Edoardo Serri
ADAS Comfort-Critical Scenarios Extraction.
Rel. Stefano Alberto Malan. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2021
Abstract
The growing spread of assisted and autonomous systems in passenger vehicles has recently drawn attention to the comfort of such systems for end users. Studying comfort raises nontrivial challenges, as humans can recognize and realize in their own driving behavior what it is comfortable and what is not, based on their own subjective perception, which is dependent on numerous factors such as perceived risk, vibrations, predictability and motion sickness. Therefore, an Advanced Driver Assistance System (ADAS) can realize a comfortable driving, for its own passengers, if it has a human-like driving style. The objective is then to understand and replicate human driving in assisted and autonomous driving systems.
To do so, it is needed to relate the subjectivity of the comfort to objective metrics that can be incorporated in a control system design Such metrics, defining human driving, can be extracted from datasets of naturalistic driving, which are, by nature, huge and unstructured
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