Three-dimensional Road Research Articles

Application of IMU/GPS Integrated Navigation System Based on Adaptive Unscented Kalman Filter Algorithm in 3D Positioning of Forest Rescue Personnel.

Utilizing reliable and accurate positioning and navigation systems is crucial for saving the lives of rescue personnel and accelerating rescue operations. However, Global Navigation Satellite Systems (GNSSs), such as GPS, may not provide stable signals in dense forests. Therefore, integrating multiple sensors like GPS and Inertial Measurement Units (IMUs) becomes essential to enhance the availability and accuracy of positioning systems. To accurately estimate rescuers' positions, this paper employs the Adaptive Unscented Kalman Filter (AUKF) algorithm with measurement noise variance matrix adaptation, integrating IMU and GPS data alongside barometric altitude measurements for precise three-dimensional positioning in complex environments. The AUKF enhances estimation robustness through the adaptive adjustment of the measurement noise variance matrix, particularly excelling when GPS signals are interrupted. This study conducted tests on two-dimensional and three-dimensional road scenarios in forest environments, confirming that the AUKF-algorithm-based integrated navigation system outperforms the traditional Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Adaptive Extended Kalman Filter (AEKF) in emergency rescue applications. The tests further evaluated the system's navigation performance on rugged roads and during GPS signal interruptions. The results demonstrate that the system achieves higher positioning accuracy on rugged forest roads, notably reducing errors by 18.32% in the north direction, 8.51% in the up direction, and 3.85% in the east direction compared to the EKF. Furthermore, the system exhibits good adaptability during GPS signal interruptions, ensuring continuous and accurate personnel positioning during rescue operations.

Network-level crash risk analysis using large-scale geometry features

Road traffic crashes are common occurrences that create substantial losses and hazards to society. A complex interaction of components, including drivers, vehicles, roads, and the environment, can impact the causes of these crashes. Due to its complexity, crash identification, and prediction research over large-scale areas faces several obstacles, including high costs and challenging data collecting. This study offers a method for large-scale road network crash risk identification based on open-source data, given that roadways’ horizontal and vertical geometric alignment is crucial in highway traffic crashes. This methodology includes a comprehensive technique for feature extraction from horizontal curves (H-curves) and vertical curves (V-curves) and a novel way of combining the XGBoost model’s attributes with the Harris Hawks Optimization (HHO) algorithm—referred to as the HHO-XGBoost model. Using this model on the road geometry-crash risk dataset developed specifically for this study, the HHO approach adaptively identifies the optimal set of XGBoost hyperparameters and yields favorable outcomes. This study creates a three-dimensional road geometry database that may be utilized for various road infrastructure management, operation, and safety in addition to completing a tiered risk analysis of “region-road-segment” for large-scale road networks. It also offers direction on using swarm intelligence algorithms in integrated learning models.

Influence of the Road Model on the Optimal Maneuver of a Racing Motorcycle

Motorcycle motion is largely influenced by the road geometry, which alters the allowable accelerations in longitudinal and lateral directions and influences the vertical wheel loads. Recently, a method for three-dimensional road reconstruction and its incorporation into transient and quasi-steady-state (QSS) minimum lap time simulations (MLTSs) has been proposed. The main purpose of this work is to demonstrate how significantly different results from a minimum lap time optimal control problem can be obtained when using inappropriate elevation data sources in the track reconstruction problem, and how the road model reconstructed using poor input data can lead to misleading conclusions when analyzing real vehicle and driver performances. Two road models derived from high- and low-resolution digital elevation models (DEMs) are compared and their impact on the optimal maneuver of a racing motorcycle is examined. The essentials of track identification are presented, as well as vehicle positioning on the 3D road and the generalized QSS motorcycle model. Obtained 3D and 2D road models are analyzed in detail, on a case example of the Road Atlanta racetrack, and used in minimum lap time simulations, which are validated by the experimental data recorded on the Supersport motorcycle. The comparative analysis showed that great care should be taken when selecting the elevation dataset in the track reconstruction process, and that the 1 m resolution local DEMs seem to be sufficient to obtain MLTS results close to the measured ones. The example of using the 3D free-trajectory QSS minimum lap time problem to localize the track segments where real driver actions can be improved is also presented. The differences between simulation results on different road models of the same racetrack can be large and influence the interpretation of optimal maneuver.

Beam Tracking Based on a New State Model for mmWave V2I Communication on 3D Roads

<p>The expansion of 5G and Internet of Things has laid a good foundation for the in-depth research of Internet of vehicles. Low frequency resources are scarce, and Internet of vehicles communication requires extremely high communication rate. Millimeter wave can meet the above two requirements, but its characteristics and the complicated communication conditions of Internet of vehicles make it difficult to combine the two. Overcoming these problems and making beam tracking accurate and steady is a major challenge at present. In this paper, a new extended Kalman filter tracking algorithm is proposed for mmWave V2I scenarios. On the basis of the original algorithm, a threshold prediction update mechanism is added. A new scheme is adopted, which takes position and velocity as tracking variables, and the tracking model is derived for the first time in MIMO 3D scenarios based on this scheme. The model considers the three-dimensional road conditions, including the vehicle deflection motion and the millimeter wave link blocked by large vehicles, which is more suitable for practical application scenarios. The simulation results reveal that the position and velocity tracking scheme is superior to the angle and gain tracking scheme, and the tracking error of the proposed algorithm is lower than that of the algorithms using similar state models. Based on the three-dimensional scene, it considers more realistic situations, and is more consistent with the kinematic characteristics of the vehicle and has more practical significance.</p> <p> </p>

A relay-assisted parallel offloading strategy for multi-source tasks in internet of vehicles

Internet of Vehicles (IoV) is paving the road for the new generation of Intelligent Transportation Systems (ITS), and Mobile Edge Computing (MEC) is enabling IoV to efficiently handle the computation-intensive and time-sensitive tasks. However, this has introduced new challenges such as maximizing computing resources, allocating resources fairly for multi-source tasks concurrently, and dividing tasks for parallelly processing to minimize the latency. To face these challenges, a three-dimensional road vehicle mobility model is constructed, and the problem of offloading strategy and resource allocation among multiple vehicles served by one Road Side Unit (RSU) is investigates to minimize the average latency of multi-source tasks while satisfying the quality of service requirements. To address the Non-deterministic Polynomial-time hardness (NP-hardness) of the problem, we design a Relay-Assisted Parallel Offloading (RAPO) strategy to obtain the optimization solution. Extensive experimental results show that the RAPO strategy introducing relay-assisted nodes can enhance performance in poor scenarios and ensure low-latency multi-tasking under various conditions, especially reducing latency by 39% compared to local computing.

Vertical vs. Horizontal Fractal Dimensions of Roads in Relation to Relief Characteristics

This paper investigated the surface length of roads from both horizontal and vertical perspectives using the theory of fractal dimension of surfaces and curves. Three progressive experiments were conducted. The first demonstrated the magnitude of the differences between the planar road length and the DTM-derived surface road length and assessed its correlation with the DTM-calculated road slope. The second investigated the road distance complexity through the fractal dimension in both planar and vertical dimensions. The third related the vertical with the horizontal fractal dimension of roads across a range of distinct physiographic regions. The study contributed theoretically by linking the planimetric complexity to vertical complexity, with clear applications for advanced transportation studies and network analyses. The core methodology used geographic information systems (GIS) to integrate a high resolution (1 × 1 m) digital terrain model (DTM) with a road network layer. A novel concept, the vertical fractal dimension of roads was introduced. Both the vertical and horizontal fractal dimensions of the roads were calculated using the box-counting methodology. We conducted an investigation into the relationship between the two fractal dimensions using fourteen study areas within four distinct physiographic regions across Slovenia. We found that the average slope of a three-dimensional (3D) road was directly related to the length difference between 3D and two-dimensional (2D) roads. The calculated values for the vertical fractal dimension in the study areas were only slightly above 1, while the maximum horizontal fractal dimension of 1.1837 reflected the more sinuous properties of the road in plan. Variations in the vertical and horizontal fractal dimensions of the roads varied between the different physiographic regions.

Research on the 3D Fine Modeling Method of In-Service Road

To achieve the digitization of all traffic infrastructure elements and enable three-dimensional digital representation of physical facilities, a multilevel road three-dimensional reverse modeling method is proposed based on road point cloud data obtained by a vehicle laser scanning system. First, based on the distribution characteristics of each target structure in the road scene and the modeling requirements, a levels of detail (LOD) modeling specification is designed, and the required feature data format for each level is defined. Next, the three-dimensional characteristic parameters needed for modeling are extracted from the vehicle point cloud data. Finally, the continuous quadrilateral algorithm is used to reconstruct the road model, the topological structure relationship algorithm is used to reconstruct the intersection model, and the instantiation lofting technology is used to reconstruct the rod-shaped target model, all based on the three-dimensional modeling platform. This approach allows for the rapid reverse reconstruction of three-dimensional road models with different LOD levels. The point cloud data of two sections of urban roads with different slopes and one section of expressways were modeled and compared with the original vehicle-mounted laser point cloud data. The data volume of different level models decreases with decreasing model fineness, and the LOD1 level model has the highest similarity with point cloud data, at approximately 92.17%. The similarity of LOD2 and LOD3 decreases in turn, at 82.91% and 75.25%, respectively. For flat or undulating roads, the overall accuracy of the nearest point distance between different levels of road models and point cloud data is better than 10 cm, significantly higher than that of traditional manual modeling. The results demonstrate that vehicle mobile laser scanning technology provides new modeling data for the rapid realization of three-dimensional reverse reconstruction of large-scale traffic infrastructure. Automatic modeling technology can effectively improve modeling efficiency, reduce data redundancy, and ensure model quality.

Comparative analysis of Road Scanning Techniques

A three-dimensional road point cloud is not only useful for civil engineers (road rehabilitation, road condition assessment) but can also be useful for vehicle engineers (autonomous vehicle driving scenario, vehicle dynamics simulation). Currently, there are several scanning techniques can be used to obtain these point clouds, such as terrestrial laser scanning (TLS), mobile laser scanning (MLS), airborne laser scanning (ALS), unmanned aerial vehicle (UAV) photogrammetry or UAV laser scanning. This paper discusses the investigation of four road surface scanning techniques by comparing their point clouds and the derived products. The comparison was performed for a section of a road with 1136 m length and 4 m width, the TLS survey provided the reference data. Aspects of point cloud evaluation included geometric accuracy, density, and the parameters of plane-fitting. CRG models were created from all studied point clouds to compare the difference between the final products to be used by the automotive industry. The results show that the MLS and the UAV photogrammetry generated the most accurate point cloud, while UAV laser scanning accuracy was the lowest. Similarly, the CRG models comparison showed that there was no significant difference between MLS and TLS models, and the UAV photogrammetry gave a smoother variation relative to the reference surface. Whereas the largest differences were noted for the CRG model derived from the UAV laser scanning models.
 

Integrated Adhesion Coefficient Estimation of 3D Road Surfaces Based on Dimensionless Data-Driven Tire Model

The tire/road peak friction coefficient (TRPFC) is the core parameter of vehicle stability control, and its estimation accuracy significantly affects the control effect of active vehicle safety. To estimate the peak adhesion coefficient accurately, a new method for the comprehensive adhesion coefficient of three-dimensional pavement based on a dimensionless data-driven tire model is proposed. Firstly, in order to accurately describe the contact state between the three-dimensional road surface and the tire during driving, stress distribution and multi-point contact are introduced into the vertical dynamic model and a new tire model driven by dimensionless data is established based on the normalization method. Secondly, the real-time assessment of lateral and longitudinal adhesion coefficients of three-dimensional pavement is realized with the unscented Kalman filter (UKF). Finally, according to the coupling relationship between the longitudinal tire adhesion coefficient and the lateral tire adhesion coefficient, a fuzzy reasoning strategy of fusing the longitudinal tire adhesion coefficient and the lateral tire adhesion coefficient is designed. The results of vehicle tests prove that the method proposed in this paper can estimate the peak adhesion coefficient of pavement quickly and accurately.

Research on Dynamic Visual Simulation of Hydropower Project Construction Based on Virtual Reality

In this paper, the basic theories and algorithms of virtual reality technology are studied, and the key technologies for drawing outdoor large-scale virtual scenes, realistic terrain simulation, virtual sound field simulation, and traffic layout in the construction site, three-dimensional road design, and dam construction simulation Visualization has been studied, and the system simulation information and graphics are closely combined to provide a new environment for the design and management of water conservancy and hydropower projects, and to provide decision-makers with a more scientific and intuitive basis.

An Automatic Approach to Extracting Large-Scale Three-Dimensional Road Networks Using Open-Source Data

3D road networks are amongst the indispensable elements of a smart city, which has been explored in various ways. However, researchers still faces challenges extracting 3D networks on a large scale. The global digital surface models (DSMs) with relatively high spatial resolution make it possible to extract 3D road networks. Nevertheless, the complete and accurate elevation of road networks cannot be obtained directly because of the limitation in sensors on the DSM production platform. Thus, we proposed a novel approach to extract large-scale 3D road networks, integrating terrain correction and road engineering rule constraint, by using the Advanced Land Observing Satellite World 3D-30 m DSM, OpenStreetMap and FABDEM. The simplification and terrain correction algorithm were applied to remove most of the edges with excessive grades and reduced the negative impact of the built-up environment in DSM on the extraction accuracy. Moreover, the tunnel parts of the 3D road networks were refined based on road engineering standards. Nanjing of China, Aalborg of Denmark and Los Angeles of the United States are selected as study areas. Using 3D road networks from unmanned aerial vehicle photogrammetry, light detection and ranging and Google Earth as references, we validated the road elevation accuracy of our method and obtained an overall root-mean-square error of 3.80 m and a mean absolute error of 1.94 m. The 3D topology of interchanges with different radii was reconstructed completely. Overall, our work is an endeavour to utilise multiple open-source data to extract large-scale 3D road networks and benefits future research related to smart city reconstruction and 3D urban analysis.

An Attention-Based Digraph Convolution Network Enabled Framework for Congestion Recognition in Three-Dimensional Road Networks

Congestion recognition is necessary for vehicle routing, traffic control, and many other applications in intelligent transportation systems. Besides, traffic facilities in the three-dimensional road network, which contains the fundamental spatiotemporal features for congestion recognition, provides multi-source traffic information. To exploit these traffic big data, in this paper, we propose an attention mechanism-based digraph convolution network (ADGCN) enabled framework to tackle the congestion recognition problem. It can be divided into two parts, spatial relevance modeling and temporal relevance modeling. At first, the representation incorporates spatiotemporal traffic information with the three-dimensional urban network, and partially decouples the global network topology to a single-knot digraph. Then a digraph-based convolution network is used to capture high-order spatial features. Finally, to proceed with time-series features, the multi-modal attention mechanism is introduced to catch the long-range temporal dependence and the congestion classifier is defined accordingly. This distinguishes the proposed model from the conventional congestion recognition methods. Comprehensive experiments are conducted based on real traffic data. The results demonstrate the advantages of the proposed framework over the existing spatiotemporal analysis methods.

Research on Tire/Road Peak Friction Coefficient Estimation Considering Effective Contact Characteristics between Tire and Three-Dimensional Road Surface

In the field of transportation, the accurate estimation of tire–road peak friction coefficient (TRPFC) is very important to improve vehicle safety performance and the efficiency of road maintenance. The existing estimation algorithms rarely consider the influence of road roughness and road texture on the estimation results. This paper proposes an estimation method of TRPFC considering the effective contact characteristics between the tire and the three-dimensional road. In the longitudinal and lateral directions of tire–road contact, the model is optimized by incorporating the effective contact area ratio coefficient into the LuGre tire model. The optimized model characterizes the road texture by road power spectrum and establishes the relationship between road texture and tire force. In the vertical direction of tire–road contact, the force transfer between tire and road is represented by a new multi-point contact method. By combining the above models with the normalization method and the unscented Kalman filter (UKF), the timely and accurate estimation of the peak friction coefficient of the tire and the 3D road is realized. Simulation and real vehicle experiments verify the effectiveness of the estimation algorithm.

도로공간의 입체적 활용을 위한 입체도로제도 적용 개발사례 분석 : 일본 입체도로제도를 적용한 개발사례를 중심으로

Purpose: Development pressure is increasing in urban areas with high population density. Therefore, the range of available land in the city center is being expanded vertically. This study investigates and analyzes development cases applying the Japanese three-dimensional road system. Method: A comprehensive analysis was conducted focusing on development cases presented by the Ministry of Land, Infrastructure and Transport in Japan. In particular, cases that meet the domestic urban development conditions and have high potential for domestic application were selected for analysis. As for the research method, application of related laws such as structural form of development cases, road law, building standards law, and urban planning law were analyzed. In addition, the relationship between the rights of road operators and building operators of land and buildings was analyzed. Result: The application and development types of the three-dimensional road system are diverse in the form of integrated and separate structures such as overpasses, underpasses, free passages, parking lots, and monorails. The development method enables the integrated maintenance of roads and buildings, increasing the degree of freedom of development planning. Regarding the rights relationship, it was investigated that in the case of a separate structure, the road operator has the separate superficial right, and the building operator has the ownership of land and buildings. In addition, in the case of an integrated structure, it was investigated that both road and building operators have the necessary rights, and the building operator has the ownership of the building.

A review of road models for vehicular control

Over the last 25 years a number of road models have been developed for use in a variety of vehicular control problems. These models are important, because they dictate the force-generating capabilities of the tyres, as well as constrain the movement of the vehicle itself. Early road models used two-dimensional (2D) ‘flat road’ representations, the advantages and deficiencies of which are well understood. Once it became apparent that three-dimensional effects can be important in limit-performance studies, ribbon-based three-dimensional (3D) road models were developed. The large lateral camber variations on highly-banked NASCAR tracks highlighted deficiencies in ribbon-based road representations that required correction. Upgraded models addressing these deficiencies were only developed recently. The purpose of this paper is to review and compare a number of road models – particularly those developed for use in racing studies. Comparative computed results are provided that hight some of the similarities and differences between these models.

Research on Mechanical Response of Polymer Grouting Repair for Longitudinal Cracks in Reconstructed and Expanded Expressway Pavement

Polymer grouting materials have been widely used in road repair projects. In order to study the application of polymer grouting materials in repairing the joint cracks and voids of new and old subgrades in the reconstruction and expansion projects of expressways. According to the phenomenon investigation data, ABAQUS finite element software was used to establish a three-dimensional road mechanical response analysis model, and the mechanical response characteristics of the road before and after polymer grouting were systematically analyzed. The research results of the road which was repaired by polymer grouting show that the shear force between the polymer grouting material and the materials on both sides of the old and new subgrades can repair the cracks at the joint and the diseases in the void area of the new subgrade, so as to reduce the displacement and settlement of the road surface. The maximum dynamic stress peak value of the old and new subgrade road surfaces is located at the load action position, and with the increase of the distance from the load action position, the dynamic stress peak value gradually decreases, showing an M-shaped distribution as a whole. The original stress transfer law of the road structure was changed, so that the dynamic stress of the new subgrade is significantly smaller than that of the old subgrade. The critical surface formed between polymer grouting material and subgrade material has a certain shear strength, which reduces the tensile stress and strain between structural layers. It will improve the bearing capacity of the subgrade and at the same time provide a practical reference for the treatment of longitudinal cracks in the reconstruction and expansion of expressway.

Modest flooding can trigger catastrophic road network collapse due to compound failure

Compound failures occur when urban flooding coincides with traffic congestion, and their impact on network connectivity is poorly understood. Firstly, either three-dimensional road networks or the traffic on the roads has been considered, but not both. Secondly, we lack network science frameworks to consider compound failures in infrastructure networks. Here we present a network-theory-based framework that bridges this gap by considering compound structural, functional, and topological failures. We analyze high-resolution traffic data using network percolation theory to study the response of the transportation network in Harris County, Texas, US to Hurricane Harvey in 2017. We find that 2.2% of flood-induced compound failure may lead to a reduction in the size of the largest cluster where network connectivity exists, the giant component, 17.7%. We conclude that indirect effects, such as changes in traffic patterns, must be accounted for when assessing the impacts of flooding on transportation network connectivity and functioning.

Pavement Surface Condition Index Prediction Based on Random Forest Algorithm

With the rise of three-dimensional road detection technology, more and more roads have begun to use three-dimensional road detection vehicles to detect road conditions. The pavement surface condition index (PCI), an important indicator of pavement performance, was used as the research target, and the PCI used the actual pavement data collected by ARAN9000 three-dimensional, multifunctional road detection vehicle. First, the data mining technology is used to consider the factors such as pavement distress, environment, and pavement structure, and to process and analyze the data related to the pavement surface condition index of a certain highway in Ontario, Canada, such as data cleaning and feature screening. Then, a machine learning prediction model of a pavement surface condition index was constructed, and the complex correlation coefficients (R2) of the multiple linear regression model, the neural network model, and the random forest model were 0.562, 0.711, and 0.895. Compared with the neural network model, the accuracy of the random forest model in predicting the pavement surface condition index was improved by 0.184, the error was reduced by 1.599, and the training speed was improved by 33 s. Finally, the random forest model with high precision is selected for optimization. Due to the large number of input variables, it is impossible to determine which line of data is the outlier through simple statistical analysis, so after establishing and predicting the model, the outlier is determined and deleted through the fitting effect between the predicted value and the real value. Then the modified data was used to retrain the model to achieve the optimal training of the current model. The results show that the prediction efficiency and accuracy of the improved random forest model are higher, and the R2 reaches 0.898. The proposed pavement condition index prediction model is accurate and effective, and the prediction results can assist the maintenance department to make more scientific and reasonable maintenance decisions.

Novel Framework for 3D Road Extraction Based on Airborne LiDAR and High-Resolution Remote Sensing Imagery

3D GIS has attracted increasing attention from academics, industries, and governments with the increase in the requirements for the interoperability and integration of different sources of spatial data. Three-dimensional road extraction based on multisource remote sensing data is still a challenging task due to road occlusion and topological complexity. This paper presents a novel framework for 3D road extraction by integrating LiDAR point clouds and high-resolution remote sensing imagery. First, a multiscale collaborative representation-based road probability estimation method was proposed to segment road surfaces from high-resolution remote sensing imagery. Then, an automatic stratification process was conducted to specify the layer values of each road segment. Additionally, a multifactor filtering strategy was proposed in consideration of the complexity of ground features and the existence of noise in LiDAR points. Lastly, a least-square-based elevation interpolation method is used for restoring the elevation information of road sections blocked by overpasses. The experimental results based on two datasets in Hong Kong Island show that the proposed method obtains competitively satisfactory results.

A simulation based large bus side slip and rollover threshold study in slope-curve section under adverse weathers.

To study the side slip and rollover threshold of large bus in slope–curve section under adverse weather, factors that affect the safety of large buses that run in slope–curve section, such as rain, snow, cross-wind environmental factors, and road geometry, were analyzed to obtain the friction coefficient of the road surface under different rainfall and snowfall intensities through field measurements and to determine the six-component force coefficient of wind that acts on large buses through wind tunnel tests. The force analysis of large bus in slope-curve section was carried out, and the mechanical equations of large bus under the limit conditions of sideslip and rollover in slope-curve section were established. TruckSim simulation test platform was used to establish a three-dimensional road model and large bus mechanical model at a design speed of 100 km/h. Input parameters, such as cross-wind speed and road friction coefficient, simulate the impact of wind-rain/snow coupling. Under the combined action of wind-rain/snow, the operation test of large bus in slope-curve section was carried out, and the key parameters and indicators of the sideslip and rollover of large bus in slope-curve section were outputted and analyzed. The sudden change point of lateral acceleration is the judging condition for sideslip of large bus in slope-curve section under different road friction coefficient (0.2–0.7), changing from 0.15m/s2 and stabilizing to 0.52 m/s2, and a 0N vertical reaction force of the inner tire is the critical judging condition for rollover under road friction coefficient0.8, and the operating speed thresholds were proposed under different road friction coefficient. This study is expected to provide theoretical support for the speed limit of large bus in slope-curve section under adverse weather.