Collision Probability Research Articles

Analytical solution versus the Monte Carlo simulation for studying the H+ beam neutralization in H2 gas target neutralizer

A comparative Study on the conversion of an energetic H+ beam inside a cell neutralizer filled with hydrogen gas target is conducted by two different methods to determine the maximum beam neutralization efficiency and corresponding optimal gas thickness in terms of beam energy, ranging from 1 keV to 10 MeV. One method analytically solves the equations of particle flux with a deterministic response. The other uses the random collisional probability based on the Monte Carlo simulation with non-deterministic response, which is implemented by COMSOL software. Effective reactions of charge exchange and reionization are commonly considered in both methods with the energy-depended collisional cross section. The results obtained from the two methods are compared, demonstrating good fitness with a mean difference of ±1.5 %. This acceptive difference arises often due to statistical errors that occur in the Monte Carlo calculations. Additionally, a close agreement is found between calculated and measured efficiencies.

Experimental study on the movement characteristics of adversarial people in the corridor

With the continuous development of pedestrian dynamics, the movement characteristics of multiple types of people (elderly, children, disabled people, etc.) under various scenarios (flood, fire, special building structure, etc.) have been extensively and deeply studied. The relevant conclusions and movement rules are of great significance for guiding the design of building structures and crowd evacuation instructions. However, there is a lack of research on the movement of adversarial crowds with different movement purposes and strong interactions. The analysis of its movement characteristics and the movement modelling of interaction rules need to be further explored through controlled experiments. Therefore, this paper focuses on the study of the movement characteristics of the adversarial crowd and carries out a realistic experiment in which pedestrians in the corridor escape from attackers to complete the evacuation. The purpose was to address the following questions: the impact of attackers and corridor width on pedestrian movement. It was found that the attacker's movement pause would increase the desired velocity of the crowd, and thus the velocity-density relationship was different from the traditional fundamental diagram. The width of the corridor has a great impact on the movement speed and the collision probability of the crowd. Based on the results of the experiment, a model was established and simulation experiments were carried out by changing the width of the corridor. It is hoped that the results of this paper can provide help for emergency evacuation guidance for pedestrians in the future.

Dynamic Risk Assessment and Control Framework for Work Zone and Its First Implementation under Simulation Environment

The number of road maintenance operations in China is increasing. Work zones can interfere with everyday driving and bring significant safety hazards to the throughput vehicles and construction personnel. However, there is currently a lack of effective methods to conduct a dynamic risk assessment of work zones and provide control guidance when necessary. Therefore, this study proposes a dynamic risk assessment and control framework for the work zones. The framework adopts a closed-loop control and dynamic monitoring architecture. A massive amount of microscopic data on vehicles’ behavior is obtained based on advanced data collection technologies (including multicamera multiobject tracking, naturalistic driving, and microscopic traffic simulation). Traffic conflicts between single vehicle and two- or multivehicle are detected using vehicle behavior analyzing technology and the surrogate safety assessment model. A comprehensive assessment index, i.e., UTECN (unit total equivalent conflict number), is established that can consider the severity and possibility of accidents caused by conflicts based on the vehicle collision energy theory, probability theory, and risk management theory. A risk assessment standard is established based on the level of safety service. The framework is first implemented in a simulation environment, and its feasibility and effectiveness are verified by taking a work zone of Shanghai Waihuan Expressway S20 as a case study. The result shows that the framework has good practicality. Under a moderate traffic level, the risk of the work zone can be assessed within 20 minutes. It can be quickly and effectively controlled at an acceptable level after several iterations, which is of great significance for ensuring the safety of life and property of throughput vehicles and construction personnel within the work zone.

Advanced Road Safety: Collective Perception for Probability of Collision Estimation of Connected Vehicles

In the dynamic landscape of vehicular communication systems, connected vehicles (CVs) present unprecedented capabilities in perception, cooperation, and, notably, probability of collision management. This paper’s main concern is the collision probability of collision estimation. Achieving effective collision estimation heavily relies on the sensor perception of obstacles and a critical collision probability prediction system. This paper is dedicated to refining the estimation of collision probability through the intentional integration of CV communications, with a specific focus on the collective perception of connected vehicles. The primary objective is to enhance the understanding of the potential probability of collisions in the surrounding environment by harnessing the collective insights gathered through inter-vehicular communication and collaboration. This improvement enables a superior anticipation capacity for both the driving system and the human driver, thereby enhancing road safety. Furthermore, the incorporation of extended perception strategies holds the potential for more accurate collision probability estimation, providing the driving system or human driver with increased time to react and make informed decisions, further fortifying road safety measures. The results underscore a significant enhancement in collision probability awareness, as connected vehicles collectively contribute to a more comprehensive collision probability landscape. Consequently, this heightened collective collision probability perception improves the anticipation capacity of both the driving system and the human driver, contributing to an elevated level of road safety. For future work, the exploration of our extended perception techniques to achieve real-time probability of collision estimation is proposed. Such endeavors aim to drive the development of robust and anticipatory autonomous driving systems that truly harness the benefits of connected vehicle technologies.

Short-Term Collision Probability Caused by Debris Cloud

We propose a method for a fast and accurate estimation of the collision probability in a short window following the generation of a debris cloud. A multirevolution Lambert targeting problem is employed to determine all the locations where the collision probability is nonnegligible. Then, the Lambert problem and state transition matrix are used to obtain the image of the target body in the spread velocity space, where the collision probability computation is performed. To perform and control the accuracy of the integration of the collision rate needed to obtain collision probabilities, we apply high-order Taylor expansion and automatic domain splitting techniques. The method is validated using a Monte Carlo simulation and a literature-based test case. Test cases demonstrate the validity of the approach for Keplerian and perturbed dynamics.

Influence of cusp magnetic field configuration on plasma parameters of radio frequency driven negative ion source for CRAFT NNBI

Radio Frequency (RF) driven negative ion source is the preferred ion source for negative ion based Neutral Beam Injection system (NNBI). The plasma generator, which consists of RF driver(s) and an expansion chamber, is one of the key parts for NNBI. The distribution of cusp magnetic field which is generated by permanent magnets installed at the lateral wall of the expansion chamber is a common issue, due to the great influence on electron density, electron temperature and spatial uniformity of plasma density in the extraction area. Two kinds of configuration of cusp magnetic field (checkerboard type and symmetric type) and three types of arrangement of permanent magnets (with three, four and five rows of magnets) have been carried out with the approach of finite element and the experimental performance. On the RF negative ion source testbed, a moveable Langmuir probe has been adopted to investigate the characteristic of electron density and temperature near the extraction area. Since the symmetric type with three rows of permanent magnets generates the distribution of cusp magnetic field that contribute to extract the plasma from RF driver and improve the probability of particle collision easily, the higher electron density and the lower electron temperature was obtained via simulation. The experimental conclusion is that the symmetric type with three rows of permanent magnets leads to a higher electron density and lower electron temperature, showing a great agreement with simulation results. The results are helpful to optimize the structure and performance of high power ion source, and provide support for the development of large area RF negative ion sources with multi-driver.

Engineered DNA bonsai system for ultrasensitive wide-field determination and intracellular dynamic imaging of protein with tunable dynamic range and sensitivity

Herein, an engineered DNA bonsai system was assembled using DNA nanocube (DNC) as pot and a group of DNA recognition hairpins (RHpn, n = 1–5) as plants for ultrasensitive wide-field detection and dynamic intracellular imaging of matrix metalloproteinase-2 (MMP-2) with tunable dynamic range and sensitivity. Impressively, by changing the stem length of the RHpn (n = 1–5), the thermodynamic parameter free energy (△G) of RHpn was regulated to control the binding reaction between RHpn and output target (OT) (like changing the plants species of bonsai, Bonsai 4-RHpn, n = 1–5). When the △G of RHpn was low, the target detection in high concentration range could be achieved. Conversely, the target detection within the low concentration range also could be realized with the high △G of RHpn. Therefore, the developed biosensing platform based on the engineered DNA bonsai system could achieve the ultrasensitive wide-field determination of MMP-2 with 150000-fold tuned detection sensitivity. Meanwhile, by virtue of the spatial restriction effect of DNC to vary the loaded number and the local concentration of RHp1 (like changing the plants density of bonsai, Bonsai n-RHp1, n = 1–4), the kinetic property of the binding process between RHp1 and OT could be manipulated, in which a low substrate concentration leaded to a low reaction efficiency and a high local concentration resulted in a quick reaction rate with high collision probability. Moreover, the designed DNA bonsai system with the highest local concentration (Bonsai 4-RHp1) could achieve rapid detection of the target MMP-2, which easily conquered the main predicaments of dynamic detection methods: limited dynamic range and low detection efficiency. Most importantly, the programmable DNA bonsai system had been successfully applied in ultrasensitive and high-efficient dynamic visualization of protein MMP-2 in living cells, which could not only make it possible to quickly and precisely monitor disease related proteins with tiny changes at different concentration ranges, but also possess tremendous potential to provide high-precision disease information at different stages in disease diagnosis and prognosis monitoring.

A prediction model of fluid–solid erosion wear in hydraulic spool valve orifice

Fluid–solid erosion wear may damage the port structure and change the linear characteristics of the spool valve. They may also affect null characteristics and the control accuracy of the valve, resulting in production and safety accidents. In the present study, the prediction model of the orifice throttling coefficient and worn profile for engineering applications such as compensation control and life degradation assessment is established. The collision probability between particles and the wall surface is particularly considered based on the E/CRC erosion wear model. The relationship between the spool opening and the impact angle of particles is investigated numerically when developing collision probability model. Meanwhile, the influence of particle concentration, spool opening, and differential pressure on the orifice throttling coefficient and worn profile is analyzed by experiments, and the microcosmic surface morphology of the spool metering edge is analyzed by scanning electron microscopy (SEM). The results show that the impacting of solid particles on the orifice will result in extrusion-thin platelets flaking wear and deformation wear. The maximum relative error between the predicted results and the experimental measurements is 2.25%. Moreover, the predicted results of the model are in good agreement with the measured values under different particle concentrations, spool openings and differential pressure.

Evolutionary optimization for risk-aware heterogeneous multi-agent path planning in uncertain environments.

Cooperative multi-agent systems make it possible to employ miniature robots in order to perform different experiments for data collection in wide open areas to physical interactions with test subjects in confined environments such as a hive. This paper proposes a new multi-agent path-planning approach to determine a set of trajectories where the agents do not collide with each other or any obstacle. The proposed algorithm leverages a risk-aware probabilistic roadmap algorithm to generate a map, employs node classification to delineate exploration regions, and incorporates a customized genetic framework to address the combinatorial optimization, with the ultimate goal of computing safe trajectories for the team. Furthermore, the proposed planning algorithm makes the agents explore all subdomains in the workspace together as a formation to allow the team to perform different tasks or collect multiple datasets for reliable localization or hazard detection. The objective function for minimization includes two major parts, the traveling distance of all the agents in the entire mission and the probability of collisions between the agents or agents with obstacles. A sampling method is used to determine the objective function considering the agents' dynamic behavior influenced by environmental disturbances and uncertainties. The algorithm's performance is evaluated for different group sizes by using a simulation environment, and two different benchmark scenarios are introduced to compare the exploration behavior. The proposed optimization method establishes stable and convergent properties regardless of the group size.

An Adaptive Contention Window using Actor-Critic Reinforcement Learning Algorithm for Vehicular Ad-hoc NETworks

The MAC layer IEEE 802.11p standard’s collision avoidance mechanism is sub-optimal due to the use of a Binary Exponential Backoff (BEB). This technique has an increase in the backoff slot time to minimize the probability of subsequent collisions after the occurrence of a collision. This leads to a degradation of radio bandwidth wastage, especially when managing access to a dense number of stations. An incorrect backoff setting can also increase the likelihood of collisions and decrease network performance. For adaptive CW selection, the proposed model is based on a Reinforcement Learning (RL) Actor-Critic (AC) network to adapt the CW value by learning the VANET environment. NS-3 for VANET is utilized to test and compute the optimal CW values. The proposed AC and EAC outperform traditional BEB used in the IEEE 802.11p standard as the number of vehicles increases. The throughput is enhanced by 54% and 69% respectively for AC and Enhanced AC (EAC). Similarly, the end-to-end delay for AC is reduced by 19%, and for EAC it’s reduced by 25%.

Revolutionizing the capture efficiency of ultrasensitive digital ELISA via an antibody oriented-immobilization strategy.

Bead-based digital ELISA, the most sensitive protein quantification method, has drawn much attention to exploring ultra-low abundance biomarkers in the life sciences and clinical applications. However, its major challenge refers to the low antigen capture efficiency in the immunoreaction process due to the low probability of collision between the deficient concentration of the analytes and the captured antibody-immobilized on the beads. Here, we achieved significantly improved reaction efficiency in the digital signal formation by fixing the orientation of antibodies and revealed the kinetic mechanism for the first time. A facile and fast antibody conjugation strategy that formed boronate ester complexes was designed to retain the uniform orientation of antibodies with controllable antibody density. Remarkably, the oriented immobilized antibody exhibited stronger antigen-binding capacity and faster antigen-binding speed compared to randomly immobilized antibodies, with capture efficiency increasing approximately 14-fold at 15 μg of antibody per 1 mg microbeads (0.035 antibody nm-2) under 0.5 h incubation. Combined with theoretical analysis, we verified that the improved capture efficiency of the oriented antibodies mainly originated from the considerable rise in the binding rate constant (kon) rather than the increase in antigen-binding sites, which further prominently decreased the limit of detection (LoD) in a shorter incubation time compared with the randomly immobilized antibody. In conclusion, the antibody oriented conjugation method effectively overcomes the low capture efficiency challenge of bead-based digital ELISA. It paves a promising way for further improving the digital immunoassay performance and promotes the early diagnosis of diseases by recognizing more ultra-low abundance significant biomarkers.

Collisions among elongated settling particles: The twofold role of turbulence

We study the collision rates of settling spheres and elongated spheroids in homogeneous, isotropic turbulence by means of direct numerical simulations aiming to understand microscale-particle encounters in oceans and lakes. We explore a range of aspect ratios and sizes relevant to the dynamics of plankton and microplastics in water environments. The results presented here confirm that collision rates between elongated particles in a quiescent fluid are more frequent than those among spherical particles in turbulence due to oblique settling. We also demonstrate that turbulence generally enhances collisions among elongated particles as compared to those expected for a random distribution of the same particles settling in a quiescent fluid, although we also find a decrease in collision rates in turbulence for particles of the highest density and moderate aspect ratios (A=5). The increase in the collision rate due to turbulence is found to quickly decrease with aspect ratio, reach a minimum for aspect ratios approximately equal to 5, and then slowly increase again, with an increase up to 50% for the largest aspect ratios investigated. This non-monotonic trend is explained as the result of two competing effects: the increase in the surface area with aspect ratio (beneficial to increase encounter rates) and the alignment of nearby prolate particles in turbulence (reducing the probability of collision). Turbulence mixing is, therefore, partially balanced by rod alignment at high particle aspect ratios.

NR-RRT: Neural Risk-Aware Near-Optimal Path Planning in Uncertain Nonconvex Environments

Balancing the trade-off between safety and efficiency is of significant importance for path planning under uncertainty. Many risk-aware path planners have been developed to explicitly limit the probability of collision to an acceptable bound in uncertain environments. However, convex obstacles or Gaussian uncertainties are usually assumed to make the problem tractable in the existing method. These assumptions limit the generalization and application of path planners in real-world implementations. In this article, we propose to apply deep learning methods to the sampling-based planner, developing a novel risk bounded near-optimal path planning algorithm named neural risk-aware RRT (NR-RRT). Specifically, a deterministic risk contours map is maintained by perceiving the probabilistic nonconvex obstacles, and a neural network sampler is proposed to predict the next most-promising safe state. Furthermore, the recursive divide-and-conquer planning and bidirectional search strategies are used to accelerate the convergence to a near-optimal solution with guaranteed bounded risk. Worst-case theoretical guarantees can also be proven owing to a standby safety guaranteed planner utilizing a uniform sampling distribution. Simulation experiments demonstrate that the proposed algorithm outperforms the state-of-the-art remarkably for finding risk bounded low-cost paths in seen and unseen environments with uncertainty and nonconvex constraints.

Packet Collision Probability of Direct-to-Satellite IoT Systems

Packet Collision Probability of Direct-to-Satellite IoT Systems

An improved 802.15.4 unslotted CSMA/CA algorithm for reducing collision probability and delay in wireless sensor networks

An improved 802.15.4 unslotted CSMA/CA algorithm for reducing collision probability and delay in wireless sensor networks

Deep Learning-based Basketball Free Throw Attitude Analysis and Hit Probability Prediction System Research

Abstract Traditional basketball free-throw hitting probability prediction mainly relies on naked eye vision to analyze basketball players, which makes it difficult to achieve stable and accurate free-throw hitting probability prediction. In this study, we propose a deep learning-based algorithm for recognizing basketball free throw poses and predicting the probability of hitting free throws, and we build a corresponding system framework. DetectNet is used to screen free throw motion detectors, real-time estimate and recognition of free throw poses, and code rewriting. At the same time, Open Pose is used as a human skeletal joint point detector to extract the joint point information of the free throw shooter. The joint points with wrong information are repaired, and the classification prediction of the free throw hitting result is realized based on the support vector machine. In the performance simulation experiments, the average accuracy of the free throw pose recognition method proposed in this paper is as high as 98.55% on the MSR Action 3D dataset, and the recognition rate of the elbow lift pose, which is the most difficult to recognize, is also higher than that of other comparative algorithms. The hit probability prediction method is also 12.64% and 6.25% more accurate than the OpenPose algorithm in hit-and-miss prediction, and the accuracy has also improved by 4.47% and 5.41%, respectively. The free throw pose recognition and hit probability prediction method in this paper has excellent performance.

Basketball Free Throw Posture Analysis and Hit Probability Prediction System Based on Deep Learning

Basketball Free Throw Posture Analysis and Hit Probability Prediction System Based on Deep Learning

Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

Risk assessment is a crucial component of collision warning and avoidance systems for intelligent vehicles. Reachability-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle collisions. However, they suffer from over-conservatism, potentially resulting in false–positive risk events in complicated real-world applications. In this paper, we combine two reachability analysis techniques, a backward reachable set (BRS) and a stochastic forward reachable set (FRS), and propose an integrated probabilistic collision–detection framework for highway driving. Within this framework, we can first use a BRS to formally check whether a two-vehicle interaction is safe; otherwise, a prediction-based stochastic FRS is employed to estimate the collision probability at each future time step. Thus, the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety-critical events. To construct the stochastic FRS, we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidence-aware dynamic belief to improve the prediction accuracy. Extensive experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data. The efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway scenarios. The proposed risk assessment framework is promising for real-world applications.

Numerical Analysis of an Autonomous Emergency Braking System for Rear-End Collisions of Electric Bicycles.

The rapid growth in the number of electric bicycles (e-bicycles) has greatly improved daily commuting for residents, but it has also increased traffic collisions involving e-bicycles. This study aims to develop an autonomous emergency braking (AEB) system for e-bicycles to reduce rear-end collisions. A framework for the AEB system composed of the risk recognition function and collision avoidance function was designed, and an e-bicycle following model was established. Then, numerical simulations were conducted in multiple scenarios to evaluate the effectiveness of the AEB system under different riding conditions. The results showed that the probability and severity of rear-end collisions involving e-bicycles significantly decreased with the application of the AEB system, and the number of rear-end collisions resulted in a 68.0% reduction. To more effectively prevent rear-end collisions, a low control delay (delay time) and suitable risk judgment criteria (TTC threshold) for the AEB system were required. The study findings suggested that when a delay time was less than or equal to 0.1 s and the TTC threshold was set at 2 s, rear-end collisions could be more effectively prevented while minimizing false alarms in the AEB system. Additionally, as the deceleration rate increased from 1.5 m/s2 to 4.5 m/s2, the probability and average severity of rear-end collisions also increased by 196.5% and 42.9%, respectively. This study can provide theoretical implications for the design of the AEB system for e-bicycles. The established e-bicycle following model serves as a reference for the microscopic simulation of e-bicycles.

Low-cost and high-performance channel access strategies for Internet of Nano-Things applications

Nanodevices, which are only a few nanometers (nm) in size, are interconnected to form the Internet of Nano-Things (IoNT) that performs complex operations. One of the key challenges is ensuring efficient channel access control for nanodevices, especially when dealing with large network sizes. Medium access control (MAC) protocols serve this purpose, but traditional approaches are not practical due to the inherent constraints of nanodevices. In this paper, we propose two novel MAC protocols for IoNT applications. The first protocol, Slot Assignment-Based (SAB) MAC, is a contention-free method relying on scheduling. In contrast to its counterparts, it enables simultaneous packet transmission through Time Spread On-Off Keying (TS-OOK), effectively minimizing the collision probability and end-to-end delay. The second protocol, Receiver-Initiated and Directed (RID) MAC, adopts a contention-based approach to reduce unnecessary transmissions caused by flooding. It achieves this by limiting the number of active nanodevices within a time interval using directional antennas without incurring scheduling overhead. We evaluated the performance of these protocols through comprehensive simulations, comparing them with counterparts in terms of packet transmission success, energy consumption, end-to-end delay and setup overhead. In dense topologies, SAB-MAC outperforms Transparent (TRN) MAC by approximately twice the packet transmission success reaching up to 95.73%. It accomplishes this with 1000 times lower end-to-end delay and reduced setup overhead than Time Division Multiple Access (TDMA). Conversely, RID-MAC achieves twice the packet transmission success of TRN-MAC and ten times that of unicast-based methods, all with lower end-to-end delay and nearly equivalent energy consumption. Consequently, due to its superior performance SAB-MAC is the optimal choice for communication between nanorouters (NRs). However, RID-MAC is more suitable for communication between nanosensors (NSs), as it incurs no setup overhead.