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%0 Thesis %A El Amouri, Amira %T Driver interaction: mathematical modeling and numerical methods %I RWTH Aachen University %V Dissertation %C Aachen %M RWTH-2025-09790 %P 1 Online-Ressource : Illustrationen %D 2025 %Z Veröffentlicht auf dem Publikationsserver der RWTH Aachen University 2026 %Z Dissertation, RWTH Aachen University, 2025 %X 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. %F PUB:(DE-HGF)11 %9 Dissertation / PhD Thesis %R 10.18154/RWTH-2025-09790 %U https://publications.rwth-aachen.de/record/1021975