MARC details
| 000 -LEADER |
|---|
| fixed length control field |
08375nam a22002657a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
201210s2024 a|||f bm|| 00| 0 eng d |
| 024 7# - Author Identifier |
|---|
| Standard number or code |
0000-0003-3128-9559 |
| Source of number or code |
ORCID |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
EG-CaNU |
| Transcribing agency |
EG-CaNU |
| 041 0# - Language Code |
|---|
| Language code of text |
eng |
| Language code of abstract |
eng |
| -- |
ara |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
610 |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ahmed Maher Mohamed Hamed |
| 245 1# - TITLE STATEMENT |
|---|
| Title |
Smart Connectivity for Teleoperated Vehicles Using a Predictive LTE Multi-Operator Approach |
| Statement of responsibility, etc. |
/Ahmed Maher Mohamed Hamed |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Date of publication, distribution, etc. |
2024 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
64p. |
| Other physical details |
ill. |
| Dimensions |
21 cm. |
| 500 ## - GENERAL NOTE |
|---|
| Materials specified |
Supervisor:<br/>Nashwa AbdelBaki |
| 502 ## - Dissertation Note |
|---|
| Dissertation type |
Thesis (M.A.)—Nile University, Egypt, 2024 . |
| 504 ## - Bibliography |
|---|
| Bibliography |
"Includes bibliographical references" |
| 505 0# - Contents |
|---|
| Formatted contents note |
Contents:<br/>DEDICATION................................................................................................................i<br/>ACKNOWLEDGEMENTS...........................................................................................ii<br/>TABLE OF CONTENTS..............................................................................................iv<br/>LIST OF FIGURES ......................................................................................................vi<br/>LIST OF TABLES.......................................................................................................vii<br/>ACRONYMS..............................................................................................................viii<br/>PUBLICATIONS..........................................................................................................xi<br/>ABSTRACT................................................................................................................xiii<br/>Chapter 1 Introduction ...................................................................................................1<br/>1.1 Connectivity-Based Services in Business............................................................1<br/>1.2 Teleoperated Services..........................................................................................2<br/>1.3 Automotive Industry Assistive Driving ...............................................................4<br/>1.4 Connectivity Service............................................................................................5<br/>1.5 Connectivity Demand Growth .............................................................................6<br/>1.6 The need behind Multi-Operator Connection ....................................................10<br/>1.7 Thesis Objectives...............................................................................................11<br/>1.8 Thesis Outline ....................................................................................................12<br/>Chapter 2 Related Teleoperating Idea & Prediction Model Data ................................13<br/>2.1 Teleoperative Supporting Devices.....................................................................13<br/>2.2 Teleoperating with Cellular ...............................................................................18<br/>2.3 Prediction Model and Correlation of the Environment Parameters...................21<br/>Chapter 3 Accurate Data Collection for Modeling ......................................................25<br/>3.1 Collecting Equipment ........................................................................................25<br/>3.2 Selected Test Area Criteria ................................................................................25<br/>3.3 Preforming Data Collection ...............................................................................27<br/>Chapter 4 Feature Engineering for Improved Predection ............................................36<br/>4.1 Data Cleansing & Labelling...............................................................................37<br/>4.1.1 Labelling Level 1: Basic Suitability .......................................................................... 37<br/>4.1.2 Labelling Level 2: Prioritization with Weighted Scores .......................................... 37<br/>4.1.3 Tie-Breaking Mechanism ........................................................................................ 38<br/>4.1.4 Addressing Unsuitable Data for Model Training .................................................... 39<br/>4.1.5 Data Features: Building the Model's Foundation................................................... 40<br/>4.2 Predictive Modelling..........................................................................................43<br/>4.2.1 Dividing the Data: Training and Testing Sets.......................................................... 45<br/>4.2.2 Addressing Data Imbalance: Ensuring a Fair Playing Field for Operators .............. 46<br/>4.2.3 Evaluating the Impact of Balanced Data: Confusion Matrix and Optimal Choice.. 48<br/>4.2.4 Best Model Results ................................................................................................. 51<br/>Chapter 5 Conclusion and Future Work ......................................................................55<br/>5.1 Conclusion .........................................................................................................55<br/>5.1.1 Building Upon Existing Work: Comparing Approaches to Throughput Prediction 56<br/>5.1.2 Model Selection and Verification ........................................................................... 56<br/>5.1.3 Success Without Private Data................................................................................. 57<br/>5.1.4 Moving Forward: Beyond Throughput ................................................................... 57<br/>5.1.5 Road Sample Distribution and Localized Impact.................................................... 58<br/>5.1.6 Impact on Model Performance............................................................................... 58<br/>5.2 Future Work .......................................................................................................59<br/>5.2.1 Enhancing Data Streams and Sensor Fusion .......................................................... 59<br/>5.2.2 Investigating More Complex Models: Beyond Simple ANNs.................................. 60<br/>5.2.3 Data Intensity Through Crowd-Sourced Collection................................................ 61 |
| 520 3# - Abstract |
|---|
| Abstract |
Abstract:<br/>The demand for reliable connectivity-driven services is on the rise, leading to <br/>increased sensitivity in technologies like Advanced Driver-Assistance Systems <br/>(ADAS). ADAS represents a prevalent technological advancement in modern <br/>vehicles, with the aspiration of achieving trustworthy autonomous driving as the <br/>ultimate goal. From both end-user and manufacturer perspectives, we are assessing <br/>Teleoperated Driving as a promising feature to address emerging needs for traffic <br/>management and health and safety precautions. Human-to-human interaction and <br/>perception have been found to be superior to human-to-machine interaction in <br/>handling tasks, such as human driving compared to machine driving.<br/>As this entire service relies on sensors which it is already implemented by various <br/>car manufacturers, and connectivity. It is obvious that the quality of the connectivity <br/>is expected to change in terms of coverage and capacity. We studied the feasibility <br/>of predicting the most suitable market operator within a specific area. This is based <br/>on previously established criteria, to serve as the primary data connection before <br/>engaging in a new measurement delay. To this end, an extensive measurement <br/>period was conducted. Establishing error margins in data collection and refining the <br/>data filtering process were meant to accurately capture the specifically targeted road <br/>conditions. These steps were completed prior to the commencement of our analysis <br/>of different models.<br/>Smart Connectivity for Teleoperated <br/>Vehicles Using a Predictive LTE <br/>Multi-Operator Approach<br/>Ahmed Maher Mohamed Hamed<br/>xiv<br/>These efforts have strengthened our study's focus on confirming the feasibility of <br/>seamless transitions between different operators, ensuring the fulfilment of the <br/>necessary teleoperation conditions. <br/>Keywords: Cellular networks, teleoperated vehicle, teleoperated driving, latency, <br/>throughput, 4G, LTE, predication models |
| 546 ## - Language Note |
|---|
| Language Note |
Text in English, abstracts in English and Arabic |
| 650 #4 - Subject |
|---|
| Subject |
informatics |
| 655 #7 - Index Term-Genre/Form |
|---|
| Source of term |
NULIB |
| focus term |
Dissertation, Academic |
| 690 ## - Subject |
|---|
| School |
informatics |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Thesis |
| 655 #7 - Index Term-Genre/Form |
|---|
| -- |
187 |