[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126684-en":3,"doc-seo-126684-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126684,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Developing and Testing Digital Twins for Vehicle Collision Prediction - A Machine Learning and Genetic Search Algorithm Approach - Master’s Thesis","This master’s thesis develops a digital twin capable of predicting and avoiding vehicle collisions using machine learning models trained on SVL Simulator data. A genetic search algorithm is introduced to generate specialized test data that resembles realistic collision scenarios, enabling comprehensive evaluation. The core contribution is assessing whether genetically generated test data can effectively measure the digital twin’s performance by analyzing correctly classified collisions. Results inform collision-prediction accuracy improvements for safer autonomous driving and intelligent transportation systems.","| \u003Cbr>THE FACULTY OF TECHNICAL AND NATURAL SCIENCES\u003Cbr>MASTER’S THESIS |  |\n| --- | --- |\n| Study programme / specialisation:\u003Cbr>Master’s in engineering /\u003Cbr>Data science | Spring semester 2023\u003Cbr>Open |\n| Author(s): Yohannes Dawit Kassaye and Sigurd Grøvdal Hansen |  |\n| Faculty supervisor: Ferhat Özgur Catak\u003Cbr>Supervisor(s): Hassan Sartaj and Shaukat Ali |  |\n| Thesis title: Developing and Testing Digital Twins for Vehicle Collision Prediction: A Machine Learning and Genetic Search Algorithm Approach |  |\n| Credits (ECTS): 60 (30 · 2) |  |\n| Keywords:\u003Cbr>Digital twins, machine learning, genetic algorithm, self-driving cars | Pages: 86\u003Cbr>+ appendix: 1 page\u003Cbr>Stavanger 15 . june 2023 |\n\nAbstract  \nThis thesis focuses on developing a digital twin which can predict and avoid collisions. The digital twin does this by using different machine learning models that are trained on data from the SVL Simulator. By harnessing the power of machine learning, the digital twin demonstrates promising abilities in collision prediction and prevention. Additionally, a genetic search algorithm is developed to generate specialized testing data, enabling comprehensive evaluation of the digital twin’s performance.  \nThe central contribution of this research lies in exploring the viability of utilizing test data that is generated by a genetic search algorithm to evaluate the performance of the digital twin. By employing the genetic search algorithm to generate data resembling real collision scenarios, classified as collisions, an interesting evaluation framework is established. Through the evaluation process, which involves analyzing the number of accurately classified collisions by the digital twin, insights are gained into the model’s effectiveness in predicting collisions.  \nThis contributes to the ongoing efforts in enhancing the accuracy of collision prediction systems, ultimately leading to improved safety measures in autonomous driving and intelligent transportation systems.  \nAcknowledgements  \nWe would like to thank Hassan Sartaj and Shaukat Ali for guiding us through the development and the writing of this thesis. They also gave us good advice on what to research and ideas.  \nContents  \nList of Figures vii  \nList of Tables x  \n1 Introduction 1  \n1.1 Motivation ............................ 1  \n1.2 Problem definition ....................... 2  \n1.3 Research questions ....................... 2  \n1.4 Outline ............................. 2  \n2 Background 4  \n2.1 Digital twins .......................... 4  \n2.2 Self-driving cars ......................... 6  \n2.3 SVL Simulator ......................... 7  \n2.3.1 WISE .......................... 9  \n2.3.2 OSSDC-Sim ....................... 10  \n2.3.3 Python API ....................... 11  \n2.4 Deep Scenario .......................... 16  \n2.4.1 Scenarios ........................ 16  \n2.4.2 Features ......................... 17  \n2.4.3 Deep scenario XML-files ................ 18  \n3 Solution Approach 20  \n3.1 Generated collision data .................... 20  \n3.2 Machine learning for collision prediction ........... 22  \n3.2.1 Introduction to machine learning ........... 22  \n3.2.2 Data preprocessing and feature engineering ..... 23  \n3.2.3 Machine learning models for collision prediction ... 24  \n3.2.4 Evaluation and performance metrics ......... 27  \n3.3 Genetic algorithm ........................ 30  \n3.3.1 Simple implementation example ............ 31  \n3.3.2 Variations of the algorithm .............. 32  \n4 Implementation 39  \n4.1 Generating the self generated data .............. 40  \n4.2 Feature engineering ....................... 40  \n4.2.1 Deep Scenario ...................... 41  \n4.2.2 Self-generated data ................... 45  \n4.3 Implementation of the features ................ 48  \n4.3.1 Distance to obstacle .................. 48  \n4.3.2 Time to collision .................... 53  \n4.3.3 Jerk ........................... 56  \n4.4 Implementation of the model ................. 56  \n4.4.1 P","cbCaifcJ0LbCCKx5","https://ap.wps.com/l/cbCaifcJ0LbCCKx5","pdf",5492892,1,99,"English","en",105,"# Introduction\n## Motivation\n## Problem definition\n## Research questions\n## Outline\n# Background\n## Digital twins\n## Self-driving cars\n## SVL Simulator\n## Deep Scenario\n# Solution Approach\n## Generated collision data\n## Machine learning for collision prediction\n## Genetic algorithm\n# Implementation\n## Generating the self generated data\n## Feature engineering\n## Implementation of the features\n## Implementation of the model\n## Using the simulator\n## Implementation of the genetic algorithm\n# Results and discussion\n## Digital twin\n## Genetic algorithm\n## Answering the research questions\n# Conclusion\n# Bibliography\n## A Source Code","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To develop and test a digital twin that can predict and avoid vehicle collisions using machine learning.\"},{\"question\":\"How is the digital twin trained and evaluated?\",\"answer\":\"Machine learning models are trained on data from the SVL Simulator, and evaluation is performed using test data generated via a genetic search algorithm.\"},{\"question\":\"What role does the genetic search algorithm play?\",\"answer\":\"It generates specialized testing data that resembles real collision scenarios, allowing an evaluation framework based on the number of collisions correctly classified by the digital twin.\"}]","Developing and Testing Digital Twins for Vehicle Collision Prediction - A Machine Learning and Genetic Search Algorithm Approach - Master’s Thesis | PDF",1785934214,249,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"developing-and-testing-digital-twins-for-vehicle-collision-prediction-a-machine-learning-and-genetic-search-algorithm-approach-masters-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/developing-and-testing-digital-twins-for-vehicle-collision-prediction-a-machine-learning-and-genetic-search-algorithm-approach-masters-thesis/126684/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the thesis?","Question",{"text":75,"@type":76},"To develop and test a digital twin that can predict and avoid vehicle collisions using machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the digital twin trained and evaluated?",{"text":80,"@type":76},"Machine learning models are trained on data from the SVL Simulator, and evaluation is performed using test data generated via a genetic search algorithm.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does the genetic search algorithm play?",{"text":84,"@type":76},"It generates specialized testing data that resembles real collision scenarios, allowing an evaluation framework based on the number of collisions correctly classified by the digital twin.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]