used to analyse the influence of mixing chamber height, seeding rate, and conveying airflow velocity on seed conveying performance. The results indicated that a mixing chamber height of 16 mm ensured stable seed acceleration, reduced the probability of seed-wall collisions. Seed collisions within the mixing chamber and expansion section increased noticeably along with the rising seeding rates. While airflow velocity in the range of 16–25 m s−1 facilitated timely seed conveyance and reduced seed-wall collisions. Verification experiments for the optimal parameter combination of the mixing component indicated that a conveying airflow velocity of 22 m s−1 resulted in the stability coefficient of variation of total seeding mass not exceeding 1.04 %, the uniformity coefficient of variation of seeding mass in each row not exceeding 3.61 %. This research offers valuable insights for structural improvements in the mixing component of the horizontal air-assisted metering device.

Novel Probabilistic Collision Detection for Manipulator Motion Planning Using HNSW

Collision detection is very important for robot motion planning. The existing accurate collision detection algorithms regard the evaluation of each node as a discrete event, ignoring the correlation between nodes, resulting in low efficiency. In this paper, we propose a novel approach that transforms collision detection into a binary classification problem. In particular, the proposed method searches the k-nearest neighbor (KNN) of the new node and estimates its collision probability by the prior node. We perform the hierarchical navigable small world (HNSW) method to query the nearest neighbor data and store the detected nodes to build the database incrementally. In addition, this research develops a KNN query technique tailored for linear data, incorporating threshold segmentation to facilitate collision detection along continuous paths. Moreover, it refines the distance function of the collision classifier to enhance the precision of probability estimations. Simulation results demonstrate the effectiveness of the proposed method.

Recent collisional history of (65803) Didymos

The Double Asteroid Redirection Test (DART, NASA) spacecraft revealed that the primary of the (65803) Didymos near-Earth asteroid (NEA) binary system is not exactly the expected spinning top shape observed for other km-size asteroids. Ground based radar observations predicted that such shape was compatible with the uncertainty along the direction of the asteroid spin axis. Indeed, Didymos shows crater and landslide features, and evidence for boulder motion at low equatorial latitudes. Altogether, the primary seems to have undergone sudden structural failure in its recent history, which may even result in the formation of the secondary. The high eccentricity of Didymos sets its aphelion distance inside the inner main belt, where it spends more than 1/3 of its orbital period and it may undergo many more collisions than in the NEA region. In this work, we investigate the collisional environment of this asteroid and estimate the probability of collision with multi-size potential impactors. We analyze the possibility that such impacts produced the surface features observed on Didymos by comparing collisional intervals with estimated times for surface destabilization by the Yarkovsky-O’Keefe-Radzievskii-Paddack (YORP) effect. We find that collisional effects dominate over potential local or global deformation due to YORP spin up.

Innovative launch collision avoidance (LCOLA) tool prioritizing accuracy, launch access and efficiency

A new Launch Collision Avoidance (LCOLA) analysis capability is presented. The method represents the combination of the patented algorithm developed by Analytical Graphics Inc. and implemented in its Launch Window Analysis (LWA) tool, together with a new LCOLA collision probability algorithm developed by COMSPOC Corporation to maximize launch window using responsive, comprehensive, and efficient LCOLA closure windows. The new tool works by assessing the underlying topography of the launch closure problem to identify and characterize all launch holds within the user-specified launch window rapidly and accurately without confining the launch operator to “top-of-the-minute” or other such constructs.This new LCOLA capability avoids the pitfalls of the typical “digitized” or “snapshot” launch window screening approach, where launch objects are compared against the on-orbit catalogue at a small launch “T-zero” step size.The new capability can detect launch closure holds based upon miss distance, collision probability or both. Prior to the probability calculation, each covariance is tested for positive definiteness and remediated if necessary. The tool's support for parallel processing, combined with its algorithmic efficiency, allow it to process all deployed objects. The tool aggregates all resulting launch holds into a concise report for the launch director and team.

Advanced Driver Assistance System (ADAS)

The motivations are art, elegance, and ethics. Human error and negligence cause 90% of road accidents, making the human the important part in this process while also being the weakest link. Every year, numerous accidents are reported as a result of excessive speed and poor decision-making. According to a logical concept, every 1% increase in velocity increases the probability of a fatal collision by 4% and the risk of a serious collision by 3%. Modern automobiles are a symbiosis of several electrical subsystems that work together to provide a safe and enjoyable driving experience. One of the technologies used in automotive development is the Advanced Driver Assistant System (ADAS). It is a system that monitors different parameters related to the vehicle and its surroundings in order to detect potentially dangerous circumstances early on. Sensors with sophisticated electronics, known as Sensor Network (SN), are already installed in many cars today to accomplish (ADAS) technology. A well-known ADAS is the Adaptive Speed Control system (ASC), Automatic Brake System (ABS), Warning Collision System (WCS), and Legal Restriction System, which are used to avert high-speed accidents. In addition, there are Lane Keeping Systems (LKS) and Lane Change Systems (LCS).

An adaptive MAC-agnostic ranging scheme for underwater acoustic mobile networks

In the last ten years several simulation studies on Autonomous Underwater Vehicle (AUV) swarm fleet formation have been performed, and some preliminary sea demonstrations of proof-of-concept prototypes were carried out. However, their actual realization is hindered by the challenges imposed by the underwater acoustic channel and the difficulties of keeping track of the vehicles’ positions due to the long latency required by traditional Two-Way Travel-Time (TWTT) ranging measurements, that require a specific signaling, hence limiting the throughput of the underwater network. Although One-Way Travel-Time (OWTT) halves the latency, it requires a high precision oscillator, such as an atomic clock or an oven controlled crystal oscillator (OCXO), to be installed in each modem processing unit: while atomic clocks are still very expensive, OCXO are very power demanding, making their application to underwater acoustic networks not always possible, especially in the case of low cost vehicle swarms.In this paper we present a network protocol stack able to perform ranging and localization within the communication task in underwater acoustic networks, limiting the network overhead. Specifically, new layers have been added to the preexisting DESERT Underwater protocol stack to perform the ranging tasks without compromising the correct operation of the communication network. A ranging layer is placed on top of the Medium Access Control (MAC) scheme, allowing the latter to be changed according to the network topology and requirements. This MAC-agnostic ranging layer is further optimized by adapting the amount of data transmitted according to the channel state, and the ranging entries inserted in the data packets according to the least recent information transmitted, hence minimizing the Age of Information. Simulation results obtained with the DESERT Underwater Framework show how this layer allows all AUVs in the swarm to know their distance from every other node in the network, thus limiting the probability of vehicle collisions and allowing better mission coordination.

Design of steering wheel torque with human-machine interaction for uncoupled shared steering

In the uncoupled shared steering architecture based on the steering-by-wire (SBW) system, direct access to road feel and automation-related information is unavailable to the driver. To address this problem, this paper proposes a steering wheel torque feedback model that considers human-machine interaction information. First, the model predictive control (MPC) is adopted in lateral vehicle control by automation. Then a fuzzy control-based control authority allocation model is applied to assign the control authority weight between the human driver and automation according to the value of the Path Lateral Hazard (PLH) Factor and the Driver’s Intent Evaluation (DIE) Factor. These two factors reflect the probability of lateral vehicle collision and the intensity of the driver’s driving intention, respectively. Next, the road feel feedback torque and the human-machine interface (HMI) feedback torque is incorporated in the steering wheel feedback torque model to enhance the driver’s experience in SBW vehicles and trust in the automation. The HMI feedback torque is designed to provide human drivers with information on control authority weight variation and desired angle deviation between the human driver and automation. Simulation and experiment results suggest that the proposed uncouple shared control method can accelerate driver acceptance of automation and provide the driver with a more intuitive steering experience.

Finite element model for partial differential equations in optimization of vehicle safety collision problem

Vehicle safety on roadsides is vital for preventing collisions, controlling failures and accidents, and ensuring driver and passenger wellness. Finite Element Analysis (FEA) in vehicle safety relies on the vehicle’s physical attributes for predicting and preventing collisions. This article introduces a Differential FEA (DFEA) model for predicting vehicle collisions regardless of the speed and direction for driver/ passenger safety. The proposed model induces a vehicle’s speed, direction, and displacement from two perspectives: self and approaching vehicle. The collision probability with the trailing or persuading vehicle is calculated by distinguishing the forward and rear features. The differential calculus for the point of deviation and distance metrics are significantly estimated for a vehicle’s front and rear ends. Such calculus generates a maximum and minimum possibility for self and approaching vehicle contact. This contact is further split based on the collision threshold; the threshold is determined using the safe distance between two vehicles for collision-less driving. The threshold exceeding vehicles are alerted for their change in direction/ speed through contact point (rear/front) differential derivatives. This ensures collision detection under fewer contact errors, leveraging the safety of the duo vehicles.