Developing and Testing Digital Twins for Vehicle Collision Prediction: A Machine Learning and Genetic Search Algorithm Approach

This 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. The 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. This 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

Garmin applikasjon for analyse av helsedata

Denne bacheloroppgaven går ut på lage en applikasjon som skal brukes på en Garmin-smartklokke. Applikasjonen bruker de to maskinlæringsmetodene lineær regresjon og bestemmelsestre til å komme fram til om en person har aterosklerose eller ikke. I oppgaven blir det også gått mer inn på andre metoder som kunne blitt brukt, samt Garmin, aterosklerose og ikke minst hvordan denne applikasjonen har blitt og hvorfor den er blitt slik. Det skal også være lett for andre å bruke, endre og å laste ned programvaren på sin egen klokke.This bachelor thesis is about creating an application to be used on a Garmin smartwatch. The application uses two machine learning methods, linear regression and decision trees, to determine whether a person has atherosclerosis or not. The thesis also goes into more detail on other methods that could've been used, as well as Garmin, atherosclerosis, the development process and justifications for our decisions. It should also be easy for others to use, modify and download the software on their own watch