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@PHDTHESIS{ElAmouri:1021975,
author = {El Amouri, Amira},
othercontributors = {Herty, Michael and Göttlich, Simone},
title = {{D}river interaction: mathematical modeling and numerical
methods},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2025-09790},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2026; Dissertation, RWTH Aachen University, 2025},
abstract = {The automotive industry is witnessing significant
advancements in autonomous and assisted driving functions.
However, a major challenge remains in ensuring these systems
are accepted by drivers and effectively integrated into
their driving routines. Hence, the necessity of a
comprehensive framework becomes crucial to analyze and
enhance the interaction between drivers and assisted driving
functions. In this thesis, we design and implement a driver
interaction based framework for shared lateral driving. The
research is divided into three main objectives: developing a
methodology to capture the dynamics of shared control,
constructing a driver interaction classifier, and modeling
concepts for individualized driver support. We introduce a
novel driver-steering interaction model to analyze the
interplay between driver and assistance system torques. The
model allows a comprehensive analysis of the driver torque,
the assistance torque, and their combined effect on the
vehicle's steering behavior. To accurately represent these
interactions, the driver-steering interaction model requires
the formulation of a quadratic program. We apply the Varying
Coefficient (VC) method to effectively formulate this QP. We
develop a driver interaction classifier based on a designed
real-driving experimental framework. We identify and suggest
suitable classification features: conflict and passivity,
path consistency, adaptation and individual path pattern
features. We categorize the identified driver interaction
strategies into five distinct classes: adaptation,
persistence, selective persistence, nonintervention and
uncertainty. The classification is validated through a
comparison with subjective expert assessments. Additionally,
we apply Dynamic Mode Decomposition (DMD) to analyze the
underlying dynamics for each class. We present concepts for
adapting the system behavior in real-time to account for the
driver interaction strategies. We design a Model Predictive
Control (MPC) system and conduct a performance analysis to
ensure it aligns with desired system behavior for each
class. We apply online active-set methods and interior point
methods to solve the optimization problems in real-time. The
framework is implemented with a focus on real-world
applicability. Additionally, we suggest further concepts for
adapting the system behavior based on independent components
of the framework.},
cin = {114610 / 110000},
ddc = {510},
cid = {$I:(DE-82)114620_20140620$ / $I:(DE-82)110000_20140620$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2025-09790},
url = {https://publications.rwth-aachen.de/record/1021975},
}
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