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  • 1
    Series available for loan
    Series available for loan
    Hannover : Leibniz Universität Hannover
    Associated volumes
    Call number: S 99.0139(384)
    In: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover, Nr. 384
    Description / Table of Contents: This dissertation shows the importance of using low-cost crowd-sourced information for the task of traffic regulator recognition (traffic signals, stop signs, priority signs, uncontrolled intersections), the cost of which in terms of time and money is much higher if standard technology is used for surveying. GPS trajectories can reveal the movement patterns of traffic participants, and the initial hypothesis that traffic regulations can be retrieved by mining the movement patterns imposed by traffic rules is verified. The predictive ability of the classifier becomes more accurate when static information derived from open maps (OSM) is merged with dynamic features extracted from GPS trajectories. An extensive evaluation of the proposed methodology on three datasets, provided classification accuracy between 95% and 97%.
    Type of Medium: Series available for loan
    Pages: ix, 178 Seiten , Illustrationen, Diagramme, Karte
    ISBN: 978-3-7696-5310-6 , 9783769653106
    ISSN: 0174-1454
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover Nr. 384
    Language: English
    Note: Dissertation, Gottfried Wilhelm Leibniz Universität Hannover, 2023 , Contents 1. Introduction 1.1. From GPS tracks to Traffic Regulations 1.2. Motivation for Learning Intersection Traffic Regulations 1.3. Research Gap 1.4. Motivation for Learning Traffic Regulations from GPS Data 1.5. Research Objectives, Challenges and Contributions 1.6. Outline of the Thesis 1.7. Summary 1.8. Acknowledgements 2. Theoretical Background 2.1. Intersections and Intersection Traffic Regulations 2.1.1. Intersections 2.1.2. Intersection Traffic Regulations 2.2. Spatiotemporal Data and Movement Trajectories 2.2.1. The Global Positioning System 2.2.2. Sampling Frequency 2.2.3. The GPS Exchange Format: GPX 2.2.4. Movement Patterns in Spatiotemporal Data 2.2.5. Spatiotemporal Data Mining 2.2.6. Semantic Enrichment of Trajectories 2.2.7. Detecting Stop and Moves: the CB-SMoT Algorithm 2.3. Machine Learning 2.3.1. Machine Learning and Types of Learning 2.3.2. Supervised-Learning: Classification 2.3.3. Unsupervised-Learning: Clustering 2.3.4. Semi-supervised Learning 2.3.5. Active-Learning 2.3.6. Incremental Learning 2.4. Acknowledgements 3. Related Work 3.1. Existing Traffic Regulation Recognition Approaches 3.2. Static Categorization 3.2.1. Map-based Category 3.2.2. Image-based Category 3.3. Dynamic Categorization 3.3.1. Episode-based Category 3.3.2. Speed-profile Category 3.3.3. Movement-summarization Category 3.4. Hybrid-based Categorization 3.5. Discussion 3.6. Knowledge Gap 3.7. Acknowledgements 4. Traffic Regulation Recognition (TRR) from GPS Data 4.1. Introduction 4.2. Datasets 4.2.1. Dataset Requirements and Limitations 4.2.2. Datasets for Testing the Proposed Methods 4.2.3. Groundtruth Map Construction 4.3. Methodology 4.3.1. Detection of Stop and Deceleration Episodes 4.3.2. The Static Approach 4.3.3. The c-Dynamic Approach 4.3.4. The Dynamic Approach 4.3.5. The Hybrid Approach 4.3.6. Implementation and Classification Settings 4.4. Results 4.5. Discussion 4.6. Summary 4.7. Acknowledgements 5. TRR From GPS Data: One-Arm versus All-Arm Models 5.1. Introduction 5.2. Methodology 5.2.1. One-Arm vs. All-Arm Models 5.2.2. The Effect of Sampling Rate 5.2.3. Reduced Models 5.2.4. The Effect of Turning/No-Turning Trajectories 5.2.5. The Effect of Number of Trajectories 5.2.6. Application of Domain Knowledge Rules 5.2.7. Classification Settings 5.3. Results 5.3.1. One-arm vs. All-arm Models 5.3.2. Testing the Effect of Sampling Rate 5.3.3. Reduced Models 5.3.4. Testing the Effect of Turning Trajectories and Examining an Optimal Number of Trajectories 5.3.5. Testing the Effect of the Number of Trajectories on Classification Performance 5.3.6. Misclassification Analysis 5.3.7. Applying Domain Knowledge Rules 5.4. Discussion 5.5. Summary 5.6. Acknowledgements 6. TRR with Sparsely Labeled and Stream Data 6.1. Introduction 6.2. TRR with Clustering 6.3. TRR with Self-Training, Active Learning and Cluster-then-Label 6.3.1. TRR with Self-Training: Using Labeled and Unlabeled Data 6.3.2. TRR with Active Learning 6.3.3. TRR with the Cluster-then-Label Algorithm 6.3.4. Comparison of All Tested Methods 6.4. Learning Transferability: Training on City A and Predicting on City B 6.5. Incremental (Online) Learning 6.6. Summary 7. Conclusions and Outlook 7.1. Research Questions Addressed in this Thesis 7.2. Outlook List of Acronyms Index A. Appendix List of Figures List of Tables Bibliography Curriculum Vitae Acknowledgements , Sprache der Kurzfassungen: Englisch, Deutsch
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  • 2
    Publication Date: 2020-07-26
    Description: This paper explores the idea of enriching maps with features predicted from GPS trajectories. More specifically, it proposes a method of classifying street intersections according to traffic regulators (traffic light, yield/priority-sign and right-of-way rule). Intersections are regulated locations and the observable movement of vehicles is affected by the underlying traffic rules. Movement patterns such as stop events or start-and-stop sequences are commonly observed at those locations due to traffic regulations. In this work, we test the idea of detecting traffic regulators by learning them in a supervised way from features derived from GPS trajectories. We explore and assess different settings of the feature vector being used to train a classifier that categorizes the intersections based on traffic regulators; also, we test several experimental setups. The results show that a Random Forest classifier with oversampling and Bagging booster enabled can predict the intersection regulators with 90.4% accuracy. We discuss future research directions and recommend next steps for improving the results of this research.
    Print ISSN: 2524-4957
    Electronic ISSN: 2524-4965
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by Springer
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  • 3
    Publication Date: 2020-10-30
    Description: Accurate information of traffic regulators at junctions is important for navigating and driving in cities. However, such information is often missing, incomplete or not up-to-date in digital maps due to the high cost, e.g., time and money, for data acquisition and updating. In this study we propose a crowdsourced method that harnesses the light-weight GPS tracks from commuting vehicles as Volunteered Geographic Information (VGI) for traffic regulator detection. We explore the novel idea of detecting traffic regulators by learning the movement patterns of vehicles at regulated locations. Vehicles’ movement behavior was encoded in the form of speed-profiles, where both speed values and their sequential order during movement development were used as features in a three-class classification problem for the most common traffic regulators: traffic-lights, priority-signs and uncontrolled junctions. The method provides an average weighting function and a majority voting scheme to tolerate the errors in the VGI data. The sequence-to-sequence framework requires no extra overhead for data processing, which makes the method applicable for real-world traffic regulator detection tasks. The results showed that the deep-learning classifier Conditional Variational Autoencoder can predict regulators with 90% accuracy, outperforming a random forest classifier (88% accuracy) that uses the summarized statistics of movement as features. In our future work images and augmentation techniques can be leveraged to generalize the method’s ability for classifying a greater variety of traffic regulator classes.
    Electronic ISSN: 2220-9964
    Topics: Architecture, Civil Engineering, Surveying , Geosciences
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  • 4
    Publication Date: 2019-10-31
    Description: Mapping with surveying equipment is a time-consuming and cost-intensive procedure that makes the frequent map updating unaffordable. In the last few years, much research has focused on eliminating such problems by counting on crowdsourced data, such as GPS traces. An important source of information in maps, especially under the consideration of forthcoming self-driving vehicles, is the traffic regulators. This information is largely lacking in maps like OpenstreetMap (OSM) and this article is motivated by this fact. The topic of this systematic literature review (SLR) is the detection and recognition of traffic regulators such as traffic lights (signals), stop-, yield-, priority-signs, right of way priority rules and turning restrictions at intersections, by leveraging non imagery crowdsourced data. More particularly, the aim of this study is (1) to identify the range of detected and recognised regulatory types by crowdsensing means, (2) to indicate the different classification techniques that can be used for these two tasks, (3) to assess the performance of different methods, as well as (4) to identify important aspects of the applicability of these methods. The two largest databases of peer-reviewed literature were used to locate relevant research studies and after different screening steps eleven articles were selected for review. Two major findings were concluded—(a) most regulator types can be identified with over 80% accuracy, even using heuristic-driven approaches and (b) under the current progress on the field, no study can be reproduced for comparative purposes nor can solely rely on open data sources due to lack of publicly available datasets and ground truth maps. Future research directions are highlighted as possible extensions of the reviewed studies.
    Electronic ISSN: 2220-9964
    Topics: Architecture, Civil Engineering, Surveying , Geosciences
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  • 5
    Publication Date: 2020-12-01
    Print ISSN: 1009-5020
    Electronic ISSN: 1993-5153
    Topics: Geosciences , Computer Science
    Published by Taylor & Francis
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