Applying effective object classification methods for radar based advanced driver assistance systems

OData support
Supervisor:
Dr. Harmati István
Department of Control Engineering and Information Technology

In my thesis I examine radar based object classifiers which are capable of

identifying the participants of common road traffic. Firstly I will present the design of the

classifiers used and I will describe the methodology of designing and tuning the

algorithms, which are necessary to use the classifiers in modern driver assistance systems.

I then introduce the object classes to be determined by the classifiers. Following

this, I discuss the aspects of collecting and preprocessing the input data which have a

strong impact on the classification performance.

To solve the classification problem, I used the Python programming language and

frameworks which support the implementation of modern classifiers (Keras, LibSVM) to

realize the algorithms. While chosing the right parameters, my focus was on the

examination of the effects of the parameters without completeness of performance

optimization.

Different methods to evaluate a classifier are then introduced. Based on the most relevant

aspects from my point of view, an evaluation and comparison of the realized classifiers

is presented. At the end of my thesis I suggest further improvements which may increase

the effectiveness and usability of the classifiers in driver assistance systems.

Downloads

Please sign in to download the files of this thesis.