A literature synthesis of LiDAR applications in transportation: feature extraction and geometric assessments of highways

Figures & data

Figure 1. LiDAR point cloud of highway segments.

Figure 2. Applications of mobile laser scanning in transportation [Adopted from (Olsen et al. 2013)].

Figure 3. Transportation features extracted from LiDAR in previous work.

Table 1. A summary of previous studies that attempted the extraction of road surface, lane marking, and road edges from LiDAR.

Author(s), Year	Location	Focus	Procedure	Findings
Lam et al. (2010)	Ontario, Canada	Extraction of urban roads surface and roadside objects	Dynamic Extraction using Kalman filters and RANSAC.	Results were satisfactory
Wang et al. (2012)	Xi amen, China	Extraction of urban roads surface	Hypothesis testing based on local variance of altitude and mean value of local altitude	Satisfactory results, however not much discussion
Zhou and Deng (2014)	China	Curbstone Detection using Airborne LiDAR data	(i) Identifying abrupt change in height. (ii) Fitted sequentially arranged points to sigmoidal function. (iii) Closing gaps between nearby and collinear line segments	Completeness varied from 53% to 92%
Zhang (2010)	US	Real-time extraction of road surface and road edges	Involves using elevation filterers and pattern recognition techniques	False alarm rate of 0.83% and a miss rate of 0.55% were reported
Kumar et al. (2013)	Williamstown, Ireland	Road edge extraction	Image segmentation techniques using a combination of gradient vector flow (GVF) and balloon parametric active contour models	Was accurate in all but two instances
Kumar et al. (2014)	Williamstown, Ireland	Road markings extraction from LiDAR data	Range dependent thresholding to the LiDAR intensity values and binary morphological operations	80/93 lane markings were correctly detected
Guan et al. (2014)	Xiamen, China	Lane Marking Extraction form MLS LiDAR	Height difference used to detect road edges. Range dependent thresholding and application of morphological processes used to extract lane markings.	Completeness of 96% and correctness of 83%
Thuy and León (2010)	Karlsruhe, Germany	Real-time Lane detection	Plotting the probability density function for reflectivity of data points. Dynamic thresholding based on standard deviation. Image binarization and canny filters.	Results of lane detection not discussed
Yan et al. (2016)	Beijing, China	Road marking extraction	Sequential ordering of LiDAR points Chronologically. Edge Detection and Edge Constraints (EDEC).	Completeness of 96%, correctness of 93%
Yadav, Singh, and Lohani (2017)	India	Road Surface Extraction	(i) Data structuring and ground filtering, (ii) road surface extraction, and (iii) road boundary refinement	Average completeness of 93.8%, correctness of 98.3%, and quality of 92.3%.
Tsai and Li (2012)	Georgia, US	Detection of pavement cracks	Segmentation using dynamic optimization method and a linear-buffered Hausdorff scoring method.	Cracks with widths of 2mm and thicker can be efficiently detected in a controlled environment.

Table 2. A summary of previous studies that attempted the extraction of traffic sign information from LiDAR.

Author(s), Year	Location	Focus	Procedure	Findings
Vu et al. (2013)	US	Real-time identification and classification of traffic signs	Filtering of data points based on intensity and Principle Component Analysis (PCA) of sign cluster.	Success rates ranging from 84 to 96%, although the limitation of the study is that it was applied at a test track
Weng et al. (2016)	Xiamen, China	Traffic sign detection and classification	Filtering based on intensity, hit count, and elevation. Classification attempted based on the geometric shape and pairwise 3D shape context	Success rate is not discussed, but some false positives are detected
Ai and Tsai (2014a)	Georgia, US	Traffic sign detection in Urban and Rural Setting	Filtering based on intensity, hit count, MUTCD elevation, and offset values. Trimble T3D software for sign detection.	I-95 highway: 94% detection, 6 false positives 37^th street: 91.4% detection, 7 false positives
Soilán et al. (2016)	Galicia, Spain	Traffic sign detection in Urban and Rural Setting	Non-ground segmentation, intensity and lateral offsets filters. Utilizes a Gaussian mixture model, a cluster algorithm and PCA.	86.1% for an urban road and 92.8% for a highway
Riveiro et al. (2016)	Spain, Portugal, Brazil	Traffic sign detection and classification	Similar to Soilán et al. (2016). Filter points by intensity and using Gaussian mixture models, and clustering and PCA. Contour recognition using a linear regression model based on a raster image used for shape-based classification	The method was tested on data from three different countries. Success rates ranged from 80 to 90%.
Gargoum et al. (2017)	Alberta, Canada	Traffic sign detection and mapping	Intensity filtering, Density Based Clustering, Geometric Filtering, and Buffer Zone filtering.	Success rates ranging from 94% to 100% on three different highways
Ai and Tsai (2016)	Georgia, US	Automatic assessment of traffic sign reflectivity conditions and sign detection	(i) Traffic signs are first extracted, and colour segmented. (ii) LiDAR points are linked with image pixels. (iii) Intensity values are normalized. (iv) intensity values are compared to retro-reflectivity standards	Detection rate of 85% Reflectivity values obtained from LiDAR consistent with values obtained from handheld retroreflectometer.

Figure 4. Mapped sign clusters extracted from a LiDAR scan of one of the test highways in Gargoum et al. (2017).

Table 3. A summary of previous studies that attempted the extraction of other roadside objects from LiDAR.

Author(s), Year	Location	Focus	Procedure	Findings
Wu et al. (2013)	Shanghai, China	Tree extraction	Voxelization, calculating voxel attributes, searching and marking neighbourhoods, extracting potential trees, and morphological elimination of non-trees	Tested on 300m flat segment with completeness and correctness of over 98% reported.
Lehtomäki et al. (2010)	Espoo, Finland	Extract and classify roadside objects	Scan-line based algorithm where poles exist as sweeps. Objects are clustered and PCA is done. Further classification can distinguish poles from trees.	77.7% detection rate and 81.0% correctness rate were reported.
Pu et al. (2011)	Enschede, the Netherlands	Extraction of Roadside object from LiDAR	Surface growing algorithm used to identify ground. Common feature of a vertical principal axis used to identify nonground. Quartile-based assessment used to classify nonground objects.	Certain objects such as poles had an 86% accuracy. Traffic signs were at 61% and trees at 64%
Cabo et al. (2014)	Galicia, Spain	Extraction of pole-like objects from MLS LiDAR	Point cloud voxelisation followed by (i) Segmentation of connected horizontal elements, (ii)selection of elements greater than maximum area criteria, and (iii)selection of elements by isolation criteria.	Average completeness of 92.3% and a correctness of 83.8%.
Lehtomäki et al. (2016)	Espoo, Finland	Classification and Recognition of roadside objects from LiDAR.	Nonground extraction, segmentation, segment classification, object location estimation, and machine learning based object classification.	Recall rate for the six object classes ranged between 66.7% and 94.3%.
Teo and Chiu (2015)	Taipei City, Taiwan	Detection of pole-like objects using MLS LiDAR	(i) Data processing where data trajectories were reorganized into road elements. (ii) Voxel-based Coarse-to-fine segmentation. (iii) Final detection.	Effective in detecting pole-like objects at a rate of 90%
El-Halawany and Lichti (2011)	Ottawa & Montreal, Canada	Detecting road poles from mobile LiDAR	2D density-based segmentation, vertical region growing, segment merging, and pole classification	Average detection rate and accuracy for three datasets tested were 86% and 97%

Table 4. A summary of previous studies that attempted the extraction of road alignment information from LiDAR.

Author(s), Year	Location	Focus	Procedure	Findings
Zhang and Christopher Frey (2006),	North Carolina, US	Vertical grades estimation using LiDAR	Used least squares regression analysis to estimate the elevation of points along the	Level of accuracy of up to 5% was reported. Only works for straight segments.
Higuera de Frutos and Castro (2017)	Madrid & Toledo, Spain	Reconstruction of road vertical profiles using GNSS data	Classification of points along the profile into grade points, parabolic curve points or border points and clustering points based on their type. Analytic expressions of the points and their integrals were estimated.	A mean error of less than 8cm was reported when estimating the geometric elements of a road’s vertical profile
Holgado‐Barco et al. (2015)	Spain	Horizontal alignment extraction from LiDAR	Segmenting trajectory point, classifying points into curved and straight segments based on changes in azimuth. Parametrizing curve attributes.	Errors of up to 2.0% in length and 0.4% for circular arc radius. For simulated segment the errors were 0.1% and 1.1% for arc radius and length, respectively.
Gargoum, El-Basyouny, and Sabbagh (2018b)	Alberta, Canada	Automatically detect and extract attributes of horizontal curves from LiDAR.	Analyzing changes in the azimuth along the road to detect curves. Linear regression analysis used to identify the point of intersection and deflection angle. Orthogonal linear equations identified to estimate centre point and measure radius.	Detection successful on all test curve. Attributes of the curves were estimated with an average percent difference of less than 3%.
Tsai et al. (2013)	Georgia, US	Extracting road cross slopes from LiDAR	The region of interest is user defined based on scanner angle and lane markings. Cross slopes estimated using linear regression.	Tested in a controlled environment, results were within 0.28% of the digital level measurement.
Holgado‐Barco et al. (2017)	Spain	Extracting road cross section to determine slope, lane widths and number of lanes	Road segmentation using adaptive height threshold and scanner angle. Intensity and geometric filters are applied. PCA is used.	Comparing multiple extractions on same roads; only slight variations existed along the road segment.
Gargoum, El-Basyouny et al. (2018)	Alberta, Canada	Fully automated method to extract cross slopes and side slopes	Estimation of vectors normal to roads axis. Cross section point extraction. MARS regression to estimate point of change in slope. Linear regression to estimate slopes.	Comparison to slope measurements obtained in GPS survey yielded differences between 0.08% to 0.22%.

Table 5. A summary of previous studies that attempted assessing sight distances using LiDAR.

Author(s), Year	Location	Focus	Procedure	Findings
Castro et al. (2011)	Spain	Sight distance using LiDAR	Using ArcGIS. Created a digital terrain model (DTM) which along with the input computes viewsheds and geometrically analyses it.	No statistically significant differences in sight distance values compared to commercial design software
Castro et al. (2013)	Spain	Sight distance using aerial LiDAR	Using ArcGIS to create a DTM of the LiDAR highway scanned aerially. Visibility is assessed using Line of Sight (LOS) tools. Available Sight Distance Calculated	No significant differences between sight distance values estimated using the proposed method and previous study
Tsai, Yang, and Wu (2011)	Georgia, US	Intersection Sight Distance assessment using LiDAR	Uses aerial LiDAR data. Sight distance triangles are determined and overlayed over the aerial DSM.	Effective in determining 92% of obstructions,
Gargoum, El-Basyouny, and Sabbagh (2018a)	Alberta, Canada	Sight distance using mobile LiDAR. Stopping and Passing sight distance assessed	Creation of a DSM using filtering points representing observers and targets while adjusting elevations. Visibility is assessed using LOS tools.	Proposed method tested on multiple segments, one flat and straight to verify results. Also tested on a long 50 km segment.

Table 6. A summary of previous studies that attempted clearance assessment using LiDAR.

Author(s), Year	Location	Focus	Procedure	Findings
Liu, Chen, and Hasuer (2012)	North Carolina, US	Vertical clearance assessment using static LiDAR scans.	Algorithm matches ground points to bridge points based on distance.	Results were compared to field measurements to verify the existence of defects in the bridge structures.
Puente et al. (2016)	Galicia, Spain	Semi-automated vertical clearance assessment in tunnels	Cross sections extraction along the trajectory, edge definition using lane markings, clearance assessment.	Relative error between ground truth and detected clearance did not exceed 1% for most cross sections
(Gargoum, Karsten, et al. 2018)	Alberta, Canada	Automated detection of overhead objects on LiDAR highways	Mapping roads trajectory, nonground segmentation, kNN search, density-based clustering, classification and clearance assessment.	Tested on three highways. Algorithm was able to detect all objects and accurately assess clearance.
Gargoum, Karsten, and El-Basyouny (2018).	Alberta, Canada	Network level assessment of clearance for issuing over-height permits	Similar procedure to (Gargoum, Karsten, et al. 2018)	Efficiently tested on three long highway corridors.

Table 7. Common techniques used in previous research to process LiDAR point clouds for transportation applications.

Technique	Description
Region Growing	Region-based segmentation method; either 2D or 3D
k-Nearest Neighbours	Nonparametric supervised machine learning algorithm that is used for classification and purposes.
Principal Component Analysis	Supervised dimension reduction algorithm that identifies the dimensional spread of observations
Support Vector Machines	Supervised machine learning algorithm which can be used for classification and regression analysis
Convolutional Neural Network	Supervised machine learning technique used for data classification; layered structure of convolutional and pooling steps to extract global and local features about 2D or 3D data;
Gaussian Mixture Models	Probabilistic unsupervised machine learning algorithm used for clustering data.
k-means	Unsupervised machine learning algorithm used for clustering data.
DBSCAN	Density-based clustering algorithm that works on grouping points based on proximity and hit count
Skewness and kurtosis balancing	Unsupervised segmentation based on the statistical distribution of points
Spline Regression	Piece-wise polynomial regression analysis of pieces defined by a sequence of knots
RANSAC	An iterative method used to fit robust models to data containing outliers; linear, planar
Voxelization	Discretizing data into three dimensional grid cells of a specified size
Octree	Discretization method which connects voxels of different scales, where a voxel contains 8 equally sized voxels
KD-Tree	Data partitioning structure; typically used for nearest neighbor searches
Rasterization	Converting vector data (shape) into a raster representation (pixels)
Convex Hull – gift wrapping	Smallest bounding shape surrounding an object in 2D/3D space; used to determine object area and/or volume
Active contour model	Framework for delineating an object outline from a possibly noisy 2D image.
Dilation	Image smoothing technique that works on filling small pixel gaps within an image
Erosion	Image processing technique that works on removing noisy pixels within an image
Canny edge detection	Image intensity edge detection operator, typically approximated as the derivative of the Gaussian
Connected Component Labelling	Image processing technique used to detect connected regions in digital images by analyzing image pixels

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