Potential Field Model Research Articles

A lightweight social cognitive risk potential field model for path planning with dedicated dynamic and static traffic factors

AbstractDriving risk assessment is crucial for autonomous vehicles to guarantee driving safety and traffic efficiency. Risk field models with insufficient consideration of traffic factors are not reliable enough to provide effective support for automated driving tasks, and those highly complex models with numerous uncertain coefficients also limit the execution of automated driving tasks. Inspired by Coulomb's law, this paper proposes a new lightweight social cognitive driving risk potential field model by leveraging interaction forces between charges to explore the effects of dynamic and static traffic factors on driving risks. Through complexity analysis, the number of coefficients in the proposed model was reduced by 36%–50% compared to other models. With parametric analysis and sensitivity analysis, the model's reliability was demonstrated. A path planner was designed by integrating the proposed driving risk field model into a model predictive controller for validating the efficacy of the proposed risk potential field model. The planned path with the proposed risk field model was also compared with existing risk potential field models. Results indicate that the proposed model can effectively account for both dynamic and static traffic factors, thereby supporting the path planner to generate highly adaptable paths for complex traffic scenarios.

Global Simulation of the Solar Wind: A Comparison with Parker Solar Probe Observations during 2018–2022

Global magnetohydrodynamic (MHD) models play an important role in the infrastructure of space weather forecasting. Validating such models commonly utilizes in situ solar wind measurements made near the Earth’s orbit. The purpose of this study is to test the performance of G3DMHD (a data driven, time-dependent, 3D MHD model of the solar wind) with Parker Solar Probe (PSP) measurements. Since its launch in 2018 August, PSP has traversed the inner heliosphere at different radial distances sunward of the Earth (the closest approach ∼13.3 R ⊙), thus providing a good opportunity to study evolution of the solar wind and to validate heliospheric models of the solar wind. The G3DMHD model simulation is driven by a sequence of maps of the photospheric field extrapolated to the assumed source surface (2.5 R ⊙) using the potential field model from 2018 to 2022, which covers the first 15 PSP orbits. The Pearson correlation coefficient (cc) and the mean absolute scaled error (MASE) are used as the metrics to evaluate the model performance. It is found that the model performs better for both magnetic intensity (cc = 0.75; MASE = 0.60) and the solar wind density (cc = 0.73; MASE = 0.50) than for the solar wind speed (cc = 0.15; MASE = 1.29) and temperature (cc = 0.28; MASE = 1.14). This is due primarily to lack of accurate boundary conditions. The well-known underestimate of the magnetic field in solar minimum years is also present. Assuming that the radial magnetic field becomes uniformly distributed with latitude at or below 18 R ⊙ (the inner boundary of the computation domain), the agreement in the magnetic intensity significantly improves (cc = 0.83; MASE = 0.49).

Crustal Architecture and Magmatism of the Campos Rifted Margin: Along‐Strike Variability and Segmentation

AbstractWe investigate the crustal architecture and structural domains of the Campos rifted margin in southeastern Brazil, focusing on rifting evolution and segmentation. Based on 2‐D and 3‐D seismic reflection interpretation and potential field modeling, our results reveal an along‐strike variability, in terms of margin architecture and magmatism, that segments the basin into three main sectors from south to north. Through the analysis of seismic reflection profiles, we interpret this lateral variability as a product of lower‐ to upper‐plate alternations and different magmatic inputs. We propose the Southern Campos margin to represent a lower‐plate tectonic setting, where the distal domain is characterized by the formation of the here named Gávea Supradetachment Basin associated with the development of a metamorphic core‐complex. The Central Campos margin is characterized by a massive residual block, representing an upper‐plate setting, with exhumation of different crustal levels in the distal domain. Our analysis suggests that the Central Campos sustained a high magmatic input throughout the rift evolution, supporting the interpretation of Seaward Dipping Reflectors and a magmatic crust in a transitional domain. The Northern Campos margin also represents an upper‐plate setting, but characterized by a progressively delaminated crust. Finally, we compare the Brazilian margin architecture with the conjugate Angola margin, discuss the role of transfer zones in the segmentation and propose along‐strike variations in the volume of magmatism.

DBN-MACTraj: Dynamic Bayesian Networks for Predicting Combinations of Long-Term Trajectories with Likelihood for Multiple Agents

Accurately predicting the long-term trajectories of agents in complex traffic environments is crucial for the safety and effectiveness of autonomous driving systems. This paper introduces DBN-MACTraj, a probabilistic model that takes historical trajectories and surrounding lane information as inputs to generate a distribution of predicted trajectory combinations for all agents. DBN-MACTraj consists of two main components: a single-agent probabilistic model and a multi-agent risk-averse sampling algorithm. The single-agent model utilizes a dynamic Bayesian network, which incorporates the driver’s maneuvering decisions along with information about surrounding lanes. The multi-agent sampling algorithm simultaneously generates predictions for all agents, using a risk potential field model to filter out samples that may lead to traffic accidents. Ultimately, this results in a probability distribution of the combinations of long-term trajectories. Experimental evaluations on the nuScenes dataset demonstrate that DBN-MACTraj delivers competitive performance in trajectory prediction compared to other state-of-the-art approaches.

Coronal Magnetic-Field Configuration Associated with Pseudostreamer and Slow Solar Wind

The global distribution of the solar wind speed V is closely related to the configuration of the coronal magnetic-field, and the expansion factor f of the flux tube is known as a parameter for determining V. However, the inverse relation between f and V does not hold for pseudostreamers, which separate open-field regions with the same polarity. In the present study, we examined the magnetic-field configuration of pseudostreamers using the potential field (PF) model analysis of magnetograph observations for six Carrington rotations (CRs) in Cycle 23 and compared it with V data derived from interplanetary scintillation observations. We calculated the parameter S, which represents the relative angular distance of foot points on the photosphere magnetically connected to adjacent pixels on the source surface and f from PF model analysis and discriminated areas of helmet and pseudostreamers on the source surface by selecting large values of S. Although the overall correlation between S and V was very poor, helmet and pseudostreamers with large S values were exclusively associated with slow V. Furthermore, helmet and pseudostreamers were associated with large and small values of f, respectively. This suggests that S enables a better discrimination of slow-wind sources associated with pseudostreamers than f. We calculated the distance from the streamer boundary (DSTB) on the source surface using data of helmet and pseudostreamers to compare with V data. Calculated DSTB data exhibited significant correlations with V data except for the solar maximum period. The average of correlation coefficients between DSTB and V over five CRs excluding one at the solar maximum were 0.69, higher than that between the distance from the coronal hole boundary (DCHB) and V. This suggests that DSTB acts as a better parameter for determining V than DCHB. We demonstrated that f for pseudostreamers tended to reach a maximum at a height lower than the source surface (2.5 R⊙). This provides important insight into the formation process of the slow solar wind in pseudostreamers.

Risk-Aware Lane Change and Trajectory Planning for Connected Autonomous Vehicles Based on a Potential Field Model

To enhance the safety of lane changes for connected autonomous vehicles in an intelligent transportation environment, this study draws from potential field theory to analyze variations in the risks that vehicles face under different traffic conditions. The safe minimum vehicle distance is dynamically adjusted, and a comprehensive vehicle risk potential field model is developed. This model systematically quantifies the risks encountered by connected autonomous vehicles during the driving process, providing a more accurate assessment of safety conditions. Subsequently, vehicle motion is decoupled into lateral and longitudinal components within the Frenet coordinate system, with quintic polynomials employed to generate clusters of potential trajectories. To improve computational efficiency, trajectory evaluation metrics are developed based on vehicle dynamics, incorporating factors such as acceleration, jerk, and curvature. An initial filtering process is applied to these trajectories, yielding a refined set of candidates. These candidate trajectories are further assessed using a minimum safety distance model derived from potential field theory, with optimization focusing on safety, comfort, and efficiency. The algorithm is tested in a three-lane curved simulation environment that includes both constant-speed and variable-speed lane change scenarios. Results show that the collision risk between the target vehicle and surrounding vehicles remains below the minimum safety distance threshold throughout the lane change process, ensuring a high level of safety. Furthermore, across various driving conditions, the target vehicle’s acceleration, jerk, and trajectory curvature remained well within acceptable limits, demonstrating that the proposed lane change trajectory planning algorithm successfully balances safety, comfort, and smoothness, even in complex traffic environments.

Total Solar Eclipse White-light Images as a Benchmark for Potential Field Source Surface Coronal Magnetic Field Models: An In-depth Analysis over a Solar Cycle

Potential field source surface (PFSS) models are widely used to simulate coronal magnetic fields. PFSS models use the observed photospheric magnetic field as the inner boundary condition and assume a perfectly radial field beyond a “source surface” (R ss). At present, total solar eclipse (TSE) white-light images are the only data that delineate the coronal magnetic field from the photosphere out to several solar radii (R ⊙). We utilize a complete solar cycle span of these images between 2008 and 2020 as a benchmark to assess the reliability of PFSS models. For a quantitative assessment, we apply the Rolling Hough Transform to the eclipse data and corresponding PFFS models to measure the difference, Δθ, between the data and model magnetic field lines throughout the corona. We find that the average Δθ, 〈Δθ〉, can be minimized for a given choice of R ss depending on the phase within a solar cycle. In particular, R ss ≈ 1.3 R ⊙ is found to be optimal for solar maximum, while R ss ≈ 3 R ⊙ yields a better match at solar minimum. Regardless, large (〈Δθ〉 > 10°) discrepancies between TSE data and PFSS-generated coronal field lines remain regardless of the choice of source surface. However, implementation of solar-cycle-dependent R ss optimal values does yield more reliable PFSS-generated coronal field lines for use in models and for tracing in situ measurements back to their sources at the Sun.

Car-following model based on artificial potential field with consideration of horizontal curvature in connected vehicles environment

Connected vehicles (CVs) will gradually replace traditional vehicles to become the main components of traffic flow. Studying the car-following behavior characteristics is crucial for improving traffic flow stability and safety in CVs environment. Additionally, the radius of road curvature significantly impacts vehicle driving behavior, making it necessary to consider it for the car-following models of CVs. The artificial potential field (APF) theory can more accurately and comprehensively depict various microscopic driving behaviors, offering a new approach for modeling vehicle microscopic behavior. Firstly, this paper constructs the attractive and repulsive potential fields considering horizontal curve curvature based on a road coordinate transformation model. Secondly, an Artificial Potential Field-Based Car-Following Model Considering Curvature (APFCCM) in connected vehicles environment is proposed. Finally, the model is calibrated and validated using the Hangzhou - Xifu Freeway dataset from the Tongji Road Trajectory Sharing (TJRD TS) platform, and compared with the full velocity difference model(FVDM), the Intelligent Driver Model (IDM) and the Driving Risk Potential Field Model (DRPFM). The results show that the APFCCM performs well in trajectory simulation, model accuracy, and scenario adaptability, and it has the lowest mean absolute error(MAE) and root mean square error(RMSE) in position, speed, and acceleration metrics.

ДОСЛІДЖЕННЯ ТА АЛГОРИТМИ ІНТЕГРОВАНОГО КЕРУВАННЯ ГРУПОЮ БЕЗПІЛОТНИХ ЛІТАЛЬНИХ АПАРАТІВ

The main prospects for the group use of unmanned aerial vehicles (UAVs) were analyzed, the problems were identified, and the stages of its solution were proposed. The basic algorithms are analyzed, which are designed to achieve joint and coordinated actions of all elements of the system of integrated control of a group of UAVs, and are used in most approaches to ensure stable interaction between system components. The analysis of the most common mathematical models for controlling groups of UAVs was carried out, in particular: the Reynolds cohesion model, the artificial potential field model, and the leader-following model. The peculiarities of the operation of these models, common and distinctive features, advantages and disadvantages are determined. Based on the considered models and basic interaction algorithms, the algorithms of the components of the UAV group control system are proposed, and the sequence of actions during the interaction between the system components is also described. The main details of UAV group control algorithms are considered. The main aspects of the interaction of the system elements are given, which include messages of various categories used at different stages of the system's operation, in particular: synchronizing messages; group-wide control messages for managing the group as a whole; messages for correcting the flight of a specific UAV; notification of the transfer of statistical information for flight correction; notification of the launch and coordination of the task execution by a specific UAV. The use of joint messages for group management is described, which allows you to relieve the transmission channel, because the flight trajectories are calculated by each UAV individually, according to predetermined criteria. The main stages of the system's operation, the peculiarities of their implementation, the sequence of message transmission in each stage, as well as the basic details of the operation of some system components are described. Block diagrams are provided to display the process of system operation at various stages of flight, with a detailed description of the steps of system operation. The process of collecting statistical data from UAVs for dynamic flight adjustment of the group is described.

A study on lane-changing risk evolution of vehicle group based on potential field model in the freeway tunnel approach section

Objective In the freeway tunnel approach section, lane-changing behaviors and transitions in the driving environment exacerbate traffic flow disruptions, increase driving risks, and lead to a higher accident rate. To this end, this study presents a method to explore the risk evolution process of lane-changing in these sections and evaluate its impact on traffic flow operations surrounding lane-changing vehicles. Methods First, a driving risk potential field model based on the field theory, which consists of a vehicle kinetic potential field and a tunnel illumination potential field, is proposed to evaluate the driving risk. Furthermore, a “vehicle group” risk graph was constructed based on graph theory, incorporating both a node-coupling driving risk model and a topological potential entropy model. Finally, trajectory datasets were collected through naturalistic driving tests to analyze the evolution of lane-changing risk and the stability of the vehicle group. Results From the analysis of coupling driving risk evolution, we found that in the [100, 500) m, [500, 1000) m, and [1000, 1500) m freeway tunnel approach sections, the coupled driving risk of lane-changing vehicle (LCV) was higher than that of the other vehicles in the vehicle group. In different tunnel approach sections, LCVs that received the highest risk were from different vehicles in vehicle group. LCVs received the highest field strength from the risk potential fields of the lateral vehicle (LV), front lateral vehicle (FLV), and front vehicle (FV) in the [100, 500) m, [500, 1000) m, [1000, 1500) m tunnel approach sections, respectively. Based on the absolute value of the vehicle group topological potential entropy, we observed the resilience of the vehicle group system improved with increasing distance from the tunnel entrance. Traffic flow regained stability more quickly after lane-changing disturbances in sections farther from the tunnel entrance. Conclusions This study highlights that the section closer to the freeway tunnel entrance significantly impact lane-changing risk, and it takes longer for the vehicle group to recover its stability after a lane-changing disturbance. The research results offer a theoretical and methodological foundation for enhancing traffic safety measures and developing microscopic driving behavior models for freeway tunnel approach sections.

Ship Trajectory Planning and Optimization via Ensemble Hybrid A* and Multi-Target Point Artificial Potential Field Model

Ship path planning is the core problem of autonomous driving of smart ships and the basis for avoiding obstacles and other ships reasonably. To achieve this goal, this study improved the traditional A* algorithm to propose a new method for ship collision avoidance path planning by combining the multi-target point artificial potential field algorithm (MPAPF). The global planning path was smoothed and segmented into multi-target sequence points with the help of an improved A* algorithm and fewer turning nodes. The improved APF algorithm was used to plan the path of multi-target points locally, and the ship motion constraints were considered to generate a path that was more in line with the ship kinematics. In addition, this method also considered the collision avoidance situation when ships meet, carried out collision avoidance operations according to the International Regulations for Preventing Collisions at Sea (COLREGs), and introduced the collision risk index (CRI) to evaluate the collision risk and obtain a safe and reliable path. Through the simulation of a static environment and ship encounter, the experimental results show that the proposed method not only has good performance in a static environment but can also generate a safe path to avoid collision in more complex encounter scenarios.

Insight into the Evolution of the Eastern Margin of the Wyoming Craton from Complex, Laterally Variable Shear Wave Splitting

Abstract Dense seismic arrays such as EarthScope’s Transportable Array (TA) have enabled high-resolution seismic observations that show the structure of cratonic lithosphere is more heterogeneous and complex than previously assumed. In this study, we pair TA data with data from the Bighorn Arch Seismic Experiment and the Crust and lithosphere Investigation of the Easternmost expression of the Laramide Orogeny (CIELO) to provide unprecedented detail on the seismic anisotropic structure of the eastern margin of the Wyoming Craton, where several orogens emerged from nominally strong cratonic lithosphere during the Laramide Orogeny. In this study, we use the splitting of teleseismic shear waves to characterize fabrics associated with deformation in the Earth’s crust and mantle. We constrain distinct anisotropic domains in the study area, and forward modeling shows that each of these domains can be explained by a single layer of anisotropy. Most significantly, we find a fast direction in the southern part of the Powder River Basin, which we refer to as the Thunder Basin Block (TBB), that deviates from absolute plate motion (APM). This change in splitting behavior coincides with changes in other modeled geophysical observations, such as active source P-wave velocity models, potential field modeling, and seismic attenuation analysis, which all show a significant change moving from the Bighorn Mountains to the TBB. We argue that these results correspond to structure predating the Laramide Orogeny, and most likely indicate a Neoarchean boundary preserved within the lithosphere.

Localized Path Planning for Mobile Robots Based on a Subarea-Artificial Potential Field Model.

The artificial potential field method has efficient obstacle avoidance ability, but this traditional method suffers from local minima, unreasonable paths, and sudden changes in heading angles during obstacle avoidance, leading to rough paths and increased energy consumption. To enable autonomous mobile robots (AMR) to escape from local minimum traps and move along reasonable, smooth paths while reducing travel time and energy consumption, in this paper, an artificial potential field method based on subareas is proposed. First, the optimal virtual subgoal was obtained around the obstacles based on the relationship between the AMR, obstacles, and goal points in the local environment. This was done according to the virtual subgoal benefit function to solve the local minima problem and select a reasonable path. Secondly, when AMR encountered an obstacle, the subarea-potential field model was utilized to solve problems such as path zigzagging and increased energy consumption due to excessive changes in the turning angle; this helped to smooth its planning path. Through simulations and actual testing, the algorithm in this paper demonstrated smoother heading angle changes, reduced energy consumption, and a 10.95% average reduction in movement time when facing a complex environment. This proves the feasibility of the algorithm.

A collision-avoidance decision-making scheme based on artificial potential fields and event-triggered control

In this paper, a collision-avoidance decision-making scheme is proposed based on improved artificial potential field (APF) and event-triggered nonlinear model predictive control. A calculation model is used to calculate the collision avoidance dynamic parameters between ships. In accordance with relevant rules of the International Regulations for Preventing Collisions at Sea (COLREGs), abovementioned collision avoidance parameters are used as the main index and applied to construct the repulsive potential field. Then, an anti-collision decision-making scheme is developed by incorporating event-triggered mechanism and improved APF method into the nonlinear model predictive control algorithm. Based on improved repulsive potential field, heading of the ship and orientation of the target point, the event-triggered conditions are set to save considerable computational resources. Following this approach, optimal collision avoidance actions in line with COLREGs is realised, and the adverse effects of ignoring the manoeuvrability characteristics of ships in the process of preventing collisions are eliminated. Finally, simulation examples verify the effectiveness and rationality of the proposed scheme for multi-ship encounter scenarios.

An Improved Artificial Potential Field Method for Ship Path Planning Based on Artificial Potential Field—Mined Customary Navigation Routes

In recent years, the artificial potential field has garnered significant attention in ship route planning and traffic flow simulation. However, the traditional artificial potential field method faces challenges in accurately simulating a ship’s customary route and navigating experience, leading to significant deviations in prediction results. To address these issues, in this study, we propose an innovative method for simulating and predicting ship traffic flow, building upon the artificial potential field approach. We introduce an AIS track heat map based on the kernel density function and enhance the artificial potential field model by incorporating factors, such as ship navigation habits and ship size. Through a comparison of traffic flow changes before and after the construction of a wind farm, the optimized model demonstrates its effectiveness in improving the accuracy of prediction results.

Evidence of lithosphere erosion in the Eastern Indian shield from multi-scale potential field modelling: geodynamic implications

Evidence of lithosphere erosion in the Eastern Indian shield from multi-scale potential field modelling: geodynamic implications

Uncrewed Boat Path Planning Algorithm based on Evolutionary Potential Field Model in Dense Obstacle Environment

In the trajectory planning of crewless ships, the artificial potential field method is commonly used. The results obtained using the classic potential field model for path design are not optimal and cannot fully meet the trajectory design requirements of uncrewed ships. This paper uses the evolutionary potential field model for trajectory planning. The evaluation formula of the potential path is combined with the differential evolution algorithm to evaluate and optimize the potential. A quadratic optimization smoothing algorithm is designed to limit the maximum turning angle of the uncrewed ship. Simulation experiments show that this method is effective and reliable.

Onset mechanism of an inverted U-shaped solar filament eruption revealed by NVST, SDO, and STEREO-A observations

Context. Solar filaments, also called solar prominences when appearing on the solar limb, consist of dense, cool plasma suspended in the hot and tenuous corona. They are the main potential sources of solar storms. Aims. To understand the onset mechanism of solar filaments, we investigated the eruption process of an inverted U-shaped solar filament and two precursory jet-like activities. Methods. Using observations from the New Vacuum Solar Telescope (NVST), the Solar Dynamics Observatory (SDO), and the Solar Terrestrial Relations Observatory-Ahead (STEREO-A), we investigated the event from two distinct observational perspectives: on the solar disk using NVST and SDO, and on the solar limb using STEREO-A. We employed both a nonlinear force-free field model and a potential field model to reconstruct the coronal magnetic field with the aim to understand its magnetic properties. Results. Two precursor jet-like activities were observed before the eruption, displaying an untwisted rotation. The second activity released an estimated twist of over two turns. During these two jet-like activities, Y-shaped brightenings, which are newly emerging magnetic flux accompanied by magnetic cancellation, and the formation of newly moving fibrils were identified. When these observational features are combined, it can be inferred that these two precursor jet-like activities released the magnetic field that constrained the filament and were caused by newly emerging magnetic flux. Before the filament eruption, it was observed that some moving flows had been ejected from the site as the onset of two jet-like activities, indicating the same physical process as in the two jet-like activities. Extrapolations revealed that the filament was below the height of the decay index of 1.0 and had a strong magnetic field (540 Gauss) and a high twist number (2.4 turns) before the eruption. An apparent rotational motion was observed during the filament eruption. Conclusions. We deduce that the solar filament, exhibiting an inverted U-shape, is a significantly twisted flux rope. The eruption of the filament was initiated by the release of constraining magnetic fields through continuous magnetic reconnection. This reconnection process was caused by the emergence of new magnetic flux.

Research on a local path planning algorithm based on multivehicle collaborative mapping and a potential field method

AbstractTo eliminate blind spots in the field of vision and achieve a safe and collision‐free path, this paper proposes a path planning method based on multivehicle collaborative mapping in the context of vehicle networking. First, a multi vehicle map merging strategy based on the fireworks algorithm is proposed. In this strategy, a dissimilarity objective function based on the concept of grid map similarity is established and an improved fireworks algorithm is used to quickly search for the maximum overlap between local maps, achieving multivehicle collaborative mapping. Second, a real‐time path planning method based on artificial potential field theory is proposed. The information obtained from multivehicle collaborative mapping is first combined with the potential field model to form a multifield coupled road environment model. Then, the obstacle repulsion potential field model is improved to address the issues of traditional artificial potential field methods that target unreachability and poor dynamic response. The feasibility and effectiveness of the collaborative path planning method and single vehicle path planning method are tested through simulation analysis. This paper demonstrates through simulation analysis that the proposed path planning method can effectively achieve beyond line of sight perception and safely and comfortably guide vehicles to complete path planning.

RETRACTED: Potential functions of construction worker–machine interaction safety assessment

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.