A Digital Twin-Assisted Hybrid Decision-Making Model for Autonomous Vehicles Behavior Analysis

Abstract

This thesis aims to center its attention on the development of a prototype of a Digital Twin that can be utilized for the analysis of autonomous vehicle behavior. The objective of the project was to explore the potential connection and configuration of Digital Twin and Autonomous Vehicles in order to create a visualization-based model for analyzing vehicle behavior. Two methods were employed to establish the connection between autonomous vehicles and a Digital Twin. The first method involved simulating a instinctive model using historical data to recreate scenarios. The second method involved simulating a computational model to incorporate a texture from the physical environment into the DT. In order to initiate the configuration of the DT, Carla was employed to generate states representing the ahead going speed and acceleration of a vehicle, thereby allowing for testing of the OpenModelica models. Additionally, efforts were made to increase the statistics by utilizing Carla as a realistic tool to integrate Autonomous Vehicles with Carla. This thesis introduces a configuration that introduces the concept of the DT being one step ahead of the PT. The PT, which is based on machine learning, was introduced as a means of generating similar parameters to those of a vehicle in a physical environment, thereby allowing for testing of this setting. However, the research did not inspect methods for integrating the same 3D geography in Carla and the machine learning-based simulator, which limited the experiments conducted while the machine learning simulators served as the PT. Furthermore, the statistics between Carla and OM was unable to investigate the concept of reflecting x back action to the PT. Consequently, a second design diagram, based on the knowledge acquired in this project, is presented in the thesis, with the potential for further investigation. In conclusion, this thesis presents a Digital Twin-based methodologies utilized throughout the project establish a strong foundation for future research in the development of a Digital Twin-based model for autonomous vehicles