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Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme

Orzechowski, Piotr F.; Burger, Christoph; Lauer, Martin

Abstract:
Behavior planning and decision-making are some of the biggest challenges for highly automated systems. A fully automated vehicle (AV) is confronted with numerous tactical and strategical choices. Most state-of-the-art AV platforms implement tactical and strategical behavior generation using finite state machines. However, these usually result in poor explainability, maintainability and scalability. Research in robotics has raised many architectures to mitigate these problems, most interestingly behavior-based systems and hybrid derivatives. Inspired by these approaches, we propose a hierarchical behavior-based architecture for tactical and strategical behavior generation in automated driving. It is a generalizing and scalable decision-making framework, utilizing modular behavior blocks to compose more complex behaviors in a bottom-up approach. The system is capable of combining a variety of scenario- and methodology-specific solutions, like POMDPs, RRT* or learning-based behavior, into one understandable and traceable architecture. We extend the hierarchical behavior-based arbitration concept to address scenarios where multiple behavior options are applicable but have no clear priority against each other. ... mehr



Zugehörige Institution(en) am KIT Institut für Mess- und Regelungstechnik mit Maschinenlaboratorium (MRT)
Publikationstyp Forschungsbericht/Preprint
Publikationsjahr 2021
Sprache Englisch
Identifikator KITopen-ID: 1000130138
Nachgewiesen in arXiv
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