• Reconstruction of the road network to assist the finding of conflict areas between vehicles and sidewalk passengers.. Particularly, following missions are target problems in this study
Introduction
Research Background
In recent years, the field of automated or self-driving vehicles has caught considerable attention of many researchers with its advantages Automated vehicles could have benefit in not only significantly increasing safety on the road, but also decreasing of traffic congestion and gas emission consumption [1] In automated driving systems, a sequence of actions is performed automatically such as sensing surrounded environment and deciding vehicle operation without involvement of human driver Therefore, it is essential for vehicles to understand their location and driving environment to make an appropriate driving plan
The high-definition (HD) map is certainly one of the key technologies for all major automated driving projects [2] The HD map can provide precise definition of road connected with semantic information for high-precision localization in 3D spaces [3], understanding the surroundings and making prediction for other vehicles’ movement [4], maneuver themselves [5] In response to developments in the field of automated driving The maps need to be refined more and more precisely to meet the high-quality requirements For highly automated driving, vehicles have to understand complex environment about not only their lanes, but also other elements of map such as sidewalks, markings, traffic sign, … For the purpose of prediction of other road users, it is required for vehicles to get knowledge of all lanes where they can move to, especially true for sidewalk passenger, since their movement behaviors is difficult to predict with only using sensors [6] Therefore, it is necessary to provide very detailed sidewalk information in maps to ensure the safety for automated driving
Sidewalk is a vital infrastructure to provide safe paths for pedestrian and wheelchair users in their daily life for different purposes such as walking, transportation, economic development and environment improvement Especially for people with disabilities, sidewalks play an important role in all their physical activities Sidewalks are desirable to support them highly safety and mobility by reducing walking along roadway crashes Roadways with sidewalks are 88.2% lower than as likely to have pedestrian crashes as them without sidewalks [7].
Related Past Works
In consideration of effective utilizations of geospatial information, automated preprocessing methods, computational tools and utilization frameworks have been proposed [8-30]
In order to conflict management for pedestrians and wheelchair for people with disabilities on the subject of automated driving, sidewalk information needs to be described in the clear and precise way Nowadays, most of the map data is developed for use in the road networks that do not includes the sidewalk information [23] There are many shortcomings in map system related to application for pedestrians and wheelchairs Compare to the vehicle traveling in the road, pedestrians and wheelchair have more degrees of freedom in movement, thus they need a precise instruction to get the destination [24] Sidewalk data is the key for routing and navigation for pedestrians and wheelchairs Therefore, several studies focused on modeling sidewalk network to provide effective assistance for pedestrians and people with disabilities [25][26][27]
OpenStreetMap (OSM) is one of the available open map data that is widely used for application in routing and navigation However, the sidewalk information still has shortcomings in the OSM data [28] In previous study done by Mobasheri et al [29], OSM and GPS traces data were employed to extract the geometry of sidewalk and construct sidewalk network On the other hand, the raster data is also used for sidewalk extraction from street view imagery data source [30].
Research Objectives
The purpose of the study is to provide a computational framework as the set of algorithms to solve crucial problems for reconstruction of navigational information for sidewalk passengers from the authentic primitive geospatial information Particularly, following missions are target problems in this study:
⚫ Reconstruction of the sidewalk network
⚫ Reconstruction of the road network to assist the finding of conflict areas between vehicles and sidewalk passengers
⚫ Detection of conflict areas as a consistent extension of the road network
In the aim to solve the problem, a computational framework was proposed as an effective integration of methods in geometric computation As proposed methods, a general scheme was introduced to form individual solutions in the form of an algorithm of computation, which are provided by an integration of matrix operations and methods in geometric computation The proposed methods were evaluated in missions of reconstructions of sidewalk and road networks and applied to the detection of conflict areas vehicles and sidewalk passengers such as pedestrians and wheelchairs
The thesis outcome is to validate proposed methods to be able to apply to the real geospatial data Particularly, as a part of Kitakyushu city was selected to be the validation area, it included two square kilometers around the Wakamatsu Campus, Kyushu Institute of Technology known as Hibikino area, which is a well-constructed suburb having sidewalks and roads with variety of widths In the computer experiment, proposed methods demonstrated that a significant reduction of the computation time in comparison to the simple model-fee approach for node-connection in the case of sidewalk network reconstruction, about 80% accuracy in the road network reconstruction and provide a solution to the detection of conflict areas vehicles and sidewalk passengers as a consistent extension of the road network
Those results will open doors for designs and operations of effective model-based solutions to treat geospatial information, which intermediate between model-free approaches and heuristic approaches in conditions of minimization of the computational cost and accuracy of the representation.
Document structure
This thesis will present the research in a meticulous structure designed for easy location of important information and reducing the possibility of missing information The thesis is presented as follows:
⚫ Chapter 2: Basement Technologies gives basic insight on the GIS data and BMI data using in this Thesis This data will be used as material for sidewalk reconstruction and conflict management
⚫ Chapter 3: Proposed Methods for Sidewalk Data Management describes the development of the methods for individual missions of reconstructions of sidewalk and road networks and applied to the detection of conflict areas vehicles and sidewalk passengers
⚫ Chapter 4: Results and Discussion and describes the results and the validation of proposed methods The limitation of proposed methods is also presented in this chapter
⚫ Chapter 5: Conclusion and Future Perspectives summarizes the accomplishments and contributions of the study and provides future perspectives for the research.
Basement Technologies
Geographic Information System (GIS)
A geographic information system (GIS) is a computer system for creating, analyzing, managing, displaying and storing geographical information GIS connect all descriptive data to a map and integrate the location of them Location can be represented in different ways such as address, latitude and longitude, … GIS can describe many kinds of information data in map such as streets, rivers, buildings and vegetation GIS enables people to understand objects, relationship and geographic context GIS provides a tool for mapping and analysis in different fields of sciences and technologies GIS data is basically divided into two types: vector data [31] and raster data [32]
Vector data is usually used to represent real world features such as roads, trees, buildings, and so on Three basic types of vector data are points, lines and polygons A point feature has and is described by its X, Y and optional Z coordinate The value of X and Y depend on the Coordinate Reference System being used The Z value is used to describe the height The point feature is used to describe geometry that consists of a single vertex such as trees, traffic lights, … A sequence of connected points is formed a polyline feature The polyline feature represents road marking, curbs, … Polygon feature is also a sequence of connected points; however, the first vertex and last vertex are the same Polygon feature describe geometry of lakes, buildings, …
Raster data is composed of a matrix of pixels where each pixel represents a specific geographical feature Raster data is used to display features that are difficult to represent using vector data such as elevation, temperature
There are particular advantages and disadvantages of two forms of data Since vector data represents data using vertices and paths, it is considered to be a good method for cartographic representation that gives high geographic accuracy However, vector data is not very efficient to use for displaying continuous data Raster data can be an efficient solution for continuous information representation due to its pixelated organization While linear feature and paths are difficult to represent using raster data
A coordinate system is a system that use numbers for identifying the location of a point or geometric element on the earth Spatial data is defined in both horizonal and vertical coordinate system Longitude and latitude commonly used in coordinate system There are two types of horizonal coordinate systems: geographic coordinate system and projected coordinate system
Geographic Coordinate System (GCS) uses a three-dimensional spherical surface to define the position on the Earth GCS typical describe the location in decimal degrees that include degree of longitude and degree of latitude The longitude ranges from +180 o to -180 o with the East-West direction, while latitude ranges from +90 o to -90 o with the North-South direction
Projected Coordinate System (PCS), as opposed to GCS, is a flat system for use two- dimensional representation of the Earth The longitude and latitude coordinates are converted to planar coordinate x and y The center horizontal line with East-West direction is referred to as the x axis and the center vertical line with North-South direction is referred to as the y axis The intersection between x and y axis is the origin of coordinate system that has the coordinate of (0, 0) The horizontal lines above the origin have the positive value and those below the origin have the negative value Similarly, the vertical lines in the right side of origin have the positive value and those in the left side of origin have the negative value The value of coordinate in PCS is converted from three-dimensional Earth’s surface to two-dimensional flat surface using mathematical formulas The transformation is referred to as map projection Currently, there are many map projections is being used in around the world Depending on the purpose for which the map will be used, a particular map projection is considered for suitable application
Figure 2.3 Geographic coordinate system (left) and Projected coordinate system
OpenStreetMap (OSM)
OpenStreetMap (OSM) is a Volunteered Geographic Information (VGI) project to create map of the world that is free to use It is considered as the Wikipedia project for maps, where the community can contribute to build the maps around the world about the roads, trails, and much more Currently, OpenStreetMap has more than 8 million registered users and growing everyday As it stands now, OpenStreetMap has a high accuracy that is able to provide map data for numerous of websites and hardware devices Whole world dataset of OpenStreetMap is free to access as an XML file called Planet.osm that is updated on weekly basis
The OpenStreetMap data consists of the following three primitive data types:
- Node, which is defined as a point in the space associated with a node identifier, latitude and longitude coordinates
- Way, which represents a line between two nodes, and associated with the way identifier and the two nodes identifiers of the two end points of the line The line could be simply a road segment, part of a boundary of a building, city/country boundary, or part of a lake contour
- Relation, which represents the relation between nodes, ways, or even other relation, and is used to express polygons For example, to express the boundaries of a building, the nodes need to be defined, then the ways that connect nodes to each other, then a relation that connects the ways together to describe the building boundary Since the Openstreetmap is a collaborative project where any volunteer can contribute, one building may be expressed in various relations depending on contributors The relation could be separated or nested where each relation is composed of either ways or nodes All OSM elements (nodes, ways, relations) can have tags that describe the meaning of elements A tag consists of two fields: a key and a value The key is used to describe category or feature type The value is used to describe the detail of corresponding key Both fields are free form text For example, the tag “highway = trunk” with a key
“highway” and a value “trunk” is used to indicate the important highway in country’s system that are not a motorway; the tag “name = 本城バイパス” is used to describe the name of the highway
Figure 2.4 OpenStreetMap data of an area of Kitakyushu, Japan
There are two ways to map sidewalks in OSM data: mapping sidewalks as separate way or mapping sidewalks as refinement of highways
In the first method, a sidewalk could be a way that is separate from highway This way is drawn by contributor and added tags “highway = footway” + “footway sidewalk” to indicate a sidewalk Disadvantages of this method is to link the sidewalk with associated road autonomously A mapper adds the name of associated road as a tag into the line of sidewalk Additionally, the “relation” tags are also required to assign road name to sidewalk
In the second method, sidewalks are mapped by adding tag “sidewalk = ?” into the sections of an existing road The values of this tag could be “both/ left/ right/ no” to indicate the position of the sidewalk relative to the road In addition, the property of sidewalk is able to be added as a sub-tag in the road such as width, surface of the sidewalk or bicycle permission on the sidewalk The drawback of this method is that detailed geometric information of sidewalks is not provided because sidewalks are attached with the main road and there is no standard view in OSM for the sidewalk data.
Figure 2.5 OpenStreetMap with sidewalk data
However, since OSM is a project that volunteers around the world could contribute, it is difficult to build a map data in a consistent way and there are still many lacks of information of sidewalk system Table 2.1 gives the statistics of highway and sidewalk information in OSM data
Table 2.1 The statistic of highways and sidewalks in OSM dataset
Number of highways Number of sidewalks as the highway refinement Number of sidewalks as the separated way
As seen in the Table 2.1, number of highways is much larger than the total number of sidewalks generated by two above approaches This statistic indicates that there is still many lacks of sidewalk information in the OSM which is considered as one of the largest open map data all over the world Therefore, the enrichment and management of sidewalk data using for the map and especially for application in automated driving is the attractive issues in the research field of automated driving and mapping.
Basic Map Information from GSI
As mentioned in the previous section, although OpenStreetMap is one of the largest open map data over the world, sidewalk still has lack of information to representing in OSM Therefore, another open map data is considered for sidewalk reconstruction in this thesis
The Geospatial Information Authority of Japan (GSI) is the national organization that conducts basic survey and mapping and instructs related organizations to clarify the conditions of land in Japan In this study, the basic map information provided by GSI is used to reconstruct sidewalk geometry for pedestrian and wheelchair movements and manage the conflict in the scenarios of sidewalk traffic
2.3.1 How to download map data for GSI
The data can be downloaded from the official website of GSI There are three types of available basic map information: (1) basic items including benchmark for surveying, coastline, boundaries and representative points of administrative divisions, road edge, outline of building, … (2) digital elevation model and (3) geoid model For the main purpose of this study, only the data of basic items is considered
The steps to download are showed as follow
Step 1: Access to the website of GSI and click the button shown in the red box for the data of basic items as shown in Figure 2.6 https://fgd.gsi.go.jp/download/
Figure 2.6 Three types of data from GSI
Step 2: A map of Japan appear and is divided into multiple areas Select downloaded area by scrolling to enlarge and clicking the number of area for download the map data
Figure 2.7 Select the area for download
In Figure 2.7, the selected box “503065” is the data of Kitakyushu area that is used in this Thesis Then click the button “ダウンロードファイル確認へ” to check the download file Note that the BMI data download service requires user registration
Step 3: Download Basic Map Information data as Figure 2.8 The data contains man types of basic item of map data shown in Figure 2.9 that can be used for various research purposes In this study, the Road composition and Road edge data will be used to analyze
Figure 2.8 Select the data for dowload
Figure 2.9 All data of basic items
The basic items of BMI data include various types of road components such as roads, sidewalks, rails, coastlines, … Each type is stored in a separated XML file that can be used for different application purposes The target of this thesis is construction of sidewalks and conflict management between pedestrians/wheelchairs and vehicles traveling in the roads, thus two considered data types of basic items are road compositions which consists of sidewalk data and road edge for road reconstruction 2.3.2 BMI data format
The road composition data consists of four types: sidewalks (歩道), median strips (分
離帯), gutters (側溝), stormwater inlets (雨水桝) Sidewalks and median trips are displayed as the green line in map
The Road edge data consists of four types: straight roads (真幅道路), walking paths
(徒歩道), road tunnels (トンネル内の道路), garden roads (庭園路等) Figure 2.10 is an example of an area with BMI data displayed in QGIS software [3].
Figure 2.10 BMI data displayed in QGIS
BMI data is stored in XML format Coordinates of points forming line is stored in the posList Additional information is also displayed such as time, name of office, … Unlike OSM data, the line in BMI data is not described by separated points with unique ID The list coordinate included in data of lines will indicate the position of lines in map
Figure 2.11 XML format of BMI data
Methods for Sidewalk Data Management
Sidewalk Network Reconstruction
3.1.1 Problems in BMI data to describe sidewalks
In the original BMI data, line segments are used to describe sidewalks Problems for this representation are shown as follow
⚫ As mention in previous chapter, the BMI data is stored in XML format for each line segments the position of line segments in the map is described using a list of coordinates
⚫ Furthermore, a same sidewalk is represented by multiple lines that make difficult to determine the connection of sidewalk and road networks
Therefore, it makes difficult to manage the sidewalk as a consistent path to detect connection of sidewalks each other and between sidewalks and roads The data structures of line segments in BMI to describe sidewalk information is showed in Figure 3.1, Each line segment consists of a list of coordinates that define the location of the points belong to line segments For example, line a with n points will be represent with the structure of coordinates {(ax1, ay1), (ax2, ay2), (ax3, ay3), … (axNa, ayNa)} as seen in Figure 3.1a Similarly, this structure is applied to line b, line c and all the line segments in the dataset
In preparation of sidewalk network reconstruction, data of each line segment will be processed in the concept of matrix representation Each coordinate now corresponds to a point on the line segment that extends from the starting point to the ending point as shown Figure 3.1b
Figure 3.1 Data structure of line segments for sidewalk representation in BMI data
According to the necessity, it is required to connect all the road segments to form a consistent line representing the sidewalk Sidewalk data need to be managed in the right way for fine treatment in detection of conflict areas between pedestrians and vehicles
3.1.2 Proposed Method 1: Node connection algorithm to integrate consistent segments in the sidewalk data
Since nodes in BMI data do not have unique ID, this makes difficult to determine whether two lines connect or not Therefore, node connection is necessary to represent sidewalk consistently Figure 3.2 shows an example of how the sidewalk is represented in BMI data The same sidewalk (pink line) is divided into multiple line segments (blue, orange and green lines) The flowchart of node connection algorithm is shown in Figure 3.3
Figure 3.2 Sidewalk is represented by separated line segments
Figure 3.3 The flowchart of node connection algorithm
Firstly, the small area is selected for easy treatment From the BMI data of this area, the line segments are extracted and stored as a data in MATLAB Each line segments will be sequentially assigned a unique ID value for data management As seen in format of BMI line data, each line segment consists of a list of nodes with coordinate (latitude and longitude) Line segments connects to each other through their two end points that are showed in Figure 3.4 Therefore, the positions of end points are necessary to describe the connectivity between line segments
Figure 3.4 Sidewalks connect each other through end points
Figure 3.5 Sidewalk data of original BMI is displayed in MATLAB
According to the line data in BMI, the coordinates of end nodes shown in Figure 3.5
End point End point are then extracted and stored as an array in MATLAB Each line is represented by two end points that are then used for describe the connectivity between line segments
Figure 3.6 List of end points
End points are determined to be coincident based on the array that are showed in Figure 3.6 If two points have the same coordinate (latitude and longitude), they are considered as connecting point of two lines which they belong to Two those lines have connectivity This connectivity is stored in a connectivity matrix of MATLAB as shown in Figure 3.7
Figure 3.7 Connectivity matrix of line segments in BMI sidewalk
In Figure 3.7, the connectivity matrix contains two values 0 and 1 that describe the connectivity between line segments through end points When two nodes A and B have the same coordinate, the value of position [endnodeA, endnodeB] in the matrix will be set to 1 that means the line having node A is connected to the line having node B In contrast, the value 0 describes that there is no connection between line segments After that position of connection nodes is extracted to use for connect lines together as Figure 3.8
Figure 3.8 Connectivity between two line segments
Subsequently, two lines having the connectivity will be merged through the common point The algorithm is iterative executed and thoroughly checked in numerous times until the sidewalks are no longer represented by many separated line segments The consistent sidewalk data then is stored and managed as the new structure with the unique identification and position of connected nodes
3.1.3 Proposed Method 2: Flipping algorithm to represent a consistent sidewalk
As introduced above, the first method works well in the sense of the minimization of the data management procedure However, it does not satisfy the requirement of the sidewalk description in senses of the accuracy and the fine data management to manage conflict problems that will be described in the following sections
Therefore, an improvement of the algorithm is considered for sidewalk network reconstruction in the purpose of reducing computational time, also fine management for conflict issues in the concept of matrix operations As mentioned, the connection algorithm uses end points of line segments for connectivity detection and then merging line segments into a consistent path to describe sidewalk for BMI data Nevertheless, The BMI data is well-refined data to form constructions in space by linked together partially, and line segments for sidewalk representation are also constructed like that
The sidewalk in original BMI data can be described by one or many line segments The important point is that these line segments are constructed one by one using directional line without a certain orientation An example of line segments that do not follow in one direction can be seen in Figure 3.9
Figure 3.9 Direction in line segments of BMI sidewalk
Each line segment representing the sidewalk is directed to management by a matrix that includes its nodes with coordinates For example, line segment A treated as a matrix ax1 ay1 ax2 ay2 ax3 ay3
… bx1 by1 bx2 by2 bx3 by3
End point End point cx1 cy1 cx2 cy2 cx3 cy3
] , line sement B treated as a matrix with coordinate
] and line sement B treated as a matrix with coordinate [
] connect each other to form the same sidewalk However, they are not described in the same direction in BMI data that make difficult to establish connection between them
In consideration of the solution, a flipping algorithm is necessary for generally connecting line segments and management of sidewalk data in a consistent way Moreover, the sidewalks are stored in the context of line data and each line segment will be assigned a unique ID for effective management
Figure 3.10 Flipping direction for the consistent line
Road network reconstruction
With the aim of conflict management between sidewalk passengers and vehicles, not only sidewalk network is necessary information, but also the road network is considered Similar to the approach of sidewalk area construction, a geometric computation is executed for detection of center line to describe the road network
Figure 3.21 The road network reconstruction model
The algorithm is designed using road edges data as materials to reconstruct the road network following the sequential process as seen in Figure 3.22 Each line in the dataset is one-by-one applied the algorithm to find a parallel line for each segment connecting two adjacent points Like algorithm for reconstruction of sidewalk area, the target line is required to be within in a limited area and parallel to the selected part to avoid combinatorial explosion The midpoints between the selected nodes are determined after refinement of selection The node generation is also performed in order to select only the necessary part
Figure 3.22 The design of algorithm for the road network reconstruction
Conflict area identification
There are vehicle paths that pass through sidewalk area for pedestrians and wheelchair users as seen in Figure 3.23, Figure 3.24, Figure 3.25, Figure 3.26 In the consideration of solution for this issue, the detection of those overlapping areas is necessary to guarantee the safety of sidewalk passengers such as pedestrians and wheelchair users to avoid traffic collision with vehicles that is described in Figure 3.27
Figure 3.23 Scenario 1 of overlapping of sidewalk and vehicle path (photos from Google Map and Street View)
Figure 3.24 Scenario 2 of overlapping of sidewalk and vehicle path (photos from Google Map and Street View)
Figure 3.25 Scenario 3 of overlapping of sidewalk and vehicle path (photos from Google Map and Street View)
Figure 3.26 Scenario 4 of overlapping of sidewalk and vehicle path (photos from Google Map and Street View)
Figure 3.27 The idea for conflict management
Figure 3.28 The design of algorithm for conflict area detection
For this purpose, the intersection point between sidewalk and road lines will be calculated in the framework of geometric computation as seen in Figure 3.28 There are two scenarios for intersection point dete0ction In the first one, the road line and the sidewalk line intersect each other, the orthogonal closest point in this case will be on the road line However, in the second one without intersection between road line and sidewalk line, it is required to find the extension line for the road in order to determine the intersection point.
Results and Discussion
Sidewalk network reconstruction
4.1.1 Integration of sidewalk data to represent a consistent path
The samples of sidewalk network before and after using proposed method are seen in Figure 4.1 and Figure 4.2, Figure 4.3 and Figure 4.4, respectively These figures clearly show the effectiveness of proposed method in the purpose of constructing the consistent path for sidewalk representation in map
In case of original BMI data, a same sidewalk is represented by multiple line segments There are many difficulties in the purpose of minimization of computational cost and conflict management in this data structure Hence, it is necessary to apply the proposed method for integration of sidewalk data to represent as consistent path
Figure 4.1 The part 1 of map of original BMI data
Figure 4.2 The part 1 of map after applying proposed method
Figure 4.3 The part 2 of map of original BMI data
Figure 4.4 The part 2 of map after applying proposed method
In computer experiment, 10 trials by method 1 and 10 trials by method 2 were obtained The computation time of each trial is shown in Table 4.1 Additionally, the standard deviation is also calculated for proposed methods as shown in Figure 4.5 As the result, the proposed method 2 that is faster than the conventional method 1 In the aim to reduce computational cost, the method 2 is much better than method 1
Table 4.1 Computational time (s) of Method 1 and Method 2
Figure 4.5 Comparison of computational cost of two proposed methods
Sidewalk area reconstruction
4.2.1 Extraction of road edge extraction
The road edge data is reconstructed using the second proposed method for consistent data structure as seen in Figure 4.6 This data will be for combination of sidewalk data to describe the geometry of sidewalk area
Figure 4.6 The result of road edge data extraction
4.2.2 Geometric computation for target edge detection
The results of sidewalk data area reconstruction by combining two sidewalk and road edge data are shown in Figure 4.7 - Figure 4.13 However, the algorithm does not work well in some case Parameters in geometric computation were finely tuned for the extraction of narrow pathways, as demonstrated The narrow road and sidewalk were difficult to extract by using the combination of conditions of geometric computation, which indicates the proposed method finely works for the target problem On the other hand, in a mixtured situation of pathways having different widths, the parameter fitting does no match precisely Therefore, parallel line detections do not work well in cases of broad wide roads as shown in Figure 47 and cases with an open area as shown in Figure
Figure 4.7 Sidewalk area reconstruction result for part 1 of the map
Figure 4.8 Sidewalk area reconstruction result for part 2 of the map
Figure 4.9 Sidewalk area reconstruction result for part 3 of the map
Figure 4.10 Sidewalk area reconstruction result for part 4 of the map
Figure 4.11 Sidewalk area reconstruction result for part 5 of the map
Figure 4.12 Sidewalk area reconstruction result for part 6 of the map
Figure 4.13 Sidewalk area reconstruction result for the whole map
Road network reconstruction
4.3.1 Geometric computation for parallel line detection
The results of road network reconstruction by using parallel line detection algorithm are shown in Figure 4.14 - Figure 4.20 The results indicate that even it is not easy to find the road at the intersection, the most of road segment is finely reconstructed
Figure 4.14 Road network reconstruction result for part 1 of the map
Figure 4.15 Road network reconstruction result for part 2 of the map
Figure 4.16 Road network reconstruction result for part 3 of the map
Figure 4.17 Road network reconstruction result for part 4 of the map
Figure 4.18 Road network reconstruction result for part 5 of the map
Figure 4.19 Road network reconstruction result for part 6 of the map
Figure 4.20 Road network reconstruction result for the whole map
4.3.2 Validation of road network reconstruction
The result of road network reconstruction is validated using the map information data from GSI The road data is geoJSON format [34] is partly downloaded and integrated to represent the road network as the road centerlines as seen in Figure 4.22 The ground truth of road network from GSI in the pixel representation Afterward, the result of road network reconstruction for the whole map in this thesis is converted from vector representation to pixel representation as seen in Figure 4.23 The result of road network reconstruction in pixel representation for easy comparison with the ground truth
Figure 4.21 The road map from GSI [35]
Figure 4.22 The ground truth of road network from GSI in the pixel representation
Figure 4.23 The result of road network reconstruction in pixel representation
Comparing to the road network data from GSI, the coverage ratio of road network reconstruction is approximately 80% that can be seen in Figure 4.24 Based on this result, the road network is almost reconstructed completely using the method in this thesis Besides that, there are still about 20% the unnecessary lines
Figure 4.24 The validation result of road network reconstruction
Conflict area identification
As seen in Figure 4.25 of the actual picture of the map data, the vehicle path passing through the sidewalk has the potential to cause a collision between vehicle and sidewalk passenger Therefore, the conflict area detection is valuable solution to ensure the safety for pedestrians and wheelchair users traveling on the sidewalks The result showed in Figure 4.26 indicates that the algorithm in this thesis is worth considering for conflict management
Figure 4.25 Actual scene of the overlapping area in reconstructed data (photos from Google Map and Street View)
Figure 4.26 The results of conflict area detection
Conclusion
The thesis is focused on the data management for sidewalk using BMI as materials that is an authentic data provided by nation organization GSI The proposed methods were evaluated in missions of reconstructions of sidewalk and road network and the applied to the detection of conflict area between vehicles and sidewalk passengers such as pedestrian and wheelchair users
In the computer experiment, proposed methods demonstrated that a significant reduction of the computation time in comparison to the simple model-fee approach for node-connection in the case of sidewalk network reconstruction, about 80% accuracy in the road network reconstruction and provide a solution to the detection of conflict areas vehicles and sidewalk passengers as a consistent extension of the road network
The results of computer experiments demonstrated a fine design of the framework as an integration of multiple types of geometric computation Those results will open doors for designs and operations of effective model-based solutions to treat geospatial information, which intermediate between model-free approaches and heuristic approaches in conditions of minimization of the computational cost and accuracy of the representation
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