TY - THES A1 - Rana, Tushar T1 - Sparse discovery of an empirical vehicle dynamics model for model predictive trajectory optimization N2 - Fully autonomous driving to prevent road accidents remains a challenge. Further work, such as route planning upon detection of obstacles and actuation of the actuators to keep the vehicle on the planned path, is often required before the collision avoidance systems are ready to roll out. There are different control strategies that are used and have been proposed for addressing this challenge. From these strategies, the Model Predictive Control has gained quite a lot of interest in recent years. The main idea of MPC is to predict the future behavior of the controlled system on a finite time horizon and to calculate the optimal control input Signal. Finding the correct predictive model of the system is a crucial part of MPC. This thesis proposes a framework based on the SINDy method to develop an empirical vehicle dynamic model that can address the accuracy and computational complexity problem of the MPC dynamic models. The first part of the work is focused on data collection using IPG CarMaker and the data preprocessing steps. The empirical model is primarily based on the data representing the system dynamics under consideration. Any uncertainty in the data leads to the degradation of the obtained model. The second part of the work is devoted to formulating the regression problem itself. There are many aspects to problem configuration that one needs to adapt to for specific system requirements. This section defines selecting the correct model structure and candidate function library. The third part of this work is focused on the model selection process that includes finding the best trade-off between the required accuracy and model complexity. The comparison of the obtained model with the already available physics-based model is also evaluated in this part. This evaluation found that the empirical model performs better during highly nonlinear maneuvers compared to the already available physics-based model. KW - Studiengang International Automotive Engineering Y1 - 2022 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-33937 CY - Ingolstadt ER -