Validation of Tractor-semitrailer Vehicle Model based on Bayesian Hypothesis Testing
Typ
Examensarbete för masterexamen
Program
Automotive engineering (MPAUT), MSc
Publicerad
2021
Författare
Dineff, Athanasia Maria
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The development of automated vehicles is the future of the automotive industry, and
it has seen a rapid advance over the past decade. The design of automated vehicles
relies on vehicle models, which predict their responses and control their behavior.
This thesis presents a vehicle model validation framework to evaluate the validity of
a simple (abstract) vehicle model against a complex (implementation) model. The
aim is to quantify the validity of the abstract model and determine its suitability for
an application. The validation procedure uses Bayesian hypothesis testing. First,
two hypotheses are stated; the null hypothesis, which supports the abstract model’s
validity, and the alternative, which rejects the model. Two approaches to Bayesian
hypothesis testing are then studied. The first approach estimates the posterior
distributions of the parameters of interest and evaluates whether the credible interval
includes the null hypothesis. The second approach relies on Bayes factors, which
compare the two hypotheses and indicate the most probable one.
The methods presented are applied on a tractor-semitrailer combination under
the driving context of city driving. The validation framework is evaluated for three
cases of the abstract model. The first case refers to the calibrated model. The
second and third cases concern an incorrectly tuned parameter, with double and
half the calibrated value, respectively. The outcome is that the abstract model is
deemed valid when calibrated, whereas it is invalid when a parameter is incorrectly
tuned. The thesis also discusses the comparison between the two proposed validation
approaches.
Beskrivning
Ämne/nyckelord
Bayesian hypothesis testing , Model validation , Vehicle modelling , Tractor-semitrailer , Bayes factor , Model comparison , Posterior summary