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μ€μΊ λλ©΄μ νμ©ν μ΄λμ½μμ© μ€λ΄ κ·Έλν λ°μ΄ν°λ² μ΄μ€ ꡬμΆ
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Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : 곡과λν 건μ€ν경곡νλΆ, 2021.8. λ°μ¬μ.μ¬λλ€μ μ€λ΄ νλμ΄ λ€μν΄μ§λ©΄μ 건물μ κ·λͺ¨κ° 컀μ§κ³ κ΅¬μ‘°κ° λ³΅μ‘ν΄μ§κ³ μλ€. μ΄λ¬ν μ€λ΄ νκ²½μ λ³νλ κ΅ν΅μ½μμ μ΄λμ± λ³΄μ₯μ λν μ¬νμ κ΄μ¬μ μ¦κ°μμΌ°μΌλ©°, κ΅ν΅μ½μ λ§μΆ€ν μ€λ΄ λΌμ°ν
μλΉμ€μ λν μμ λν μ¦κ°μμΌ°λ€. νΉν λ§μ μ΄λ μ μ½μ κ°μ§λ μ΄λμ½μ λμ μλΉμ€μ κ²½μ°μλ, μ΅μ κ²½λ‘λ₯Ό κ³ννλ κ³Όμ μμ κ°μΈμ μ νΈλ κ²½νμ΄ λ°μλ κ°μΈνλ μλΉμ€λ‘ λ²μκ° νμ₯λκ³ μλ€. μ΄λ¬ν λ°°κ²½μμ, μ€ν€λ§κ° μ μ°νκ³ λ°μ΄ν°μ κ°κ³΅ λ° μ²λ¦¬κ° ν¨μ¨μ μΈ λ°μ΄ν°λ² μ΄μ€μ ꡬμΆμ΄ νμνλ€.
λ³Έ μ°κ΅¬μμλ μ€μΊν λλ©΄ μ΄λ―Έμ§λ₯Ό νμ©ν μ΄λμ½μμ© μ€λ΄ κ·Έλνλ°μ΄ν°λ² μ΄μ€ κ΅¬μΆ κΈ°λ²μ μ μνμλ€. λ¨Όμ , κ΅λ΄μΈ μ€λ΄ κ³΅κ° κ΄λ ¨ νμ€ λ° μ€κ³ κΈ°μ€λ€μ κ²ν λ₯Ό ν΅ν΄ μ΄λμ½μμ ν΅νκ³Ό κ΄λ ¨λ μ€λ΄ κ³΅κ° λ° κ°μ²΄, μν₯ μμΈλ€μ λμΆνμ¬ κ°λ
μ λ°μ΄ν° λͺ¨λΈμ μ€κ³νμλ€. λν, μ€λ΄μ κ° κ³΅κ°κ³Ό μμ€λ¬Όμ κΈ°νμ 보μ μμμ 보λ₯Ό κΈ°λ°μΌλ‘ μ΄λμ½μμ μ κ·Όμ± λ° ν΅ν κ°λ₯μ±μ μ λννκΈ° μν μ κ·Όμ± μ§μλ₯Ό μ€κ³νμλ€. λ€μμΌλ‘, μ€μΊ λλ©΄μ μ
λ ₯νμ¬ μ΄λμ½μμ© μ€λ΄ κ·Έλν λ°μ΄ν°λ² μ΄μ€ ꡬμΆμ μν νλ‘μΈμ€λ₯Ό μ μνμλ€. μ μν νλ‘μΈμ€λ μ μ΄νμ΅ κΈ°λ° μ κ·Ό λ°©μμ ν΅ν΄ μ€μΊ λλ©΄μμ 곡κ°μ ꡬ쑰 μ 보λ₯Ό μΆμΆνκ³ , ν ν΄λ‘μ§ μΆμΆ λ° μ κ·Όμ± νκ°λ₯Ό ν΅ν΄ μ΄λμ½μμ© λ€νΈμν¬ λͺ¨λΈμ μμ±νλ©°, μμ±ν λ€νΈμν¬ λͺ¨λΈμ κ·Έλν λ°μ΄ν°λ² μ΄μ€λ‘ μλ λ³ννλ κ³Όμ μ ν¬ν¨νλ€. ꡬ체μ μΌλ‘, μ μ νλ‘μΈμ€λ μμ λ ResNet κΈ°λ°μ λͺ¨λΈμ μλ‘κ² λΌλ²¨λ§ν λλ©΄μΌλ‘ λ―ΈμΈ μ‘°μ νμ¬ μ¬μ©ν¨μΌλ‘μ¨ μ€λ΄ ꡬ쑰맡μ μμ±νλ€. μ΄ν μΆμΆλ κ°μ²΄λ€μ κ³΅κ° κ΄κ³λ₯Ό κΈ°λ°μΌλ‘ κ° κ³΅κ°μ λ
Έλμ λ§ν¬λ‘ ννν μ€λ΄ λ€νΈμν¬ λͺ¨λΈμ ꡬμΆνλ€. κ° κ³΅κ°μ μ κ·Όμ± μ 보λ μ μλ μ κ·Όμ± μ§μμ μκ³κ°μ μ¬μ©νμ¬ μμ±λ ν λ°μ΄ν°λ² μ΄μ€μ μ μ₯λμ΄, μ΄λμ½μλ₯Ό μν μ κ·Ό κ°λ₯ν κ·Έλν μΆμΆμ νμ©λ μ μλ€.
λ³Έ μ°κ΅¬μμλ μ μν κΈ°λ²μ μμΈλνκ΅ λλ©΄ λ°μ΄ν° μ
μ μ μ©νμ¬ μ΄λμ½μμ© μ€λ΄ κ·Έλν λ°μ΄ν°λ² μ΄μ€λ₯Ό ꡬμΆνκ³ νκ°νμλ€. ꡬμΆν μ€λ΄ κ·Έλν λ°μ΄ν°λ² μ΄μ€λ₯Ό νμ©νμ¬ λ€μΈ΅ κ²½λ‘ κ³νκ³Ό μ€λ΄μΈ μ°κ³ κ²½λ‘ κ³νμ 2κ°μ§ μλ리μ€μ λ°λΌ μ΅μ κ²½λ‘λ₯Ό λμΆνμλ€. κ·Έ κ²°κ³Ό, μΌλ° 보νμμ μ΅μ κ²½λ‘μ λΉκ΅νμ¬ μ΄λμ½μμ© μ΅μ κ²½λ‘λ κ°κΉμ΄ κ³λ¨μ΄ μλ μλ¦¬λ² μ΄ν°λ₯Ό ν΅ν μμ§ μ΄λμ ν¬ν¨νμμ λΏλ§ μλλΌ μ κ·Ό λΆκ°λ₯ν 곡κ°μ ννΌνλλ‘ λμΆλμλ€. μ¦, μ μν κΈ°λ²μ ν΅ν΄ μ΄λμ½μ μΈ‘λ©΄μμ ν΅ν μ₯μ μ 보λ₯Ό ν¬ν¨νμ¬ μ€λ΄ νκ²½μ μ μ νκ² λ¬μ¬νλ λ°μ΄ν°λ² μ΄μ€μ ꡬμΆμ΄ κ°λ₯ν¨μ νμΈν μ μμλ€. λν, μΆμ
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λ κ΄κ³ μμ±λ§μΌλ‘ μ€μΌμΌμ΄λ μ’ν λ³ν μμ΄ μ€λ΄μΈ μ°κ³ κ²½λ‘ κ³νμ΄ κ°λ₯νμλλ°, μ΄λ λ
립μ μΈ λ°μ΄ν° κ° μ°κ³ μ¬μ©μ μ ν©ν κ·Έλν λ°μ΄ν°λ² μ΄μ€μ νΉμ±μ λ°μν κ²°κ³Όλ‘ νλ¨ν μ μλ€.
λ³Έ μ°κ΅¬μ μ£Όμ κΈ°μ¬λ μ€μΊν λλ©΄μ μ¬μ©νμ¬ μ΄λμ½μμ© μ€λ΄ κ·Έλν λ°μ΄ν°λ² μ΄μ€λ₯Ό ꡬμΆνκΈ° μν νλ‘μΈμ€λ₯Ό κ°λ°ν κ²μ΄λ€. ꡬ체μ μΌλ‘, μ΄λμ½μμ μ΄λμ μ΄μ μ λκ³ μ€κ³ν λ°μ΄ν° λͺ¨λΈμ κΈ°λ°μΌλ‘ ν λ°μ΄ν°λ² μ΄μ€ ꡬμΆμ΄ κ°λ₯νλ―λ‘ μ΄λμ½μμ© μ€λ΄ κΈΈμλ΄ μλΉμ€μ νμ©λ μ μλ€. λν, ν ν΄λ‘μ§ κ΅¬μΆ λ° κ·Έλν λ°μ΄ν°λ² μ΄μ€λ‘μ λ³νμ μν νμ νλ‘μμ Έλ₯Ό κ°λ°νμμΌλ©°, μ μ νλ‘μΈμ€λ ν΄λΉ νλ‘μμ Έλ€λ‘ ꡬμ±λμ΄ λλ©΄ μ
λ ₯μ ν΅ν΄ μ΄λμ½μμ© μ€λ΄ κ·Έλν λ°μ΄ν°λ² μ΄μ€ ꡬμΆμ κ°λ₯νκ² νλ€. ν΄λΉ νμ νλ‘μμ Έλ€μ μλμΌλ‘ μνλ μ μμ΄ λ°μ΄ν°λ² μ΄μ€ κ΅¬μΆ μ μμλλ μκ°κ³Ό λΉμ©μ μ κ°ν μ μλ€. λν, λ€μν μ ν λ° λΉμ ν λ°μ΄ν°μ μ°κ³μ μ ν©ν κ·Έλν λ°μ΄ν°λ² μ΄μ€μ νΉμ§μ μν΄, μ μν νλ‘μΈμ€λ₯Ό ν΅ν΄ ꡬμΆν μ€λ΄ λ°μ΄ν°λ² μ΄μ€λ κΈ°μ‘΄ κ³΅κ° λͺ¨λΈμ κΈ°λ₯μ ν¬ν¨νλ©΄μ λ€μν μ νμ κΈΈμλ΄ μλΉμ€μ νμ©λ μ μμ κ²μΌλ‘ κΈ°λλλ€.Changes to the indoor environment have increased social interest in ensuring the mobility of people with disabilities. Therefore, the demand for customized indoor routing services for people with mobility disabilities (PWMD), who have many travel restrictions, is increasing. These services have progressed from spatial routing to personalized routing, which reflects personal preferences and experiences in planning an optimal path. In this regard, it is necessary to generate a database for PWMD with a flexible schema suitable for the efficient manipulation and processing of data.
This study aims to propose a technique of generating an indoor graph database for PWMD using scanned floor plans. First, a conceptual data model was developed by deriving relevant indoor features and influential factors, considering various international regulations on indoor environments. Also, the accessibility index was designed based on the data model to quantify the difficulties in accessing spaces based on each indoor spaces geometric characteristics. Next, a three-stage process was proposed: retrieving the structure of spaces from scanned floor plans through a transfer learning-based approach, retrieving topology and assessing accessibility for creating an indoor network model for PWMD, and converting the network model into a graph database. Specifically, an indoor structure map is created by fine-tuning the modified Resnet-based model with newly annotated floor plans for extracting structure information. Also, based on the spatial relationship of the extracted features, the indoor network model was created by abstracting indoor spaces with nodes and links. The accessibility of each space is determined by the proposed indices and thresholds; thereby, a feasible network for PWMD could be derived. Then, a process was developed for automatically converting an indoor network model, including accessibility property, into a graph database.
The proposed technique was applied to the Seoul National University dataset to generate an indoor graph database for PWMD. Two scenario-based routing tests were conducted using the generated database to verify the utility of results: multi-floor routing and integrated indoor-outdoor routing. As a result, compared with the path for general pedestrians, the optimal path for PWMD was derived by avoiding inaccessible spaces, including vertical movement using elevators rather than the nearest stairs. In other words, applying the proposed technique, a database that adequately described an indoor environment in terms of PWMD with sufficient mobile constraint information could be constructed. Moreover, an integrated indoor-outdoor routing could be conducted by only creating an entrance-labeled relationship, without scale and coordinate transformation. This result reflects the usability of the generated graph database and its suitability regarding the incorporation of multiple individual data sources.
The main contribution lies in the development of the process for generating an indoor graph database for PWMD using scanned floor plans. In particular, the database for PWMD routing can be generated based on the proposed data model with PWMD-related features and factors. Also, sub-procedures for topology retrieval and graph database conversion are developed to generate the indoor graph database by the end-to-end process. The developed sub-procedures are performed automatically, thereby reducing the required times and costs. It is expected that the target database of the proposed process can be generated considering utilization for various types of routing since the graph database is easily integrated with multiple types of information while covering the existing spatial models function.1. Introduction 1
1.1 Objectives and contributions 1
1.2 Related works 7
1.2.1 Indoor environment conceptualization 7
1.2.2 Indoor data construction 11
1.2.3 Accessibility assessment 19
1.3 Research scope and flow 22
2. Conceptual modeling 26
2.1 Relevant features and factors 28
2.2 Proposed data model 30
2.3 Space accessibility for PWMD 36
2.3.1 Influential factors within indoor environments 37
2.3.2 Accessibility index 41
3. Indoor graph database for PWMD from scanned floor plans 43
3.1 Retrieving structure of indoor spaces 43
3.1.1 Pre-trained model for detecting indoor geometry 45
3.1.2 Dataset with new annotation 47
3.1.3 Transfer learning-based approach 52
3.2 Generating the indoor network model for PWMD 56
3.2.1 Definition of nodes and links in the network model 60
3.2.2 The classification rule of space polygons 63
3.2.3 Connection between general spaces and doors 68
3.2.4 Node-link generation for horizontal transition spaces 71
3.2.5 Vertical link generation 75
3.2.6 Connectivity and accessibility information generation 79
3.3 Indoor graph database for PWMD 80
3.3.1 Graph representation of indoor environments 80
3.3.2 Conversion of network model into graph database 83
3.4 Entire process 87
4. Experiment and results 89
4.1 Experimental setup and test data 89
4.2 Evaluation for retrieved information 92
4.2.1 Results of structure retrieval 92
4.2.2 Results of topology retrieval 99
4.3 Generated indoor graph database for PWMD 128
4.3.1 Results of the indoor graph database for PWMD 128
4.3.2 Query-based routing 136
5. Conclusion 147
References 150
Appendix 166
κ΅λ¬Έμ΄λ‘ 178λ°
A System-driven Automatic Ground Truth Generation Method for DL Inner-City Driving Corridor Detectors
Data-driven perception approaches are well-established in automated driving
systems. In many fields even super-human performance is reached. Unlike
prediction and planning approaches, mainly supervised learning algorithms are
used for the perception domain. Therefore, a major remaining challenge is the
efficient generation of ground truth data. As perception modules are positioned
close to the sensor, they typically run on raw sensor data of high bandwidth.
Due to that, the generation of ground truth labels typically causes a
significant manual effort, which leads to high costs for the labelling itself
and the necessary quality control. In this contribution, we propose an
automatic labeling approach for semantic segmentation of the drivable ego
corridor that reduces the manual effort by a factor of 150 and more. The
proposed holistic approach could be used in an automated data loop, allowing a
continuous improvement of the depending perception modules.Comment: 8 page
Comparison of Arterial-Level Signal Coordination Features of Five Selected Software Programs
Synchro, PTV Vistro, TransModeler, Tru-Traffic, and TranSync are software programs that are part of different software packages that can all be used for traffic signal coordination tasks in countries that use ring-barrier signal controllers. Each program has different functions, features, inputs and outputs. Synchro, Vistro, and TransModeler all require volume inputs to make an analysis while Tru-Traffic and TranSync do not require volumes. These five programs were compared on the basis of their ability to assist the engineer in designing an optimized arterial coordination timing plan, which included automatic optimization, editing and viewing information presented on the time-space diagram, and selecting various timing plans and intersections to show the time-space diagram.The same arterial was modeled in all five programs and phase sequence and offset default optimization functions were executed separately for Synchro, Vistro, Tru-Traffic and TranSync. The average vehicle travel time and average number of vehicle stops were simulated using TransModeler, a third-party traffic simulation program to ensure results are not biased. Results showed that Synchro and Tru-Traffic had the lowest peak direction travel time through the whole arterial and the least number of stops. The optimization produced by Vistro and TranSync was 1 minute or 10% slower for the peak direction average travel time than Synchro and Tru-Traffic, which were tied. The southbound travel time for the optimization produced by Vistro was very similar to TranSync, but TranSync had the shortest northbound travel time. The northbound travel time from TranSync was 10% or 1 minute faster than the slowest off-peak (northbound) optimization which was performed by Vistro. TranSync had the greatest number of features for the time-space diagram and timing plan options compared to the other four programs.This research reviewed the features of each software package so practitioners can make a better educated decision on which program they would like to use. Using the right tool for the task can save project resources (time, budget, etc.) and contribute to efficiently designed timing plans
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