This paper introduces COR-MCTS (Conservation of Resources – Monte Carlo Tree
Search), a novel tactical decision-making approach for automated driving
focusing on maneuver planning over extended horizons. Traditional
decision-making algorithms are often constrained by fixed planning horizons,
typically up to 6 seconds for classical approaches and 3 seconds for
learning-based methods limiting their adaptability in particular dynamic
driving scenarios. However, planning must be done well in advance in
environments such as highways, roundabouts, and exits to ensure safe and
efficient maneuvers. To address this challenge, we propose a hybrid method
integrating Monte Carlo Tree Search (MCTS) with our prior utility-based
framework, COR-MP (Conservation of Resources Model for Maneuver Planning). This
combination enables long-term, real-time decision-making, significantly
enhancing the ability to plan a sequence of maneuvers over extended horizons.
Through simulations across diverse driving scenarios, we demonstrate that
COR-MCTS effectively improves planning robustness and decision efficiency over
extended horizons.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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