Adaptive Path Planning for Skill-Based Personalization in Parking Maneuvers

This work introduces the idea of skill-based personalization for Advanced Driver Assistance Systems, aiming to address the limitations of traditional imitation-based personalization methods. Therefore, an innovative, adaptive path planning approach is developed as a crucial intermediate step in realizing this concept. This approach is exemplified through automated parking. For this purpose, the Hybrid <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{A}^{*}$</tex> and the Elastic-Band methods were integrated and modified to accommodate flexible target positions and incorporate additional Key Performance Indicators (KPIs) relevant for personalized parking algorithms in their cost functions. Additionally, two novel shortcut algorithms are proposed to address some of the limitations in adjusting these KPIs. As a result, a path planner is developed that is capable of producing customized paths aligned with the user's specific skills and preferences.