Collision Distance Research Articles

Study of modulation in complex refractive indices induced by ultrafast relativistic electrons using infrared and THz probe pulses.

Objective 
Achieving ultra-precise temporal resolution in ionizing radiation detection is essential, particularly in positron emission tomography, where precise timing enhances signal-to-noise ratios and may enable reconstruction-less imaging. A promising approach involves utilizing ultrafast modulation of the complex refractive index, where sending probe pulses to the detection crystals will result in changes in picoseconds (ps), and thus a sub - 10 ps coincidence time resolution can be realized. Towards this goal, here, we aim to first measure the ps changes in probe pulses using an ionizing radiation source with high time resolution.
Approach 
We used relativistic, ultrafast electrons to induce complex refractive index and use probe pulses in the near-infrared (800 nm) and terahertz (THz, 300 µm) regimes to test the hypothesized wavelength-squared increase in absorption coefficient in the Drude free-carrier absorption model. We measured BGO, ZnSe, BaF2, ZnS, PBG, and PWO with 1 mm thickness to control the deposited energy of the 3 MeV electrons, simulating ionization energy of the 511 keV photons. 
Main results 
Both with the 800 nm and THz probe pulses, transmission decreased across most samples, indicating the free carrier absorption, with an induced signal change of 11% in BaF2, but without the predicted Drude modulation increase. To understand this discrepancy, we simulated ionization tracks and examined the geometry of the free carrier distribution, attributing the mismatch in THz modulations to the sub-wavelength diameter of trajectories, despite the lengths reaching 500 µm to 1 mm. Additionally, thin samples truncated the final segments of the ionization tracks, and the measured initial segments have larger inter-inelastic collision distances due to lower stopping power (dE/dx) for high-energy electrons, exacerbating diffraction-limited resolution. 
Significance
Our work offers insights into ultrafast radiation detection using complex refractive index modulation and highlights critical considerations in sample preparation, probe wavelength, and probe-charge carrier coupling scenarios.

Improving Pedestrian Safety with Head-Up Display Warning in a Connected Environment

In this paper, the potential of using a head-up display (HUD) in the connected environment to improve a vehicle’s running comfort and pedestrian safety is tested, by providing warning information to drivers in advance. To achieve this objective, driving simulation technology is used to construct the connected environment and develop the HUD, and the effectiveness of the system is then tested. Specifically, thirty-four participants were recruited to conduct driving simulation experiments in six scenarios: three warning display types (Baseline/Head-down display/Head-up display) combined with two weather conditions (clear weather/foggy weather). The effects of the three different warning display types on braking risk-avoidance strategy were studied by comparing the drivers’ performance during the perception and decision stage (position of accelerator-pedal release, position of first braking), the risk-avoidance manipulation stage (maximum deceleration, braking distance) and the risk-avoidance result stage (minimum collision distance, position of minimum speed). The influences of weather conditions and driver attributes were also considered. When the HUD warnings were activated, drivers started to decelerate further away from pedestrians, with a more stable and moderate deceleration process and a greater safety margin between the vehicle and the pedestrians. Using HUD warnings in foggy conditions improved drivers’ perception and decision abilities, this study confirmed the great benefits that HUD warnings in the connected environment can bring to traffic safety, especially under risky situations and inclement weathers. The research results provide a reference for the more humanized and rationalized optimization design of these warning systems.

Obstacle-aware path following of omni-wheeled robots using fuzzy inference approach

This study proposes a real-time LiDAR-based approach for efficient path following in omni-wheeled robots (OWRs) capable of dynamically adapting to surrounding obstacles. To achieve this, two fuzzy control schemes are developed: Fuzzy Sliding-Mode Tracking Control (FTC) for precise path following and Fuzzy Obstacle-avoidance Control (FOC) for real-time collision avoidance using LiDAR data. FTC utilizes fuzzified sliding surfaces, derived from tracking errors, to follow a planned path. In the presence of obstacles along the path, FOC, employing LiDAR measurements, assesses collision distance and direction, ensuring safe navigation. The outputs of FTC and FOC are seamlessly merged using a smooth switching factor to generate voltage inputs for the actuators. The effectiveness of the proposed approach is demonstrated through extensive simulations and real-world experiments under various conditions, including wheel disturbances, diverse trajectories, and varying initial positions.

General Comparative Analysis of Bird-Bat Collisions at a Wind Power Plant in the Department of Rivas, Nicaragua, between 2014 and 2022

From 2014 to 2022, eight monitoring campaigns have been carried out, documenting 119 collisions of birds belonging to 25 species corresponding to 14 families. Of the total collisions, 35 individuals could not be identified because only remains of the animal’s body (feathers) were found, due to the presence of day and night predators. Of the bird species identified, 17 are resident, three are migratory species, two are transient species, one species with transient populations that migrate south, and two species with both resident and migratory populations. During these years, no species with an important conservation category according to the IUCN were reported. Three species are on Nicaragua’s Red List, the migratory species Blackburnian Warbler (Setophaga fusca) and the resident species Mangrove Cuckoo (Coccyzus minor), both with the Near Threatened category; and the only migratory hummingbird for Nicaragua, the Ruby-throated Hummingbird (Archilochus colubris) with the Vulnerable category. The family with the highest frequency of collided bird species is the pigeons (Columbidae), these being very significant (X2 = 345.20, p < 0.01). The species with the highest frequency of collisions is the Common Ground-Dove (Columbina passerina) species, with these collisions being very significant (X2 = 72.05, p < 0.01). Collisions per month and year show very significant differences, especially for April 2016 (X2 = 41.60, p < 0.01). Tower T13 shows significant collisions in these eight monitoring campaigns (X2 = 36.48, p < 0.05). Bird collision distances show very significant collisions between 0 and 5 m away from the turbines (X2 = 268.96, p < 0.01). No differences were found regarding collisions between large and small birds (X2 = 2.09, p > 0.05). The granivore group presented a very significant frequency of collisions (X2 = 84.00, p < 0.01), as well as a very significant frequency of collisions in the non-passerines birds (X2 = 42.26, p < 0.01). Very significantly higher fatality frequencies were observed in 2016 (X2 = 60.16, p < 0.01). For the period 2014 to 2022 in bats, 134 collisions of bats belonging to 18 species corresponding to four families were observed. Of the total number of collisions, 24 individuals could not be identified because they were in poor condition. No species with important conservation criteria have been recorded according to the IUCN or the Nicaraguan Red List. The Molossidae family is the group with the highest number of very significant collisions (X2 = 53.61, p < 0.01), with the Pallas’s Mastiff Bat (Molossus molossus) being the one that presented very significant collisions (X2 = 199.69, p < 0.01). Very significant collisions occur, with a peak in November 2015 (X2 = 75.19, p < 0.01). Tower T15 presents the highest frequency of collisions, which is very significant (X2 = 39.04, p < 0.01). The frequency of collisions remains very significantly higher in the three distance categories closest to the towers, from 0 to 14.9 m (X2 = 43.64, p < 0.01). The highest frequency for very significant collisions of bats is that of insectivores (X2 = 222.10, p < 0.01). A very significant frequency of collisions was observed in 2015 (X2 = 68.75, p < 0.01). Key periods like fall and spring bird migration should be prioritized when planning collision monitoring plans. Standardized monitoring protocols and implementation of estimated mortality formulas that compensate for the search effectiveness of observers, the search area, and the removal of collisions are also important for not underestimating bird and bat collisions.

Using Hill Climb Assembler Encoding neural networks to control follower vehicles in an underwater swarm

The paper presents the application of neural networks evolving under the Hill Climb Assembler Encoding (HCAE) algorithm to control follower autonomous underwater vehicles that are members of a swarm consisting of one leader vehicle and a group of followers. The leader is responsible for global navigation and guiding the swarm, while low-cost followers unable to cover longer distances on their own follow the leader. To locate the leader, the followers only use information about the distance to it. Directional information is unavailable to the followers. Moreover, information about the distance is transmitted to followers with a frequency depending on the number of followers: the more followers, the lower the frequency. In addition to tracking the leader, the followers must also be able to avoid collisions with other followers and the leader. To this end, they are equipped with short-range sensors looking around each follower. The simulations presented in the paper were carried out for different swarm sizes, different sensor ranges, different collision distances, and variable leader speed, and showed high effectiveness of the proposed neural solution. The most common strategy for the followers when following a leader was to circle it at a certain safe distance depending on the sensor range.

Effect of fluid–structure interaction on the oblique water entry of the projectile under the influence of floating ice structure

The water entry of a projectile constrained by polar floating ice presents a unique cross-media challenge. This paper investigates the dynamics of oblique water entry for a projectile influenced by floating ice using the fluid–structure interaction (FSI) method. The validity of the numerical method has been confirmed through experimental validation. The water entry process of a projectile from the side of the floating ice is examined. The evolution of the cavity and the movement patterns of objects as the distance between the projectile and the floating ice decreases toward collision are investigated. The influence of water on the critical collision distance between the projectile and the floating ice during oblique water entry is analyzed. Additionally, the physical mechanism of floating ice deflection through collision is investigated based on the theory of cavity dynamics. Subsequently, the study focuses on the oblique water entry process of a projectile colliding with the upper surface of the floating ice. Different entry angles determine the collision mode between the projectile and the floating ice surface. This study also examines how varying entry angles influence cavity evolution and object movement patterns during oblique collisions. Different collision modes between the projectile and the floating ice lead to asymmetric cavity evolution and various modes of object deflection motion. Finally, changes in the flow field and vortex structure during oblique collisions are studied to examine the influence of the FSI process between the projectile and the floating ice on the flow field.

Active learning of the collision distance function for high-DOF multi-arm robot systems

Abstract Motion planning for high-DOF multi-arm systems operating in complex environments remains a challenging problem, with many motion planning algorithms requiring evaluation of the minimum collision distance and its derivative. Because of the computational complexity of calculating the collision distance, recent methods have attempted to leverage data-driven machine learning methods to learn the collision distance. Because of the significant training dataset requirements for high-DOF robots, existing kernel-based methods, which require $O(N^2)$ memory and computation resources, where $N$ denotes the number of dataset points, often perform poorly. This paper proposes a new active learning method for learning the collision distance function that overcomes the limitations of existing methods: (i) the size of the training dataset remains fixed, with the dataset containing more points near the collision boundary as learning proceeds, and (ii) calculating collision distances in the higher-dimensional link $SE(3)^n$ configuration space – here $n$ denotes the number of links – leads to more accurate and robust collision distance function learning. Performance evaluations with high-DOF multi-arm robot systems demonstrate the advantages of the proposed active learning-based strategy vis- $\grave{\text{a}}$ -vis existing learning-based methods.

A graphical collision alert system of power-driven ship in sight situations considering geometric encounter situations

This paper proposes a power-driven ship collision alert model considering geometric encounter situations in sight situations by collecting the natural environment, ship static and dynamitic information. The kernel of the proposed method is to introduce a ship geometric encounter model to determine the ship encounter situation and to identify the influencing factors, including ship position, ship speed and ship heading, by considering ship-ship collision situations. These factors, together with ship static parameters and natural environment, are treated as input variables to derive the collision risk through a fuzzy inference system. The merit of developed model is that the relative bearing, collision distances and relative course are intuitively described by graphical user interface, which can assist the crew well understand the decision of machine and can be applied to human-machine collaboration for intelligent ship (e.g. remotely control ship). Moreover, from further analysis, the nearest point and the actions taken at the nearest point provide a useful alert for collision risk. From these analyses, the proposed model can be used to improve ship handling and reduce the probability of collision during ship encounters.

Intelligent vehicle obstacle avoidance path-tracking control based on adaptive model predictive control

Abstract. In order to solve the problems of low path-tracking accuracy, poor safety, and stability of intelligent vehicles with variable speeds and obstacles on the road, a double-layer adaptive model predictive controller (MPC) is designed. A vehicle point mass model is used in an obstacle avoidance planning controller, and the safety collision distance model is established according to the distance relationship between the vehicle and the obstacle to improve the driving safety of the vehicle. The design of the path-tracking controller is based on the three-degrees-of-freedom dynamics model. According to the relationship between the predictive horizon and vehicle speed in the MPC algorithm, an adaptive path-tracking control strategy which can update the prediction horizon in real time is proposed to improve the accuracy of vehicle path tracking. To increase the vehicle stability, a sideslip angle and an acceleration control variable are added to the vehicle dynamics model as soft constraint conditions. The proposed method is simulated based on a CarSim and MATLAB/Simulink co-simulation platform. The simulation results show that the maximum lateral path deviation and the maximum centroid sideslip angle of the designed controller are 0.13 m and 0.4∘, respectively. Compared with the traditional MPC, the adaptive MPC maximum lateral path deviation and the maximum centroid sideslip angle are reduced by 0.51 m and 1.57∘, respectively, which proves the effectiveness of the proposed method.

Development of model predictive controller in avoidance design of Unmanned Aerial Vehicle (UAV)

This research was accomplished by employing already developed mathematical model of a 6DoF UAV in free space through the motion of the 6DoF aircraft (unmanned aerial vehicle) determined by coordinate systems which allow an aircraft’s position and orientation in space to be kept tracked. The discrete method of the model developed for simulation in Simulink was realized with a suitable transfer function. Then, an artificial neural network model predictive adaptive controller that can handle the nonlinearities associated with the UAV was developed based on state space technique and the model predictive controller (MPC) utilizes a neural network model to envisage future plant (aircraft) responses to potential control signals. The developed model predictive controller network was successfully trained offline using Feed-forward Back-propagation algorithm with speed and position as inputs since the underlying objective of this work is to improve the speed and position in order to advance the safety collision distance of the UAV because these parameters are mostly considered in the avoidance maneuver performance. Also, the results reveal that the speed of the generated UAV is approximately the input speed of 75MS–1 during the time the UAV is affecting maneuvering. Significance of the result is that the proposed algorithm is capable of generating collision-free trajectory for different waypoint cases.

Simulation of submerged granular flows by development of a two-phase incompressible explicit mesh-free method

The interaction between fluid and sediment particles is widely involved in hydraulic engineering problems. In the current study, an explicit incompressible mesh-free method in the framework of the Moving Particle Semi-implicit (MPS) method is proposed to simulate the interaction between the two phases in submerged conditions. The proposed method solves two sets of the continuity and momentum equations, respectively, for the fluid phase and the sediment phase according to the mixture theory. In the method, the pressure and velocity fields are explicitly calculated for the sediment phase with the solid concentration obtained from the mixture theory, in which a rheology model is used to account for the viscosity and shear stress. The governing equations for the fluid are solved using the predictor–corrector time-splitting scheme and the resulting Poisson equation for pressure is calculated using an explicit scheme. In each loop, the discretized points move according to the velocity of the fluid in the mesh-free Lagrangian approach. The proposed method is used to simulate the submerged granular flows including submerged granular column collapse, submerged granular sliding on slopes, and erodible dam-break flow. In the simulations, the parameters of particle distance and collision distance in the numerical method are investigated. It is found that the flow characteristics in the granular flows are reflected by the proposed method and the simulated free surface and water–sediment interface profiles are in good agreement with experimental observations.

The Investigation of the Mixture Formation and Combustion Characteristic of the Spray-wall Impingement

In recent years, investigations into the formation of mixture or combustion characteristics of diesel spray-wall impingement have been summarized. On one hand, the air entrainment strength and fuel concentration distribution in the near-wall area during the spray-wall collision process have been studied. On the other hand, the combustion characteristics of the impinged spray, including ignition and combustion characteristics, have also been discussed. Additionally, the effect of impingement between spray and wall on real diesel engines’ operating condition has been analyzed and summarized. Results showed that increasing the pressure of the injection and shortening the collision distance could enhance the air entrainment strength of spray-wall impingement. Moreover, increasing the injection pressure, shortening the impingement distance, decreasing the environmental pressure, increasing the wall temperature, and reducing the nozzle diameter all improved the mixture formation. Conversely, reducing the environmental temperature, decreasing the nozzle diameter, and increasing the impingement distance all reduced the concentration of soot, and even achieved no soot formation. Under real diesel engine operation conditions, some of the soot formed in the spray-wall collision and deposited on the wall. The structure of these deposits was mainly a simple cluster structure composed of several basic particles, and the porous structure of the deposits was conducive to the oxidation of soot and reduced the deposition rate.

Examining the characteristics between time and distance gaps of secondary crashes

Abstract Understanding the characteristics of time and distance gaps between the primary (PC) and secondary crashes (SC) is crucial for preventing SC ccurrences and improving road safety. Although previous studies have tried to analyse the variation of gaps, there is limited evidence in quantifying the relationships between different gaps and various influential factors. This study proposed a two-layer stacking framework to discuss the time and distance gaps. Specifically, the framework took random forests (RF), gradient boosting decision tree (GBDT) and eXtreme gradient boosting as the base classifiers in the first layer and applied logistic regression (LR) as a combiner in the second layer. On this basis, the local interpretable model-agnostic explanations (LIME) technology was used to interpret the output of the stacking model from both local and global perspectives. Through SC dentification and feature selection, 346 SCs and 22 crash-related factors were collected from California interstate freeways. The results showed that the stacking model outperformed base models evaluated by accuracy, precision, and recall indicators. The explanations based on LIME suggest that collision type, distance, speed and volume are the critical features that affect the time and distance gaps. Higher volume can prolong queue length and increase the distance gap from the SCs to PCs. And collision types, peak periods, workday, truck involved and tow away likely induce a long-distance gap. Conversely, there is a shorter distance gap when secondary roads run in the same direction and are close to the primary roads. Lower speed is a significant factor resulting in a long-time gap, while the higher speed is correlated with a short-time gap. These results are expected to provide insights into how contributory features affect the time and distance gaps and help decision-makers develop accurate decisions to prevent SCs.

GraphDistNet: A Graph-Based Collision-Distance Estimator for Gradient-Based Trajectory Optimization

Trajectory optimization (TO) aims to find a sequence of valid states while minimizing costs. However, its fine validation process is often costly due to computationally expensive collision searches, otherwise coarse searches lower the safety of the system losing a precise solution. To resolve the issues, we introduce a new collision-distance estimator, GraphDistNet, that can precisely encode the structural information between two geometries by leveraging edge feature-based convolutional operations, and also efficiently predict a batch of collision distances and gradients through 25,000 random environments with a maximum of 20 unforeseen objects. Further, we show the adoption of attention mechanism enables our method to be easily generalized in unforeseen complex geometries toward TO. Our evaluation show GraphDistNet outperforms state-of-the-art baseline methods in both simulated and real world tasks.

Probabilistic Prediction of Collisions between Cyclists and Vehicles Based on Uncertainty of Cyclists’ Movements

The uncertainty of cyclists’ movements has a significant impact on predicting the risk of collisions between cyclists and vehicles. The purpose of this study was to provide a method for assessing collision risk using probability, taking into account the uncertainty of cyclists’ movements. A cyclist model was first developed using a first-order Markov model. Then, based on Monte Carlo sampling, the distribution characteristics of the minimum distance and the time-to-collision (TTC) between the vehicle and the cyclist were extracted. By fitting these features, the probability density functions of the collision distance and TTC were estimated to derive the collision probabilities. The effectiveness of the collision probability prediction model was benchmarked against a deterministic crash risk prediction model (autonomous emergency braking [AEB] system) applied to three real-world cases previously reconstructed in an in-depth crash database. The results show that the collision probability prediction model can effectively predict the risk of collisions between cyclists and vehicles with better accuracy than AEB systems using a fixed trigger threshold. This study is a valuable reference for the development of advanced vehicle collision avoidance systems to protect cyclists and other vulnerable road users.

A Position‐Free Path Integral for Homogeneous Slabs and Multiple Scattering on Smith Microfacets

AbstractWe consider the problem of multiple scattering on Smith microfacets. This problem is equivalent to computing volumetric light transport in a homogeneous slab. Although the symmetry of the slab allows for significant simplification, fully analytic solutions are scarce and not general enough for most applications. Standard Monte Carlo simulation, although general, is expensive and leads to variance that must be dealt with.We present the first unbiased, truly position‐free path integral for evaluating the BSDF of a homogeneous slab. We collapse the spatially‐1D path integral of previous works to a position‐free form using an analytical preintegration of collision distances. Evaluation of the resulting path integral, which now contains only directions, reduces to simple recursive manipulation of exponential distributions. Applying Monte Carlo to solve the reduced integration problem leads to lower variance.Our new algorithm allows us to render multiple scattering on Smith microfacets with less variance than prior work, and, in the case of conductors, to do so without any bias. Additionally, our algorithm can also be used to accelerate the rendering of BSDFs containing volumetrically scattering layers, at reduced variance compared to standard Monte Carlo integration.

MODELING OF SPRAY WALL IMPINGEMENT AND FUEL FILM FORMATION UNDER THE GASOLINE DIRECT INJECTION CONDITION

Direct-injection spark-ignition (DISI) engines, which have a better fuel economy than conventional gasoline engines, have been widely introduced in the market. However, in these engines, the rich air−fuel mixtures associated with fuel films during cold starts, caused by spray impingement, produce particulate matter. To predict soot formation, it is important to predict the mixture field precisely; thus, accurate spray and film models are prerequisites for creating a soot model. Previous wall impingement models were well matched with low Weber number collision conditions, such as those of diesel engines, which have relatively high ambient pressures and small Sauter mean diameters. In this study, the outliers of the previous model were observed to decrease as the collision distance increased and when a strong droplet dissipation occurred owing to a high ambient pressure. However, the kinetic energy in DISI engines is considerably larger than the dissipation energy calculated using the Weber number and surface tension; thus, the amount of dissipation energy should be determined within a realistic range. To analyze the two-dimensional (2D) spray-wall impingement phenomenon more accurately, a 2D child droplet generation was considered. Finally, the film and spray behaviors were measured to validate the SNU model. The Mie scattering images of the gasoline spray near the wall were captured to measure the rebound spray radius. Then, a laser-induced fluorescence with a total internal reflection was used to determine the film shape and thickness. Compared with existing models, the SNU model exhibits better agreement with the Mie experimental results without requiring case-dependent changes to the model constant. However, the film simulation part needs improvement in future work.

Event-Triggered Probability-Driven Adaptive Formation Control for Multiple Elliptical Agents

In this article, an event-triggered control scheme is proposed for a group of elliptical agents to achieve a predefined formation. The agents are assumed to have the same dynamics, and the control law for each agent is only updated at its event sequence based on its own minimum collision time and deviation time. The probability-driven controller is designed to prevent the stuck problem among agents. Mapping-adaptive strategy and angle-adaptive scheme based on the minimum collision distance are also developed. Two examples are presented to analyze and demonstrate the effectiveness and potential of the new event-triggered adaptive control algorithm.

An Evaluation Method of Vehicle Functional Safety Controllability Based on Driver-in-the-Loop Platform

Existing vehicle functional safety controllability evaluation methods are difficult to implement in practice and lack quantitative evaluation basis. In this paper, we propose a simulation-based scenario evaluation method, which can achieve verifiable, quantitative, and generalized functional safety controllability evaluation. The method focuses on the driver’s reaction model. By analyzing the driver’s behavior in emergency braking scenarios, we propose the “remaining collision distance” metrics as the controllability criterion. To investigate the reaction time of drivers in different emergencies, we experimented in a driver-in-the-loop platform and classified the scenario factors that might affect controllability. The results show that our method is feasible to evaluate the controllability of vehicle-functional safety in emergency braking scenarios.

Safe Distance Model for Control of Vehicle Emergency Collision Avoidance

The vehicle needs to be able to assist the driver in realizing the control strategy of emergency collision avoidance to ensure the safety of car drivers during driving. This article introduces the basic principles of vehicle emergency collision avoidance using time to collision (TTC) and Berkeley safety distance models. In addition, the Berkeley model was improved so that it could be adjusted for different driving styles. On the simulation platform, simulation experiments were carried out on the driving conditions of the front vehicle at a standstill state, at a constant speed and at a deceleration state. The performance of three collision avoidance strategies were analysed. The results showed that the extroverted driver had the fastest response speed, followed by the moderate driver and the introverted driver; no matter what the working condition, the increase in the driving speed of the driver could reduce the distance between the vehicle and the vehicle in front when the vehicle stopped due to braking, increasing the risk of rear-end collision; no matter what the working condition, the anti-collision strategy that adopted the improved Berkeley model could effectively adapt to different driving styles, keeping a stable distance from the front vehicle when stopping due to braking and the distance between the vehicles was larger than the other two collision avoidance strategies.