Dario Rezaei Riabi
Development of an algorithm for the automatic evaluation of Adaptive Cruise Control performance based on subjective and objective data fusion.
Rel. Andrea Tonoli, Stefano Virginio. Politecnico di Torino, Master of science program in Mechatronic Engineering, 2022
Abstract
The purpose of this thesis is the definition of the Key Performance Indicators (KPIs) related to the Adaptive Cruise Control feature. In particular, the scope of this research is to analyse vehicles of different car manufacturers in order to determine which are the performance indicators that are most influential in the subjective evaluations given by different drivers about the Adaptive Cruise Control, and to develop an algorithm which is able to automatically identify the indicators values and evaluate the system during the development phase. The starting point of this work was the analysis of physical and electrical quantities obtained directly through the acquisition of vehicles' internal signals or through the use of an Inertial Measurement Unit (IMU) during test activities performed by qualified drivers for the compilation of the Quality Profile, a document used by the drivers to evaluate the Adaptive Cruise Control feature through various entries.
Combining these subjective evaluations with the objective measurements, it was possible to define ranges of values for the KPIs considered as ideal targets for Stellantis vehicles
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