MSc. Thesis: Model validation for autonomous trucks
While parts of the modelling of the dynamics of road vehicles follows from first principles, a major part (e.g. the behavior of the tires) are often based on empirical evidence. On top of this, there are a number of physical phenomena that are not captured by the standard models, many model parameters are uncertain, measurements are inexact and non-modeled disturbances are often present. For this reason, the models are always associated with some degree of uncertainty. The process of quantifying such uncertainty is often referred to as model validation.
Data-driven model validation is an important topic in the field of system identification. Most methods form some kind of statistics around the model residuals (i.e. the part of the data that could not be explained by the model) in order to infer the quality of the model. The aim of this thesis is to perform an extreme value analysis on the model residuals to predict the likelihood of seeing extreme model errors.
- Familiarizing with dynamic models of articulated vehicles
- Familiarizing with existing software for model validation
- Implement necessary methods to perform an extreme value analysis
- Perform an extreme value analysis on the model residuals
- Documentation of results
Master student in engineering mathematics, engineering physics, automation and mechatronics, automotive engineering, control theory or similar.
Good programming skills (MATLAB and/or Python).
Nice to have skills
Knowledge of Extreme value theory, System identification, Vehicle dynamics modelling, Control & systems theory, Numerical methods for optimization, Numerical methods for ODEs.
Starting date: January 2020
Number of students: 1-2
Prof. Holger Rootzén, Mathematical Sciences, Chalmers University of Technology
Emil Klintberg, Vehicle Automation, Volvo Group
Robert Hult, Vehicle Automation, Volvo Group, +46 313229292
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