Road safety evaluation through automatic extraction of road horizontal alignments from Mobile LiDAR System and inductive reasoning based on a decision tree

Martín-Jiménez, Jose Antonio; Zazo, Santiago; Justel, José Juan Arranz; Rodríguez‐Gonzálvez, Pablo; González‐Aguilera, Diego

doi:10.1016/j.isprsjprs.2018.10.004

Cited by 27 publications

(18 citation statements)

References 58 publications

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“…In terms of road geometry safety, some scholars analyzed the force characteristics of cars under different road geometry conditions and established the mathematical models of car sideslip and rollover. Some scholars analyzed the force characteristics of cars under different road geometry conditions, established the mathematical models of car sideslip and rollover, and analyzed the impact of alignment on traffic safety [ 12 – 17 ]. Scholars have summarized the history of curve linear design in the United States from the two aspects (e.g., flat and vertical curves), especially the setting of easement curves [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

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

Tian

et al. 2021

PLoS ONE

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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.

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Section: Introductionmentioning

confidence: 99%

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

Tian

et al. 2021

PLoS ONE

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“…More specifically, the road surface is extracted with best-fit plane detection algorithms based on robust methods (e.g., RANSAC) or using Principal Component Analysis (PCA) [37]. Road shoulders [38], sidewalks, or road alignment [39] can be extracted using decision trees [40] or fusion of feature-based and deep learning approaches [41]. Another important topic is road marking extraction.…”

Section: Overview Of Automatic Road Extraction Methods From Lidar Datamentioning

confidence: 99%

Roads Detection and Parametrization in Integrated BIM-GIS Using LiDAR

2020

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Building Information Modeling (BIM) has a crucial role in smart road applications, not only limited to the design and construction stages, but also to traffic monitoring, autonomous vehicle navigation, road condition assessment, and real-time data delivery to drivers, among others. Point clouds collected through LiDAR are a powerful solution to capture as-built conditions, notwithstanding the lack of commercial tools able to automatically reconstruct road geometry in a BIM environment. This paper illustrates a two-step procedure in which roads are automatically detected and classified, providing GIS layers with basic road geometry that are turned into parametric BIM objects. The proposed system is an integrated BIM-GIS with a structure based on multiple proposals, in which a single project file can handle different versions of the model using a variable level of detail. The model is also refined by adding parametric elements for buildings and vegetation. Input data for the integrated BIM-GIS can also be existing cartographic layers or outputs generated with algorithms able to handle LiDAR data. This makes the generation of the BIM-GIS more flexible and not limited to the use of specific algorithms for point cloud processing.

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“…There are many lines of research and novel methodologies aimed at the extraction of road elements utilizing LiDAR [5,31]. The extraction of features allows the separate consideration of single objects and permits their evaluation, inventory, among other uses.…”

Section: Feature Extractionmentioning

confidence: 99%

Framework for 3D Point Cloud Modelling Aimed at Road Sight Distance Estimations

et al. 2019

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Existing roads require periodic evaluation in order to ensure safe transportation. Estimations of the available sight distance (ASD) are fundamental to make sure motorists have sufficient visibility to perform basic driving tasks. Mobile LiDAR Systems (MLS) can provide these evaluations with accurate three-dimensional models of the road and surroundings. Similarly, Geographic Information System (GIS) tools have been employed to obtain ASD. Due to the fact that widespread GIS formats used to store digital surface models handle elevation as an attribute of location, the presented methodology has separated the representation of ground and aboveground elements. The road geometry and surrounding ground are stored in digital terrain models (DTM). Correspondingly, abutting vegetation, manmade structures, road assets and other roadside elements are stored in 3D objects and placed on top of the DTM. Both the DTM and 3D objects are accurately obtained from a denoised and classified LiDAR point cloud. Based on the consideration that roadside utilities and most manmade structures are well-defined geometric elements, some visual obstructions are extracted and/or replaced with 3D objects from online warehouses. Different evaluations carried out with this method highlight the tradeoff between the accuracy of the estimations, performance and geometric complexity as well as the benefits of the individual consideration of road assets.

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Road safety evaluation through automatic extraction of road horizontal alignments from Mobile LiDAR System and inductive reasoning based on a decision tree

Cited by 27 publications

References 58 publications

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

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

Roads Detection and Parametrization in Integrated BIM-GIS Using LiDAR

Framework for 3D Point Cloud Modelling Aimed at Road Sight Distance Estimations

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